diff --git a/Makefile b/Makefile index a5d3a9c..ae01871 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ VERSION := $(shell grep . VERSION.txt | cut -f1 -d:) -PROGRAM_NAME := project +PROGRAM_NAME := prostate CC := g++ # CC := g++-mp-7 # typical macports compiler name @@ -11,16 +11,19 @@ ifdef PHYSICELL_CPP CC := $(PHYSICELL_CPP) endif +# prostate model has more than 64 nodes, so we have +# to set up this var to the number of nodes +MABOSS_MAX_NODES = 256 + ### MaBoSS configuration # MaBoSS max nodes ifndef MABOSS_MAX_NODES -MABOSS_MAX_NODES = 256 +MABOSS_MAX_NODES = 64 endif # MaBoSS directory MABOSS_DIR = addons/PhysiBoSS/MaBoSS/engine CUR_DIR = $(shell pwd) -CUSTOM_DIR = sample_projects/Arnau_model/custom_modules ifneq ($(OS), Windows_NT) LDL_FLAG = -ldl @@ -58,7 +61,9 @@ ARCH := native # best auto-tuning # ARCH := nocona #64-bit pentium 4 or later # CFLAGS := -march=$(ARCH) -Ofast -s -fomit-frame-pointer -mfpmath=both -fopenmp -m64 -std=c++11 -CFLAGS := -march=$(ARCH) -O3 -fomit-frame-pointer -mfpmath=both -fopenmp -m64 -std=c++11 +CFLAGS := -g -march=$(ARCH) -O3 -fomit-frame-pointer -mfpmath=both -fopenmp -m64 -std=c++11 +# debug: +# CFLAGS := -march=$(ARCH) -O0 -ggdb -fomit-frame-pointer -mfpmath=both -fopenmp -m64 -std=c++11 ifeq ($(OS),Windows_NT) else @@ -91,18 +96,21 @@ MaBoSS := ./addons/PhysiBoSS/MaBoSS/engine/src/BooleanNetwork.h PhysiBoSS_OBJECTS := maboss_network.o maboss_intracellular.o -PhysiCell_custom_module_OBJECTS := custom.o +PhysiCell_custom_module_OBJECTS := custom.o drug_sensitivity.o boolean_model_interface.o pugixml_OBJECTS := pugixml.o PhysiCell_OBJECTS := $(BioFVM_OBJECTS) $(pugixml_OBJECTS) $(PhysiCell_core_OBJECTS) $(PhysiCell_module_OBJECTS) -ALL_OBJECTS := $(PhysiCell_OBJECTS) $(PhysiCell_custom_module_OBJECTS) $(PhysiBoSS_OBJECTS) +ALL_OBJECTS := $(PhysiCell_OBJECTS) $(PhysiCell_custom_module_OBJECTS) $(PhysiBoSS_OBJECTS) +#$(PhysiBoSS_module_OBJECTS) # compile the project all: main.cpp $(ALL_OBJECTS) $(MaBoSS) $(COMPILE_COMMAND) $(INC) -o $(PROGRAM_NAME) $(ALL_OBJECTS) main.cpp $(LIB) - make name + @echo "" + @echo "check for $(PROGRAM_NAME)" + make name static: main.cpp $(ALL_OBJECTS) $(MaBoSS) $(LINK_COMMAND) $(INC) -o $(PROGRAM_NAME) $(ALL_OBJECTS) main.cpp $(LIB) -static-libgcc -static-libstdc++ $(STATIC_OPENMP) @@ -134,16 +142,16 @@ PhysiCell_utilities.o: ./core/PhysiCell_utilities.cpp PhysiCell_custom.o: ./core/PhysiCell_custom.cpp $(COMPILE_COMMAND) -c ./core/PhysiCell_custom.cpp - + PhysiCell_constants.o: ./core/PhysiCell_constants.cpp - $(COMPILE_COMMAND) -c ./core/PhysiCell_constants.cpp - + $(COMPILE_COMMAND) -c ./core/PhysiCell_constants.cpp + PhysiCell_signal_behavior.o: ./core/PhysiCell_signal_behavior.cpp $(COMPILE_COMMAND) -c ./core/PhysiCell_signal_behavior.cpp - + PhysiCell_rules.o: ./core/PhysiCell_rules.cpp $(COMPILE_COMMAND) -c ./core/PhysiCell_rules.cpp - + # BioFVM core components (needed by PhysiCell) BioFVM_vector.o: ./BioFVM/BioFVM_vector.cpp @@ -189,19 +197,19 @@ PhysiCell_MultiCellDS.o: ./modules/PhysiCell_MultiCellDS.cpp $(MaBoSS) PhysiCell_various_outputs.o: ./modules/PhysiCell_various_outputs.cpp $(COMPILE_COMMAND) -c ./modules/PhysiCell_various_outputs.cpp - + PhysiCell_pugixml.o: ./modules/PhysiCell_pugixml.cpp $(COMPILE_COMMAND) -c ./modules/PhysiCell_pugixml.cpp PhysiCell_settings.o: ./modules/PhysiCell_settings.cpp - $(COMPILE_COMMAND) -c ./modules/PhysiCell_settings.cpp - + $(COMPILE_COMMAND) -c ./modules/PhysiCell_settings.cpp + PhysiCell_basic_signaling.o: ./core/PhysiCell_basic_signaling.cpp - $(COMPILE_COMMAND) -c ./core/PhysiCell_basic_signaling.cpp + $(COMPILE_COMMAND) -c ./core/PhysiCell_basic_signaling.cpp PhysiCell_geometry.o: ./modules/PhysiCell_geometry.cpp $(COMPILE_COMMAND) -c ./modules/PhysiCell_geometry.cpp - + # user-defined PhysiCell modules Compile_MaBoSS: ./addons/PhysiBoSS/MaBoSS/engine/src/BooleanNetwork.h @@ -214,14 +222,24 @@ else python3 addons/PhysiBoSS/setup_libmaboss.py endif +maboss_intracellular.o: ./addons/PhysiBoSS/src/maboss_intracellular.cpp $(MaBoSS) + $(COMPILE_COMMAND) $(INC) -c ./addons/PhysiBoSS/src/maboss_intracellular.cpp + maboss_network.o: ./addons/PhysiBoSS/src/maboss_network.cpp $(MaBoSS) $(COMPILE_COMMAND) $(INC) -c ./addons/PhysiBoSS/src/maboss_network.cpp -maboss_intracellular.o: ./addons/PhysiBoSS/src/maboss_intracellular.cpp $(MaBoSS) - $(COMPILE_COMMAND) $(INC) -c ./addons/PhysiBoSS/src/maboss_intracellular.cpp +utils.o: ./addons/PhysiBoSS/src/utils.cpp #$(MaBoSS) + $(COMPILE_COMMAND) $(INC) -c ./addons/PhysiBoSS/src/utils.cpp custom.o: ./custom_modules/custom.cpp $(MaBoSS) - $(COMPILE_COMMAND) $(INC) -c ./custom_modules/custom.cpp + $(COMPILE_COMMAND) $(INC) -c ./custom_modules/custom.cpp + +drug_sensitivity.o: ./custom_modules/drug_sensitivity.cpp + $(COMPILE_COMMAND) $(INC) -c ./custom_modules/drug_sensitivity.cpp + +boolean_model_interface.o: ./custom_modules/boolean_model_interface.cpp + $(COMPILE_COMMAND) $(INC) -c ./custom_modules/boolean_model_interface.cpp + # cleanup @@ -231,29 +249,31 @@ reset: rm -f ./custom_modules/* touch ./custom_modules/empty.txt touch ALL_CITATIONS.txt - touch ./core/PhysiCell_cell.cpp rm ALL_CITATIONS.txt cp ./config/PhysiCell_settings-backup.xml ./config/PhysiCell_settings.xml - rm -fr ./config/cells.csv ./config/cell_rules.csv + rm -rf ./config/boolean_network/ rm -rf ./scripts MaBoSS-clean: rm -fr addons/PhysiBoSS/MaBoSS -clean: +clean: rm -f *.o rm -f $(PROGRAM_NAME)* data-cleanup: + rm -f *.mat + rm -f *.xml + rm -f *.svg rm -rf ./output mkdir ./output touch ./output/empty.txt # archival - + checkpoint: zip -r $$(date +%b_%d_%Y_%H%M).zip Makefile *.cpp *.h config/*.xml custom_modules/* - + zip: zip -r latest.zip Makefile* *.cpp *.h BioFVM/* config/* core/* custom_modules/* matlab/* modules/* sample_projects/* cp latest.zip $$(date +%b_%d_%Y_%H%M).zip @@ -273,26 +293,10 @@ unzip: untar: cp ./archives/latest.tar . tar -xzf latest.tar - -# easier animation - -FRAMERATE := 24 -OUTPUT := output - -jpeg: - @magick identify -format "%h" $(OUTPUT)/initial.svg > __H.txt - @magick identify -format "%w" $(OUTPUT)/initial.svg > __W.txt - @expr 2 \* \( $$(grep . __H.txt) / 2 \) > __H1.txt - @expr 2 \* \( $$(grep . __W.txt) / 2 \) > __W1.txt - @echo "$$(grep . __W1.txt)!x$$(grep . __H1.txt)!" > __resize.txt - @magick mogrify -format jpg -resize $$(grep . __resize.txt) $(OUTPUT)/s*.svg - rm -f __H*.txt __W*.txt __resize.txt -gif: - magick convert $(OUTPUT)/s*.svg $(OUTPUT)/out.gif - movie: - ffmpeg -r $(FRAMERATE) -f image2 -i $(OUTPUT)/snapshot%08d.jpg -vcodec libx264 -pix_fmt yuv420p -strict -2 -tune animation -crf 15 -acodec none $(OUTPUT)/out.mp4 + ffmpeg -r 25 -i output/snapshot%08d.svg -pix_fmt yuv420p output.mp4 + vlc output.mp4 # upgrade rules @@ -322,7 +326,7 @@ upgrade: $(SOURCE) mv -f PhysiCell/documentation/User_Guide.pdf documentation rm -f -r PhysiCell rm -f $(SOURCE) - + # use: make save PROJ=your_project_name PROJ := my_project @@ -367,3 +371,4 @@ unpack: list-user-projects: @echo "user projects::" @cd ./user_projects && ls -dt1 * | grep . | sed 's!empty.txt!!' + diff --git a/README.md b/README.md index 6ebe806..b404954 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,21 @@ # About -This repository is a template for PhysiBoSS models, that can be used to submit a new model to the database. +This repository is an update of the prostate model to version 2.2.3 of PhysiBoSS. The project does not rely on the cell rules to perform the inhibition, but on an ad-hoc cell sensitivity that depends on the cell line. + +A prior version of this model was used to simulate the drug response in prostate cell lines in Figures 6, 7 and 8 of [the PhysiBoSS 2.0 paper](https://www.nature.com/articles/s41540-023-00314-4). # Getting started -- Fork this repository. -- Make a new branch from main for your PhysiBoSS model. -- Update the new branch with your model -- Update model.yml metadata file, as well as the readme for a quick description of the model -- Open a pull request. Building of your model will be tested on Windows, Mac and Linux. -- When your pull request is accepted by the editors a new repository will be created in the github PhysiBoSS-Models organization, and build/upload of your model will automatically be triggered. Once complete, the model is available on PhysiBoSS-Models. +The XML in the `config` folder are: +- PhysiCell_settings_LNCaP.xml: LNCaP cell line simulation +- PhysiCell_settings_LNCaP_Luminespib_2.xml: LNCaP cell line simulation adding Luminespib drug (anti_HSPs). +- PhysiCell_settings_LNCaP_Pictilisib_2.xml: LNCaP cell line simulation adding Pictilisib drug (anti_PI3K). +- PhysiCell_settings_LNCaP_Luminespib_Pictilisib_2.xml: LNCaP cell line simulation adding Luminespib and Pictilisib drugs. + +Files in the `scripts` folder have post processing scripts. + +# Authors: + +Arnau Montagud, Annika Meert, Gerard Pradas and Miguel Ponce de Leon, BSC-CNS + +Contact: arnau.montagud at csic.es diff --git a/config/PhysiCell_settings_LNCaP.xml b/config/PhysiCell_settings_LNCaP.xml new file mode 100644 index 0000000..8854269 --- /dev/null +++ b/config/PhysiCell_settings_LNCaP.xml @@ -0,0 +1,332 @@ + + + -500 + 500 + -500 + 500 + -500 + 500 + 15 + 15 + 15 + false + + + + 10080 + min + micron + 0.01 + 0.1 + 6 + + + + 4 + + + + output_nodrug_new_dt6 + + 240 + true + + + 240 + true + + false + true + + + + false + + + + + false + true + false + + + + + + 100000.0 + 0.0005 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + + 1200.0 + 0.0275 + + 0.0 + 0.0 + + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + 1200.0 + 0.0275 + + 0.0 + 0.0 + + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + true + true + + + ./config/initial.mat + + + + ./config/dirichlet.mat + + + + + + + + + + 0.0003155 + + + + + + 5.31667e-05 + + 516 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 0.0 + + 0 + 86400 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 2494 + 0.75 + 540 + 0.05 + 0.0045 + 0.0055 + 0 + 0 + 2.0 + + + 0.4 + 100.0 + 1.1 + + 1.0 + 0.8 + + + 1.8 + 15.12 + + 4.0 + 10.0 + 0.1 + 1.0 + 0.0 + + + 1 + 1 + 0 + + + false + false + + false + oxygen + 1 + + + false + false + + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + 0 + 38 + 10 + 0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + + + 0.0 + + 0.0 + 0.0 + + + 0.0 + 0.0 + + 1.0 + + 0.0 + 0.0 + + + + + + + + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg + + 6.0 + 0.0 + 10.0 + 0.0 + + + + + + + + + + 0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + + + ./config + init2.csv + + + + + 0 + + 0 + 0.35 + 2 + + + 2 + 0.0003155 + + + 5 + LNCaP + + + + -1 + + 0.0025 + 0.1 + 2.30653618101248 + 3.85680179514967 + 10.0 + 0.0 + 0 + 0.0025 + 0.1 + 2.30653618101248 + 7.02401783651853 + 0.25 + 0.0 + 0 + + diff --git a/config/PhysiCell_settings_LNCaP_Luminespib_2.xml b/config/PhysiCell_settings_LNCaP_Luminespib_2.xml new file mode 100644 index 0000000..4a11b4a --- /dev/null +++ b/config/PhysiCell_settings_LNCaP_Luminespib_2.xml @@ -0,0 +1,332 @@ + + + -500 + 500 + -500 + 500 + -500 + 500 + 15 + 15 + 15 + false + + + + 10080 + min + micron + 0.01 + 0.1 + 6 + + + + 1 + + + + output_lumi + + 240 + true + + + 240 + true + + false + true + + + + false + + + + + false + true + false + + + + + + 100000.0 + 0.0005 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + + 1200.0 + 0.0275 + + 0.0 + 0.0 + + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + 1200.0 + 0.0275 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + true + true + + + ./config/initial.mat + + + + ./config/dirichlet.mat + + + + + + + + + + 0.0003155 + + + + + + 5.31667e-05 + + 516 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 0.0 + + 0 + 86400 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 2494 + 0.75 + 540 + 0.05 + 0.0045 + 0.0055 + 0 + 0 + 2.0 + + + 0.4 + 100.0 + 1.1 + + 1.0 + 0.8 + + + 1.8 + 15.12 + + 4.0 + 10.0 + 0.1 + 1.0 + 0.0 + + + 1 + 1 + 0 + + + false + false + + false + oxygen + 1 + + + false + false + + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + 0 + 38 + 10 + 0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + + + 0.0 + + 0.0 + 0.0 + + + 0.0 + 0.0 + + 1.0 + + 0.0 + 0.0 + + + + + + + + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg + + 6.0 + 0.0 + 10.0 + 0.0 + + + + + + + + + + 0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + + + ./config + init2.csv + + + + + 0 + + 0 + 0.35 + 2 + + + 2 + 0.0003155 + + + 5 + LNCaP + + + + 1 + + 0.0025 + 0.1 + 2.30653618101248 + 3.85680179514967 + 10.0 + 0.0 + 0 + 0.0025 + 0.1 + 2.30653618101248 + 7.02401783651853 + 0.25 + 0.0 + IC90 + + diff --git a/config/PhysiCell_settings_LNCaP_Luminespib_Pictilisib_2.xml b/config/PhysiCell_settings_LNCaP_Luminespib_Pictilisib_2.xml new file mode 100644 index 0000000..54afa89 --- /dev/null +++ b/config/PhysiCell_settings_LNCaP_Luminespib_Pictilisib_2.xml @@ -0,0 +1,332 @@ + + + -500 + 500 + -500 + 500 + -500 + 500 + 15 + 15 + 15 + false + + + + 10080 + min + micron + 0.01 + 0.1 + 6 + + + + 4 + + + + output_lumi_picti + + 240 + true + + + 240 + true + + false + true + + + + false + + + + + false + true + false + + + + + + 100000.0 + 0.0005 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + + 1200.0 + 0.0275 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + + 1200.0 + 0.0275 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + true + true + + + ./config/initial.mat + + + + ./config/dirichlet.mat + + + + + + + + + + 0.0003155 + + + + + + 5.31667e-05 + + 516 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 0.0 + + 0 + 86400 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 2494 + 0.75 + 540 + 0.05 + 0.0045 + 0.0055 + 0 + 0 + 2.0 + + + 0.4 + 100.0 + 1.1 + + 1.0 + 0.8 + + + 1.8 + 15.12 + + 4.0 + 10.0 + 0.1 + 1.0 + 0.0 + + + 1 + 1 + 0 + + + false + false + + false + oxygen + 1 + + + false + false + + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + 0 + 38 + 10 + 0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + + + 0.0 + + 0.0 + 0.0 + + + 0.0 + 0.0 + + 1.0 + + 0.0 + 0.0 + + + + + + + + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg + + 6.0 + 0.0 + 10.0 + 0.0 + + + + + + + + + + 0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + + + ./config + init2.csv + + + + + 0 + + 0 + 0.35 + 2 + + + 2 + 0.0003155 + + + 5 + LNCaP + + + + 1 + + 0.0025 + 0.1 + 2.30653618101248 + 3.85680179514967 + 10.0 + 0.0 + IC90 + 0.0025 + 0.1 + 2.30653618101248 + 7.02401783651853 + 0.25 + 0.0 + IC90 + + diff --git a/config/PhysiCell_settings_LNCaP_Pictilisib_2.xml b/config/PhysiCell_settings_LNCaP_Pictilisib_2.xml new file mode 100644 index 0000000..cccc9a7 --- /dev/null +++ b/config/PhysiCell_settings_LNCaP_Pictilisib_2.xml @@ -0,0 +1,332 @@ + + + -500 + 500 + -500 + 500 + -500 + 500 + 15 + 15 + 15 + false + + + + 10080 + min + micron + 0.01 + 0.1 + 6 + + + + 4 + + + + output_picti + + 240 + true + + + 240 + true + + false + true + + + + false + + + + + false + true + false + + + + + + 100000.0 + 0.0005 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + + 1200.0 + 0.0275 + + 0.0 + 0.0 + + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + 1200.0 + 0.0275 + + 160.0 + 160.0 + + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + 38.0 + + + + true + true + + + ./config/initial.mat + + + + ./config/dirichlet.mat + + + + + + + + + + 0.0003155 + + + + + + 5.31667e-05 + + 516 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 0.0 + + 0 + 86400 + + + + 0.05 + 0 + 1.66667e-02 + 5.83333e-03 + 0 + 2.0 + + + + + 2494 + 0.75 + 540 + 0.05 + 0.0045 + 0.0055 + 0 + 0 + 2.0 + + + 0.4 + 100.0 + 1.1 + + 1.0 + 0.8 + + + 1.8 + 15.12 + + 4.0 + 10.0 + 0.1 + 1.0 + 0.0 + + + 1 + 1 + 0 + + + false + false + + false + oxygen + 1 + + + false + false + + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + 0 + 38 + 10 + 0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + 0.0 + 1.0 + 0.0025 + 0.0 + + + + + 0.0 + + 0.0 + 0.0 + + + 0.0 + 0.0 + + 1.0 + + 0.0 + 0.0 + + + + + + + + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd + ./config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg + + 6.0 + 0.0 + 10.0 + 0.0 + + + + + + + + + + 0 + 0.0 + 0.0 + 0.0 + 0.0 + + + + + + + + + ./config + init2.csv + + + + + 0 + + 0 + 0.35 + 2 + + + 2 + 0.0003155 + + + 5 + LNCaP + + + + 1 + + 0.0025 + 0.1 + 2.30653618101248 + 3.85680179514967 + 10.0 + 0.0 + IC90 + 0.0025 + 0.1 + 2.30653618101248 + 7.02401783651853 + 0.25 + 0.0 + 0 + + diff --git a/config/boolean_network/LNCaP_mut_RNA_00.bnd b/config/boolean_network/LNCaP_mut_RNA_00.bnd new file mode 100644 index 0000000..accd46a --- /dev/null +++ b/config/boolean_network/LNCaP_mut_RNA_00.bnd @@ -0,0 +1,823 @@ +Node Acidosis { + logic = (Acidosis); + rate_up = @logic ? $u_Acidosis : 0; + rate_down = @logic ? 0 : $d_Acidosis; +} + +Node AKT { + logic = ((HSPs | (PDK1 & PIP3) | PIP3 | (SHH & PIP3)) & !PTCH1); + rate_up = @logic ? $u_AKT : 0; + rate_down = @logic ? 0 : $d_AKT; +} + +Node AMPK { + logic = (ATR | HIF1 | AMP_ATP | ATM) & !FGFR3; + rate_up = @logic ? $u_AMPK : 0; + rate_down = @logic ? 0 : $d_AMPK; +} + +Node AMP_ATP { + logic = (!Nutrients); + rate_up = @logic ? $u_AMP_ATP : 0; + rate_down = @logic ? 0 : $d_AMP_ATP; +} + +Node Androgen { + logic = (Androgen); + rate_up = @logic ? $u_Androgen : 0; + rate_down = @logic ? 0 : $d_Androgen; +} + +Node Angiogenesis { + logic = (VEGF & (NF_kB | SMAD)); + rate_up = @logic ? $u_Angiogenesis : 0; + rate_down = @logic ? 0 : $d_Angiogenesis; +} + +Node APAF1 { + logic = ((Caspase8 | BAX | p53 | Bak | HSPs) & !Bcl_XL & !BCL2 & !AKT); + rate_up = @logic ? $u_APAF1 : 0; + rate_down = @logic ? 0 : $d_APAF1; +} + +Node Apoptosis { + logic = (Caspase9 | Caspase8 | Caspase3); + rate_up = @logic ? $u_Apoptosis : 0; + rate_down = @logic ? 0 : $d_Apoptosis; +} + +Node AR { + logic = ((GLI | EP300 | HSPs | NKX3_1 | EZH2 | NCOA3 | PKC | SMAD | Androgen) & !PTEN & !NCOR1 & !NCOR2 & !MDM2); + rate_up = @logic ? $u_AR : 0; + rate_down = @logic ? 0 : $d_AR; +} + +Node AR_ERG { + logic = ((AR & fused_event) | (AR & fused_event & !NKX3_1)); + rate_up = @logic ? $u_AR_ERG : 0; + rate_down = @logic ? 0 : $d_AR_ERG; +} + +Node ATM { + logic = (DNA_Damage); + rate_up = @logic ? $u_ATM : 0; + rate_down = @logic ? 0 : $d_ATM; +} + +Node ATR { + logic = (DNA_Damage); + rate_up = @logic ? $u_ATR : 0; + rate_down = @logic ? 0 : $d_ATR; +} + +Node AXIN1 { + logic = (GSK3); + rate_up = @logic ? $u_AXIN1 : 0; + rate_down = @logic ? 0 : $d_AXIN1; +} + +Node BAD { + logic = (!AKT & !HIF1 & !p90RSK & !RAF & !YWHAZ); + rate_up = @logic ? $u_BAD : 0; + rate_down = @logic ? 0 : $d_BAD; +} + +Node Bak { + logic = (Caspase8); + rate_up = @logic ? $u_Bak : 0; + rate_down = @logic ? 0 : $d_Bak; +} + +Node BAX { + logic = (!BCL2 & !HIF1 & !YWHAZ & (JNK | HIF1 | p53) & !ETS1); + rate_up = @logic ? $u_BAX : 0; + rate_down = @logic ? 0 : $d_BAX; +} + +Node BCL2 { + logic = ((HSPs | NF_kB | RUNX2) & !BAX & !p53 & !DAXX & !BAD); + rate_up = @logic ? $u_BCL2 : 0; + rate_down = @logic ? 0 : $d_BCL2; +} + +Node Bcl_XL { + logic = (AR & !BAD & !p53); + rate_up = @logic ? $u_Bcl_XL : 0; + rate_down = @logic ? 0 : $d_Bcl_XL; +} + +Node beta_catenin { + logic = (!GSK3 & !p53 & !AXIN1 & (EZH2 | YWHAZ)); + rate_up = @logic ? $u_beta_catenin : 0; + rate_down = @logic ? 0 : $d_beta_catenin; +} + +Node BIRC5 { + logic = (HSPs & !p53); + rate_up = @logic ? $u_BIRC5 : 0; + rate_down = @logic ? 0 : $d_BIRC5; +} + +Node BMP2 { + logic = (GLI & (TGFBR | FGFR3)); + rate_up = @logic ? $u_BMP2 : 0; + rate_down = @logic ? 0 : $d_BMP2; +} + +Node BRCA1 { + logic = ((E2F1 | ATM | ATR | (E2F1 & EP300) | (ATM & EP300) | (ATR & EP300)) & !CyclinD & !Bak); + rate_up = @logic ? $u_BRCA1 : 0; + rate_down = @logic ? 0 : $d_BRCA1; +} + +Node BRCA2 { + logic = (ATM & ATR & EP300); + rate_up = @logic ? $u_BRCA2 : 0; + rate_down = @logic ? 0 : $d_BRCA2; +} + +Node Carcinogen { + logic = (Carcinogen); + rate_up = @logic ? $u_Carcinogen : 0; + rate_down = @logic ? 0 : $d_Carcinogen; +} + +Node Caspase3 { + logic = (Caspase8 | Caspase9); + rate_up = @logic ? $u_Caspase3 : 0; + rate_down = @logic ? 0 : $d_Caspase3; +} + +Node Caspase8 { + logic = (!cFLAR & FADD); + rate_up = @logic ? $u_Caspase8 : 0; + rate_down = @logic ? 0 : $d_Caspase8; +} + +Node Caspase9 { + logic = ((!AKT & CytoC & APAF1 & PTCH1) | (!AKT & BIRC5 & CytoC & APAF1) | (!AKT & BIRC5 & CytoC & APAF1 & PTCH1)); + rate_up = @logic ? $u_Caspase9 : 0; + rate_down = @logic ? 0 : $d_Caspase9; +} + +Node CDH2 { + logic = (TWIST1); + rate_up = @logic ? $u_CDH2 : 0; + rate_down = @logic ? 0 : $d_CDH2; +} + +Node cFLAR { + logic = ((AKT | AR) & !JNK); + rate_up = @logic ? $u_cFLAR : 0; + rate_down = @logic ? 0 : $d_cFLAR; +} + +Node CHK1_2 { + logic = (ATM & ATR & BRCA1); + rate_up = @logic ? $u_CHK1_2 : 0; + rate_down = @logic ? 0 : $d_CHK1_2; +} + +Node COX4I2 { + logic = (HIF1); + rate_up = @logic ? $u_COX4I2 : 0; + rate_down = @logic ? 0 : $d_COX4I2; +} + +Node CyclinB { + logic = (E2F1 & !p21); + rate_up = @logic ? $u_CyclinB : 0; + rate_down = @logic ? 0 : $d_CyclinB; +} + +Node CyclinD { + logic = ((GLI | TCF | NF_kB) & MYC_MAX & !p21 & !FOXO & !p15 & !GSK3); + rate_up = @logic ? $u_CyclinD : 0; + rate_down = @logic ? 0 : $d_CyclinD; +} + +Node CytoC { + logic = ((Caspase8 | BAX | p53 | Bak) & !Bcl_XL & !BCL2 & !AKT); + rate_up = @logic ? $u_CytoC : 0; + rate_down = @logic ? 0 : $d_CytoC; +} + +Node DAXX { + logic = (!ATM & !ATR & !SPOP); + rate_up = @logic ? $u_DAXX : 0; + rate_down = @logic ? 0 : $d_DAXX; +} + +Node DNA_Damage { + logic = ((Carcinogen | (Carcinogen & ROS)) & !SPOP); + rate_up = @logic ? $u_DNA_Damage : 0; + rate_down = @logic ? 0 : $d_DNA_Damage; +} + +Node DNA_Repair { + logic = (ATM & ATR & (BRCA1 | BRCA2)); + rate_up = @logic ? $u_DNA_Repair : 0; + rate_down = @logic ? 0 : $d_DNA_Repair; +} + +Node Dsh { + logic = (WNT); + rate_up = @logic ? $u_Dsh : 0; + rate_down = @logic ? 0 : $d_Dsh; +} + +Node E2F1 { + logic = ((AR | (FOXA1 & AR) | (ATR & AR) | (ATM & AR)) & !RB1 & !MXI1); + rate_up = @logic ? $u_E2F1 : 0; + rate_down = @logic ? 0 : $d_E2F1; +} + +Node eEF2 { + logic = (!eEF2K); + rate_up = @logic ? $u_eEF2 : 0; + rate_down = @logic ? 0 : $d_eEF2; +} + +Node eEF2K { + logic = (p90RSK | p70S6kab); + rate_up = @logic ? $u_eEF2K : 0; + rate_down = @logic ? 0 : $d_eEF2K; +} + +Node EGF { + logic = (EGF); + rate_up = @logic ? $u_EGF : 0; + rate_down = @logic ? 0 : $d_EGF; +} + +Node EGFR { + logic = ((!TGFBR & !FRS2 & !EGFR & !PKC & (ERK | Androgen | EGF)) | (!TGFBR & !FRS2 & (Androgen | EGF))); + rate_up = @logic ? $u_EGFR : 0; + rate_down = @logic ? 0 : $d_EGFR; +} + +Node EMT { + logic = (CDH2 & !E_cadherin & !FOXA1); + rate_up = @logic ? $u_EMT : 0; + rate_down = @logic ? 0 : $d_EMT; +} + +Node EP300 { + logic = (AKT); + rate_up = @logic ? $u_EP300 : 0; + rate_down = @logic ? 0 : $d_EP300; +} + +Node ERG { + logic = (ERK); + rate_up = @logic ? $u_ERG : 0; + rate_down = @logic ? 0 : $d_ERG; +} + +Node ERK { + logic = (RAF | MEK1_2); + rate_up = @logic ? $u_ERK : 0; + rate_down = @logic ? 0 : $d_ERK; +} + +Node ETS1 { + logic = (!p53 & (RTK | ERK)); + rate_up = @logic ? $u_ETS1 : 0; + rate_down = @logic ? 0 : $d_ETS1; +} + +Node ETV1 { + logic = (ETS1 | MEK1_2); + rate_up = @logic ? $u_ETV1 : 0; + rate_down = @logic ? 0 : $d_ETV1; +} + +Node EZH2 { + logic = (AKT & E2F1 & (ERG | AR_ERG)); + rate_up = @logic ? $u_EZH2 : 0; + rate_down = @logic ? 0 : $d_EZH2; +} + +Node E_cadherin { + logic = ((beta_catenin | AR_ERG) & !Snail & !ERG & !NF_kB & !Slug); + rate_up = @logic ? $u_E_cadherin : 0; + rate_down = @logic ? 0 : $d_E_cadherin; +} + +Node FADD { + logic = (PTCH1 | TNFalpha) & !SHH & !MAP3K1_3; + rate_up = @logic ? $u_FADD : 0; + rate_down = @logic ? 0 : $d_FADD; +} + +Node FGF { + logic = (FGF); + rate_up = @logic ? $u_FGF : 0; + rate_down = @logic ? 0 : $d_FGF; +} + +Node FGFR3 { + logic = ((FGF | (FGF & RUNX2)) & !FRS2 & !PKC & !TGFBR); + rate_up = @logic ? $u_FGFR3 : 0; + rate_down = @logic ? 0 : $d_FGFR3; +} + +Node FOS { + logic = (ERK | ETS1 | AR); + rate_up = @logic ? $u_FOS : 0; + rate_down = @logic ? 0 : $d_FOS; +} + +Node FOXA1 { + logic = (FOXO); + rate_up = @logic ? $u_FOXA1 : 0; + rate_down = @logic ? 0 : $d_FOXA1; +} + +Node FOXO { + logic = (MAP3K1_3 & JNK & !AKT); + rate_up = @logic ? $u_FOXO : 0; + rate_down = @logic ? 0 : $d_FOXO; +} + +Node FRS2 { + logic = (!ERK & !EGFR & FGFR3 & !FRS2 & !TGFBR); + rate_up = @logic ? $u_FRS2 : 0; + rate_down = @logic ? 0 : $d_FRS2; +} + +Node fused_event { + logic = (fused_event); + rate_up = @logic ? $u_fused_event : 0; + rate_down = @logic ? 0 : $d_fused_event; +} + +Node GADD45 { + logic = (SMAD | p53); + rate_up = @logic ? $u_GADD45 : 0; + rate_down = @logic ? 0 : $d_GADD45; +} + +Node GLI { + logic = ((WNT | SMO) & !SPOP); + rate_up = @logic ? $u_GLI : 0; + rate_down = @logic ? 0 : $d_GLI; +} + +Node GLUT1 { + logic = ((HIF1 | MYC_MAX | AKT) & Nutrients); + rate_up = @logic ? $u_GLUT1 : 0; + rate_down = @logic ? 0 : $d_GLUT1; +} + +Node Glycolysis { + logic = (GLUT1 & Lactic_acid); + rate_up = @logic ? $u_Glycolysis : 0; + rate_down = @logic ? 0 : $d_Glycolysis; +} + +Node GSH { + logic = (MYC_MAX | NF_kB | p21); + rate_up = @logic ? $u_GSH : 0; + rate_down = @logic ? 0 : $d_GSH; +} + +Node GSK3 { + logic = (!AKT & !Dsh & MEK1_2 & !mTORC2 & !p90RSK); + rate_up = @logic ? $u_GSK3 : 0; + rate_down = @logic ? 0 : $d_GSK3; +} + +Node HIF1 { + logic = (!PHDs & !VHL & !p53 & !FOXO & (HSPs | mTORC1 | mTORC2 | Hypoxia | MYC_MAX)); + rate_up = @logic ? $u_HIF1 : 0; + rate_down = @logic ? 0 : $d_HIF1; +} + +Node HSPs { + logic = ((PKC | FOXA1 | Carcinogen) & !BRCA1 & !E2F1 & !AKT); + rate_up = @logic ? $u_HSPs : 0; + rate_down = @logic ? 0 : $d_HSPs; +} + +Node Hypermethylation { + logic = (!IDH1); + rate_up = @logic ? $u_Hypermethylation : 0; + rate_down = @logic ? 0 : $d_Hypermethylation; +} + +Node Hypoxia { + logic = (Hypoxia); + rate_up = @logic ? $u_Hypoxia : 0; + rate_down = @logic ? 0 : $d_Hypoxia; +} + +Node IDH1 { + logic = (SMAD | FOXA1 | mTORC2 | ((SMAD | mTORC2 | FOXA1) & MEK1_2)); + rate_up = @logic ? $u_IDH1 : 0; + rate_down = @logic ? 0 : $d_IDH1; +} + +Node IKK { + logic = ((mTORC2 | AKT) & (PKC | TAK1 | ETS1) & !PHDs & !p53); + rate_up = @logic ? $u_IKK : 0; + rate_down = @logic ? 0 : $d_IKK; +} + +Node Invasion { + logic = ((CDH2 & SMAD) | (CDH2 & SMAD & EZH2) | beta_catenin); + rate_up = @logic ? $u_Invasion : 0; + rate_down = @logic ? 0 : $d_Invasion; +} + +Node JNK { + logic = ((ATM | GADD45 | TAK1 | MAP3K1_3) & !ERK & !p38); + rate_up = @logic ? $u_JNK : 0; + rate_down = @logic ? 0 : $d_JNK; +} + +Node JUN { + logic = (AR | ETS1 | JNK); + rate_up = @logic ? $u_JUN : 0; + rate_down = @logic ? 0 : $d_JUN; +} + +Node Lactic_acid { + logic = (LDHA); + rate_up = @logic ? $u_Lactic_acid : 0; + rate_down = @logic ? 0 : $d_Lactic_acid; +} + +Node LDHA { + logic = (HIF1 | MYC_MAX | IDH1); + rate_up = @logic ? $u_LDHA : 0; + rate_down = @logic ? 0 : $d_LDHA; +} + +Node MAP3K1_3 { + logic = (RAS & !(FOS | JUN | JNK | p38)); + rate_up = @logic ? $u_MAP3K1_3 : 0; + rate_down = @logic ? 0 : $d_MAP3K1_3; +} + + +Node MDM2 { + logic = (((AKT | p53) & DAXX & !p14ARF & !ATR & !ATM)); + rate_up = @logic ? $u_MDM2 : 0; + rate_down = @logic ? 0 : $d_MDM2; +} + +Node MED12 { + logic = (!GLI); + rate_up = @logic ? $u_MED12 : 0; + rate_down = @logic ? 0 : $d_MED12; +} + +Node MEK1_2 { + logic = (MAP3K1_3 | RAF | PDK1); + rate_up = @logic ? $u_MEK1_2 : 0; + rate_down = @logic ? 0 : $d_MEK1_2; +} + +Node Metastasis { + logic = (Migration & AR_ERG); + rate_up = @logic ? $u_Metastasis : 0; + rate_down = @logic ? 0 : $d_Metastasis; +} + +Node Migration { + logic = (((AKT & ERK) | (AKT & ETV1)) & Invasion & EMT); + rate_up = @logic ? $u_Migration : 0; + rate_down = @logic ? 0 : $d_Migration; +} + +Node mTORC1 { + logic = ((AKT | (AKT & Rheb)) & !AMPK & !TSC1_2); + rate_up = @logic ? $u_mTORC1 : 0; + rate_down = @logic ? 0 : $d_mTORC1; +} + +Node mTORC2 { + logic = ((!AMPK & RAGS & Rheb) | (AKT & !AMPK & Rheb) | (AKT & !AMPK & RAGS) | (AKT & RAGS & Rheb & !AMPK)); + rate_up = @logic ? $u_mTORC2 : 0; + rate_down = @logic ? 0 : $d_mTORC2; +} + +Node MXI1 { + logic = (HIF1); + rate_up = @logic ? $u_MXI1 : 0; + rate_down = @logic ? 0 : $d_MXI1; +} + +Node MYC { + logic = ((GLI | ERK | TCF | JUN | FOS | AR_ERG | ERG | NF_kB | SHH) & (mTORC1 | E2F1 | p38) & !HIF1); + rate_up = @logic ? $u_MYC : 0; + rate_down = @logic ? 0 : $d_MYC; +} + +Node MYC_MAX { + logic = (MYC & !MXI1 & !TGFBR & !SMAD); + rate_up = @logic ? $u_MYC_MAX : 0; + rate_down = @logic ? 0 : $d_MYC_MAX; +} + +Node NCOA3 { + logic = (!SPOP & p38); + rate_up = @logic ? $u_NCOA3 : 0; + rate_down = @logic ? 0 : $d_NCOA3; +} + +Node NCOR1 { + logic = (!AKT); + rate_up = @logic ? $u_NCOR1 : 0; + rate_down = @logic ? 0 : $d_NCOR1; +} + +Node NCOR2 { + logic = (!AKT & !FOXO); + rate_up = @logic ? $u_NCOR2 : 0; + rate_down = @logic ? 0 : $d_NCOR2; +} + +Node NF1 { + logic = (!PKC); + rate_up = @logic ? $u_NF1 : 0; + rate_down = @logic ? 0 : $d_NF1; +} + +Node NF_kB { + logic = ((E_cadherin | IKK | DNA_Damage) & (NCOA3 | PIP3 | Snail)); + rate_up = @logic ? $u_NF_kB : 0; + rate_down = @logic ? 0 : $d_NF_kB; +} + +Node NKX3_1 { + logic = (PKC & AR & !ERG & !AR_ERG); + rate_up = @logic ? $u_NKX3_1 : 0; + rate_down = @logic ? 0 : $d_NKX3_1; +} + +Node Nutrients { + logic = (Nutrients); + rate_up = @logic ? $u_Nutrients : 0; + rate_down = @logic ? 0 : $d_Nutrients; +} + +Node p14ARF { + logic = (E2F1 & MYC_MAX & RAS); + rate_up = @logic ? $u_p14ARF : 0; + rate_down = @logic ? 0 : $d_p14ARF; +} + +Node p15 { + logic = (ZBTB17); + rate_up = @logic ? $u_p15 : 0; + rate_down = @logic ? 0 : $d_p15; +} + +Node p21 { + logic = ((p53 | SMAD | HIF1 | ZBTB17) & !TERT & !MYC_MAX & !MDM2 & !AKT & !ERK); + rate_up = @logic ? $u_p21 : 0; + rate_down = @logic ? 0 : $d_p21; +} + +Node p38 { + logic = (MAP3K1_3 & !ERK & !GADD45); + rate_up = @logic ? $u_p38 : 0; + rate_down = @logic ? 0 : $d_p38; +} + +Node p53 { + logic = ((Acidosis | CHK1_2 | p38 | HIF1) & !BCL2 & !MDM2 & !HSPs & !Snail); + rate_up = @logic ? $u_p53 : 0; + rate_down = @logic ? 0 : $d_p53; +} + +Node p70S6kab { + logic = (mTORC2 | PDK1); + rate_up = @logic ? $u_p70S6kab : 0; + rate_down = @logic ? 0 : $d_p70S6kab; +} + +Node p90RSK { + logic = (PDK1 | ERK); + rate_up = @logic ? $u_p90RSK : 0; + rate_down = @logic ? 0 : $d_p90RSK; +} + +Node PDK1 { + logic = (HIF1 | MYC_MAX | PIP3); + rate_up = @logic ? $u_PDK1 : 0; + rate_down = @logic ? 0 : $d_PDK1; +} + +Node PHDs { + logic = (!Hypoxia & ROS); + rate_up = @logic ? $u_PHDs : 0; + rate_down = @logic ? 0 : $d_PHDs; +} + +Node PI3K { + logic = (TGFBR | FRS2 | EGFR); + rate_up = @logic ? $u_PI3K : 0; + rate_down = @logic ? 0 : $d_PI3K; +} + +Node PIP3 { + logic = ((!p53 & PI3K & !PTEN) | (!p53 & PI3K)); + rate_up = @logic ? $u_PIP3 : 0; + rate_down = @logic ? 0 : $d_PIP3; +} + +Node PKC { + logic = (RTK & WNT); + rate_up = @logic ? $u_PKC : 0; + rate_down = @logic ? 0 : $d_PKC; +} + +Node Proliferation { + logic = (CyclinB | CyclinD); + rate_up = @logic ? $u_Proliferation : 0; + rate_down = @logic ? 0 : $d_Proliferation; +} + +Node PTCH1 { + logic = (!SHH & GLI); + rate_up = @logic ? $u_PTCH1 : 0; + rate_down = @logic ? 0 : $d_PTCH1; +} + +Node PTEN { + logic = ((!NF_kB & !Snail & FGFR3 & p53) | (!NF_kB & !Snail & p53)); + rate_up = @logic ? $u_PTEN : 0; + rate_down = @logic ? 0 : $d_PTEN; +} + +Node Quiescence { + logic = (!Apoptosis & !Proliferation); + rate_up = @logic ? $u_Quiescence : 0; + rate_down = @logic ? 0 : $d_Quiescence; +} + +Node RAF { + logic = (!EZH2 & RAS) | (EZH2); + rate_up = @logic ? $u_RAF : 0; + rate_down = @logic ? 0 : $d_RAF; +} + +Node RAGS { + logic = (!Hypoxia & Nutrients); + rate_up = @logic ? $u_RAGS : 0; + rate_down = @logic ? 0 : $d_RAGS; +} + +Node RAS { + logic = (EGFR | TGFBR | FRS2 | FGFR3); + rate_up = @logic ? $u_RAS : 0; + rate_down = @logic ? 0 : $d_RAS; +} + +Node RB1 { + logic = (!CyclinB & !CyclinD & CHK1_2 & !p53 & !AMPK); + rate_up = @logic ? $u_RB1 : 0; + rate_down = @logic ? 0 : $d_RB1; +} + +Node Rheb { + logic = (!TSC1_2); + rate_up = @logic ? $u_Rheb : 0; + rate_down = @logic ? 0 : $d_Rheb; +} + +Node ROS { + logic = ((COX4I2 | Hypoxia) & !GSH); + rate_up = @logic ? $u_ROS : 0; + rate_down = @logic ? 0 : $d_ROS; +} + +Node RTK { + logic = (EGFR | FGFR3); + rate_up = @logic ? $u_RTK : 0; + rate_down = @logic ? 0 : $d_RTK; +} + +Node RUNX2 { + logic = (!ETS1 & !FOXO & Slug) | (ETS1 & !FOXO); + rate_up = @logic ? $u_RUNX2 : 0; + rate_down = @logic ? 0 : $d_RUNX2; +} + +Node SHH { + logic = (!BMP2 & !ATR); + rate_up = @logic ? $u_SHH : 0; + rate_down = @logic ? 0 : $d_SHH; +} + +Node Slug { + logic = ((TCF | NF_kB) & !DAXX & !p53 & !MDM2); + rate_up = @logic ? $u_Slug : 0; + rate_down = @logic ? 0 : $d_Slug; +} + +Node SMAD { + logic = ((TNFalpha | TGFBR) & !AR); + rate_up = @logic ? $u_SMAD : 0; + rate_down = @logic ? 0 : $d_SMAD; +} + +Node SMO { + logic = (!PTCH1 & SHH); + rate_up = @logic ? $u_SMO : 0; + rate_down = @logic ? 0 : $d_SMO; +} + +Node Snail { + logic = ((GLI | NF_kB | SMAD | beta_catenin) & !GSK3 & !p53); + rate_up = @logic ? $u_Snail : 0; + rate_down = @logic ? 0 : $d_Snail; +} + +Node SPOP { + logic = (SPOP); + rate_up = @logic ? $u_SPOP : 0; + rate_down = @logic ? 0 : $d_SPOP; +} + +Node TAK1 { + logic = (TGFBR | TNFalpha); + rate_up = @logic ? $u_TAK1 : 0; + rate_down = @logic ? 0 : $d_TAK1; +} + +Node TCF { + logic = ((beta_catenin | AR_ERG | ERG) & !TAK1); + rate_up = @logic ? $u_TCF : 0; + rate_down = @logic ? 0 : $d_TCF; +} + +Node TERT { + logic = ((GLI | AKT | HIF1 | MYC_MAX | NF1 | NF_kB | MYC_MAX) & !ZBTB17 & !p53 & !eEF2 & !SMAD); + rate_up = @logic ? $u_TERT : 0; + rate_down = @logic ? 0 : $d_TERT; +} + +Node TGFb { + logic = (TGFb); + rate_up = @logic ? $u_TGFb : 0; + rate_down = @logic ? 0 : $d_TGFb; +} + +Node TGFBR { + logic = ((TGFb | (HIF1 & TGFb)) & !MED12); + rate_up = @logic ? $u_TGFBR : 0; + rate_down = @logic ? 0 : $d_TGFBR; +} + +Node TNFalpha { + logic = (TNFalpha); + rate_up = @logic ? $u_TNFalpha : 0; + rate_down = @logic ? 0 : $d_TNFalpha; +} + +Node TSC1_2 { + logic = ((AMPK | p53 | HIF1) & !AKT & !ERK & !RAF & !p90RSK); + rate_up = @logic ? $u_TSC1_2 : 0; + rate_down = @logic ? 0 : $d_TSC1_2; +} + +Node TWIST1 { + logic = (Snail | beta_catenin); + rate_up = @logic ? $u_TWIST1 : 0; + rate_down = @logic ? 0 : $d_TWIST1; +} + +Node VEGF { + logic = (HIF1 & MYC_MAX); + rate_up = @logic ? $u_VEGF : 0; + rate_down = @logic ? 0 : $d_VEGF; +} + +Node VHL { + logic = (!Hypoxia & !ROS); + rate_up = @logic ? $u_VHL : 0; + rate_down = @logic ? 0 : $d_VHL; +} + +Node WNT { + logic = ((((ERG | AR_ERG) & GLI) | ERG | AR_ERG) & !p53); + rate_up = @logic ? $u_WNT : 0; + rate_down = @logic ? 0 : $d_WNT; +} + +Node YWHAZ { + logic = (AR); + rate_up = @logic ? $u_YWHAZ : 0; + rate_down = @logic ? 0 : $d_YWHAZ; +} + +Node ZBTB17 { + logic = (!MYC_MAX & AR); + rate_up = @logic ? $u_ZBTB17 : 0; + rate_down = @logic ? 0 : $d_ZBTB17; +} + diff --git a/config/boolean_network/LNCaP_mut_RNA_00.cfg b/config/boolean_network/LNCaP_mut_RNA_00.cfg new file mode 100644 index 0000000..3936353 --- /dev/null +++ b/config/boolean_network/LNCaP_mut_RNA_00.cfg @@ -0,0 +1,575 @@ +$u_Acidosis = 1; +$d_Acidosis = 1; +$u_AKT=1.15285; +$d_AKT=0.86742; +$u_AMP_ATP=0.06407; +$d_AMP_ATP=15.60793; +$u_AMPK=0; +$d_AMPK=0.91263; +$u_Androgen = 1; +$d_Androgen = 1; +$u_Angiogenesis = 1; +$d_Angiogenesis = 1; +$u_Apoptosis = 1; +$d_Apoptosis = 1; +$u_AR=100.0; +$d_AR=0; +$u_AR_ERG = 1; +$d_AR_ERG = 1; +$u_ATM=0; +$d_ATM=5.81395; +$u_ATR=0.1955; +$d_ATR=5.11509; +$u_AXIN1=0.3574; +$d_AXIN1=2.79799; +$u_BAD=4.09692; +$d_BAD=0.24409; +$u_Bak=0.1955; +$d_Bak=5.11509; +$u_BAX=0; +$d_BAX=10.39933; +$u_BCL2=0.01; +$d_BCL2=100.0; +$u_Bcl_XL=0.28221; +$d_Bcl_XL=3.54346; +$u_beta_catenin=0.60475; +$d_beta_catenin=0; +$u_BIRC5=0.23826; +$d_BIRC5=4.1971; +$u_BMP2 = 1; +$d_BMP2 = 1; +$u_BRCA1=0.49177; +$d_BRCA1=2.03347; +$u_BRCA2=0; +$d_BRCA2=5.11509; +$u_Carcinogen = 1; +$d_Carcinogen = 1; +$u_Caspase3 = 1; +$d_Caspase3 = 1; +$u_Caspase8=0.07573; +$d_Caspase8=13.20481; +$u_Caspase9=0.1955; +$d_Caspase9=5.11509; +$u_CDH2=0.01; +$d_CDH2=100.0; +$u_cFLAR=1.0; +$d_cFLAR=1.0; +$u_CHK1_2=0; +$d_CHK1_2=0.74346; +$u_COX4I2 = 1; +$d_COX4I2 = 1; +$u_CyclinB=0.08593; +$d_CyclinB=11.63738; +$u_CyclinD=0.72702; +$d_CyclinD=1.37548; +$u_CytoC=0.47463; +$d_CytoC=2.1069; +$u_APAF1=0; +$d_APAF1=65.74622; +$u_DAXX=0.73662; +$d_DAXX=1.35755; +$u_DNA_Damage = 1; +$d_DNA_Damage = 1; +$u_DNA_Repair = 1; +$d_DNA_Repair = 1; +$u_Dsh=0; +$d_Dsh=0.40456; +$u_E2F1=0.43193; +$d_E2F1=2.31519; +$u_E_cadherin=100.0; +$d_E_cadherin=0.01; +$u_eEF2=22.1308; +$d_eEF2=0.04519; +$u_eEF2K=5.11518; +$d_eEF2K=0.1955; +$u_EMT = 1; +$d_EMT = 1; +$u_EP300=1.59488; +$d_EP300=0.62701; +$u_ERG=0.01; +$d_ERG=100.0; +$u_ERK=1.46443; +$d_ERK=0.68286; +$u_ETS1=0.0303; +$d_ETS1=33.0033; +$u_ETV1=99.99718; +$d_ETV1=0.01; +$u_EZH2=0.25411; +$d_EZH2=3.9353; +$u_FADD=0.13852; +$d_FADD=7.21917; +$u_FOS=0.01086; +$d_FOS=92.08103; +$u_JUN=0.3012; +$d_JUN=3.32005; +$u_FOXA1=99.99995; +$d_FOXA1=0.01; +$u_FOXO=0.70855; +$d_FOXO=1.41133; +$u_EGF=8.51034; +$d_EGF=0.1175; +$u_EGFR=87.28647; +$d_EGFR=0.01146; +$u_FGF=0; +$d_FGF=100.0; +$u_FGFR3=0.01043; +$d_FGFR3=95.87728; +$u_FRS2=1.0; +$d_FRS2=1.0; +$u_GADD45=0.57004; +$d_GADD45=1.75426; +$u_GLI=0.01; +$d_GLI=100.0; +$u_GLUT1=0.38474; +$d_GLUT1=2.59916; +$u_Glycolysis = 1; +$d_Glycolysis = 1; +$u_GSH=34.85754; +$d_GSH=0.02869; +$u_GSK3=0.36889; +$d_GSK3=2.71084; +$u_HIF1=0.50171; +$d_HIF1=1.99318; +$u_HSPs=1.04632; +$d_HSPs=0.95573; +$u_Hypermethylation = 1; +$d_Hypermethylation = 1; +$u_Hypoxia = 1; +$d_Hypoxia = 1; +$u_IDH1=24.23845; +$d_IDH1=0.04126; +$u_Invasion = 1; +$d_Invasion = 1; +$u_IKK=0.47494; +$d_IKK=2.10553; +$u_JNK=0.70598; +$d_JNK=1.41647; +$u_Lactic_acid=6.08374; +$d_Lactic_acid=0.16437; +$u_LDHA=6.08374; +$d_LDHA=0.16437; +$u_MAP3K1_3=0.22457; +$d_MAP3K1_3=4.45295; +$u_MDM2=3.70371; +$d_MDM2=0.27; +$u_MED12=0.49995; +$d_MED12=2.0002; +$u_MEK1_2=2.74199; +$d_MEK1_2=0; +$u_Metastasis = 1; +$d_Metastasis = 1; +$u_Migration = 1; +$d_Migration = 1; +$u_mTORC1=1.03682; +$d_mTORC1=0.96449; +$u_mTORC2=1.73313; +$d_mTORC2=0.57699; +$u_MXI1=16.71485; +$d_MXI1=0.05983; +$u_MYC=4.81493; +$d_MYC=0.20769; +$u_MYC_MAX=2.19429; +$d_MYC_MAX=0.45573; +$u_NCOA3=0; +$d_NCOA3=13.75138; +$u_NCOR1=5.11518; +$d_NCOR1=0.1955; +$u_NCOR2=4.09692; +$d_NCOR2=0.24409; +$u_NF1=0; +$d_NF1=1.0; +$u_NF_kB=0.23915; +$d_NF_kB=4.18148; +$u_NKX3_1=100.0; +$d_NKX3_1=0.01; +$u_Nutrients = 1; +$d_Nutrients = 1; +$u_p14ARF=0.01565; +$d_p14ARF=63.89776; +$u_p15=0.01001; +$d_p15=99.9001; +$u_p21=10.60511; +$d_p21=0.09429; +$u_p38=0.04421; +$d_p38=22.61932; +$u_p53=4.77257; +$d_p53=0.20953; +$u_p70S6kab=2.97053; +$d_p70S6kab=0.33664; +$u_p90RSK=1.92967; +$d_p90RSK=0.51822; +$u_PDK1=26.84952; +$d_PDK1=0.03724; +$u_PHDs=0.37349; +$d_PHDs=2.67745; +$u_PI3K=0.51668; +$d_PI3K=1.93543; +$u_PIP3=0.01521; +$d_PIP3=65.74622; +$u_PKC=0; +$d_PKC=2.5697; +$u_Proliferation = 1; +$d_Proliferation = 1; +$u_PTCH1=0.33025; +$d_PTCH1=3.02801; +$u_PTEN=0; +$d_PTEN=2.07753; +$u_Quiescence = 1; +$d_Quiescence = 1; +$u_RAF=1.55217; +$d_RAF=0.64426; +$u_RAGS=0.01; +$d_RAGS=100.0; +$u_RAS=0.4606; +$d_RAS=2.17108; +$u_RB1=0.12234; +$d_RB1=8.17394; +$u_Rheb=0.1955; +$d_Rheb=5.11509; +$u_ROS = 1; +$d_ROS = 1; +$u_RTK=0.08743; +$d_RTK=11.43772; +$u_RUNX2=0.01369; +$d_RUNX2=73.04602; +$u_SHH=0.01; +$d_SHH=100.0; +$u_Slug=0.01; +$d_Slug=100.0; +$u_SMAD=0.31951; +$d_SMAD=3.12979; +$u_SMO=98.34156; +$d_SMO=0.01017; +$u_Snail=0.07454; +$d_Snail=13.41562; +$u_SPOP=3.75839; +$d_SPOP=0.26607; +$u_TAK1=0.01521; +$d_TAK1=65.74622; +$u_TCF=0.01552; +$d_TCF=64.43299; +$u_TERT=0.24141; +$d_TERT=4.14233; +$u_TGFb=0.01143; +$d_TGFb=87.48906; +$u_TGFBR=0.035; +$d_TGFBR=28.57143; +$u_fused_event = 1; +$d_fused_event = 1; +$u_TNFalpha=0.13372; +$d_TNFalpha=7.47831; +$u_TSC1_2=9.35066; +$d_TSC1_2=0.10694; +$u_TWIST1=0.02243; +$d_TWIST1=44.58315; +$u_VEGF=0.25808; +$d_VEGF=3.87477; +$u_VHL=7.12902; +$d_VHL=0.14027; +$u_WNT=0; +$d_WNT=27.5786; +$u_YWHAZ=0.04405; +$d_YWHAZ=22.70148; +$u_ZBTB17=0.0441; +$d_ZBTB17=22.67574; + +discrete_time = 0; +use_physrandgen = FALSE; +seed_pseudorandom = 100; +sample_count = 1; +max_time = 1; //10 min: 1 in Maboss = 10 in Physicell +time_tick = 0.01; +thread_count = 1; +statdist_traj_count = 100; +statdist_cluster_threshold = 0.9; + + +Acidosis.is_internal = TRUE; +AKT.is_internal = TRUE; +AMPK.is_internal = TRUE; +AMP_ATP.is_internal = TRUE; +Androgen.is_internal = TRUE; +Angiogenesis.is_internal = FALSE; +APAF1.is_internal = TRUE; +Apoptosis.is_internal = FALSE; +AR.is_internal = TRUE; +AR_ERG.is_internal = TRUE; +ATM.is_internal = TRUE; +ATR.is_internal = TRUE; +AXIN1.is_internal = TRUE; +BAD.is_internal = TRUE; +Bak.is_internal = TRUE; +BAX.is_internal = TRUE; +BCL2.is_internal = TRUE; +Bcl_XL.is_internal = TRUE; +beta_catenin.is_internal = TRUE; +BIRC5.is_internal = TRUE; +BMP2.is_internal = TRUE; +BRCA1.is_internal = TRUE; +BRCA2.is_internal = TRUE; +Carcinogen.is_internal = TRUE; +Caspase3.is_internal = TRUE; +Caspase8.is_internal = TRUE; +Caspase9.is_internal = TRUE; +CDH2.is_internal = TRUE; +cFLAR.is_internal = TRUE; +CHK1_2.is_internal = TRUE; +COX4I2.is_internal = TRUE; +CyclinB.is_internal = TRUE; +CyclinD.is_internal = TRUE; +CytoC.is_internal = TRUE; +DAXX.is_internal = TRUE; +DNA_Damage.is_internal = TRUE; +DNA_Repair.is_internal = FALSE; +Dsh.is_internal = TRUE; +E2F1.is_internal = TRUE; +eEF2.is_internal = TRUE; +eEF2K.is_internal = TRUE; +EGF.is_internal = TRUE; +EGFR.is_internal = TRUE; +EMT.is_internal = TRUE; +EP300.is_internal = TRUE; +ERG.is_internal = TRUE; +ERK.is_internal = TRUE; +ETS1.is_internal = TRUE; +ETV1.is_internal = TRUE; +EZH2.is_internal = TRUE; +E_cadherin.is_internal = TRUE; +FADD.is_internal = TRUE; +FGF.is_internal = TRUE; +FGFR3.is_internal = TRUE; +FOS.is_internal = TRUE; +FOXA1.is_internal = TRUE; +FOXO.is_internal = TRUE; +FRS2.is_internal = TRUE; +fused_event.is_internal = TRUE; +GADD45.is_internal = TRUE; +GLI.is_internal = TRUE; +GLUT1.is_internal = TRUE; +Glycolysis.is_internal = FALSE; +GSH.is_internal = TRUE; +GSK3.is_internal = TRUE; +HIF1.is_internal = TRUE; +HSPs.is_internal = TRUE; +Hypermethylation.is_internal = TRUE; +Hypoxia.is_internal = TRUE; +IDH1.is_internal = TRUE; +IKK.is_internal = TRUE; +Invasion.is_internal = FALSE; +JNK.is_internal = TRUE; +JUN.is_internal = TRUE; +Lactic_acid.is_internal = TRUE; +LDHA.is_internal = TRUE; +MAP3K1_3.is_internal = TRUE; +MDM2.is_internal = TRUE; +MED12.is_internal = TRUE; +MEK1_2.is_internal = TRUE; +Metastasis.is_internal = FALSE; +Migration.is_internal = FALSE; +mTORC1.is_internal = TRUE; +mTORC2.is_internal = TRUE; +MXI1.is_internal = TRUE; +MYC.is_internal = TRUE; +MYC_MAX.is_internal = TRUE; +NCOA3.is_internal = TRUE; +NCOR1.is_internal = TRUE; +NCOR2.is_internal = TRUE; +NF1.is_internal = TRUE; +NF_kB.is_internal = TRUE; +NKX3_1.is_internal = TRUE; +Nutrients.is_internal = TRUE; +p14ARF.is_internal = TRUE; +p15.is_internal = TRUE; +p21.is_internal = TRUE; +p38.is_internal = TRUE; +p53.is_internal = TRUE; +p70S6kab.is_internal = TRUE; +p90RSK.is_internal = TRUE; +PDK1.is_internal = TRUE; +PHDs.is_internal = TRUE; +PI3K.is_internal = TRUE; +PIP3.is_internal = TRUE; +PKC.is_internal = TRUE; +Proliferation.is_internal = FALSE; +PTCH1.is_internal = TRUE; +PTEN.is_internal = TRUE; +Quiescence.is_internal = FALSE; +RAF.is_internal = TRUE; +RAGS.is_internal = TRUE; +RAS.is_internal = TRUE; +RB1.is_internal = TRUE; +Rheb.is_internal = TRUE; +ROS.is_internal = TRUE; +RTK.is_internal = TRUE; +RUNX2.is_internal = TRUE; +SHH.is_internal = TRUE; +Slug.is_internal = TRUE; +SMAD.is_internal = TRUE; +SMO.is_internal = TRUE; +Snail.is_internal = TRUE; +SPOP.is_internal = TRUE; +TAK1.is_internal = TRUE; +TCF.is_internal = TRUE; +TERT.is_internal = TRUE; +TGFb.is_internal = TRUE; +TGFBR.is_internal = TRUE; +TNFalpha.is_internal = TRUE; +TSC1_2.is_internal = TRUE; +TWIST1.is_internal = TRUE; +VEGF.is_internal = TRUE; +VHL.is_internal = TRUE; +WNT.is_internal = TRUE; +YWHAZ.is_internal = TRUE; +ZBTB17.is_internal = TRUE; + +//LNCAP initial conditions: +[Androgen].istate = 0 [1], 1 [0]; +[Acidosis].istate = 0 [1], 1 [0]; +[Carcinogen].istate = 0 [1], 1 [0]; +[Hypoxia].istate = 0 [1], 1 [0]; +[TGFb].istate = 0 [1], 1 [0]; +[TNFalpha].istate = 0 [1], 1 [0]; +[HSPs].istate = 0 [1], 1 [0]; + +//Pro-proliferative conditions: +[EGF].istate = 1 [1], 0 [0]; +[FGF].istate = 1 [1], 0 [0]; +[Nutrients].istate = 1 [1] , 0 [0]; +[Caspase8].istate = 0[1], 1[0]; +[Caspase9].istate = 0[1], 1[0]; +[Caspase3].istate = 0[1], 1[0]; +[FADD].istate = 0[1], 1[0]; +[CytoC].istate = 0[1], 1[0]; +[APAF1].istate = 0[1], 1[0]; +[Bak].istate = 0[1], 1[0]; + + +//orphan nodes initial conditions: +[fused_event].istate= 1 [0] , 0 [1]; +[AR_ERG].istate= 1 [0] , 0 [1]; + +//outputs: +[Angiogenesis].istate = 0 [1], 1 [0]; +[Apoptosis].istate = 0 [1], 1 [0]; +[DNA_Repair].istate = 0 [1], 1 [0]; +[Glycolysis].istate = 0 [1], 1 [0]; +[Hypermethylation].istate = 0 [1], 1 [0]; +[Invasion].istate = 0 [1], 1 [0]; +[Metastasis].istate = 0 [1], 1 [0]; +[Migration].istate = 0 [1], 1 [0]; +[Proliferation].istate = 0 [1], 1 [0]; +[Quiescence].istate = 0 [1], 1 [0]; + +[AKT].istate = 0.51544[1], 0.48456[0]; +[AMP_ATP].istate = 0.20167[1], 0.79833[0]; +[ATR].istate = 0.32278[1], 0.6772199999999999[0]; +[AXIN1].istate = 0.38829[1], 0.61171[0]; +[BAD].istate = 0.65311[1], 0.34689000000000003[0]; +//[Bak].istate = 0.32278[1], 0.6772199999999999[0]; +[Bcl_XL].istate = 0.36264[1], 0.6373599999999999[0]; +[BCL2].istate = 1e-05[1], 0.99999[0]; +[BIRC5].istate = 0.34426[1], 0.65574[0]; +[BRCA1].istate = 0.42294[1], 0.57706[0]; +//[Caspase8].istate = 0.21981[1], 0.7801899999999999[0]; +//[Caspase9].istate = 0.32278[1], 0.6772199999999999[0]; +[CDH2].istate = 0.0[1], 1.0[0]; +[cFLAR].istate = 0.5[1], 0.5[0]; +[CyclinB].istate = 0.23353[1], 0.76647[0]; +[CyclinD].istate = 0.46539[1], 0.53461[0]; +//[CytoC].istate = 0.41909[1], 0.58091[0]; +[DAXX].istate = 0.46681[1], 0.53319[0]; +[E_cadherin].istate = 1.0[1], 0.0[0]; +[E2F1].istate = 0.40885[1], 0.5911500000000001[0]; +[eEF2].istate = 0.83625[1], 0.16374999999999995[0]; +[eEF2K].istate = 0.67722[1], 0.32277999999999996[0]; +[EGFR].istate = 0.98524[1], 0.014759999999999995[0]; +[EP300].istate = 0.55068[1], 0.44932000000000005[0]; +[ERG].istate = 0.0[1], 1.0[0]; +[ERK].istate = 0.54142[1], 0.45858[0]; +[ETS1].istate = 0.12035[1], 0.87965[0]; +[ETV1].istate = 1.0[1], 0.0[0]; +[EZH2].istate = 0.35125[1], 0.6487499999999999[0]; +//[FADD].istate = 0.28538[1], 0.71462[0]; +[FGFR3].istate = 0.00457[1], 0.99543[0]; +[FOS].istate = 0.00892[1], 0.99108[0]; +[FOXA1].istate = 1.0[1], 0.0[0]; +[FOXO].istate = 0.46259[1], 0.5374099999999999[0]; +[FRS2].istate = 0.5[1], 0.5[0]; +[GADD45].istate = 0.43898[1], 0.5610200000000001[0]; +[GLI].istate = 0.0[1], 1.0[0]; +[GLUT1].istate = 0.39629[1], 0.60371[0]; +[GSH].istate = 0.88557[1], 0.11443000000000003[0]; +[GSK3].istate = 0.39173[1], 0.60827[0]; +[HIF1].istate = 0.42511[1], 0.57489[0]; +//[HSPs].istate = 0.50492[1], 0.49507999999999996[0]; +[IDH1].istate = 0.84613[1], 0.15386999999999995[0]; +[IKK].istate = 0.41916[1], 0.58084[0]; +[JNK].istate = 0.4622[1], 0.5378000000000001[0]; +[JUN].istate = 0.36971[1], 0.63029[0]; +[Lactic_acid].istate = 0.69604[1], 0.30396[0]; +[LDHA].istate = 0.69604[1], 0.30396[0]; +[MAP3K1_3].istate = 0.33784[1], 0.6621600000000001[0]; +[MDM2].istate = 0.64216[1], 0.35784000000000005[0]; +[MED12].istate = 0.42473[1], 0.57527[0]; +[ZBTB17].istate = 0.1611[1], 0.8389[0]; +[mTORC1].istate = 0.50393[1], 0.49607[0]; +[mTORC2].istate = 0.55971[1], 0.44028999999999996[0]; +[MXI1].istate = 0.80578[1], 0.19421999999999995[0]; +[MYC].istate = 0.67065[1], 0.32935000000000003[0]; +[MYC_MAX].istate = 0.58532[1], 0.41468000000000005[0]; +[NCOR1].istate = 0.67722[1], 0.32277999999999996[0]; +[NCOR2].istate = 0.65311[1], 0.34689000000000003[0]; +[NF_kB].istate = 0.34467[1], 0.65533[0]; +[NKX3_1].istate = 1.0[1], 0.0[0]; +[p14ARF].istate = 0.04863[1], 0.95137[0]; +[p15].istate = 8e-05[1], 0.99992[0]; +[p21].istate = 0.75638[1], 0.24361999999999995[0]; +[p38].istate = 0.16139[1], 0.83861[0]; +[p53].istate = 0.66969[1], 0.33031[0]; +[p70S6kab].istate = 0.61821[1], 0.38178999999999996[0]; +[p90RSK].istate = 0.57137[1], 0.42862999999999996[0]; +[PDK1].istate = 0.85723[1], 0.14276999999999995[0]; +[PHDs].istate = 0.39307[1], 0.60693[0]; +[PI3K].istate = 0.4283[1], 0.5717[0]; +[PIP3].istate = 0.04553[1], 0.95447[0]; +[PTCH1].istate = 0.37971[1], 0.62029[0]; +[RAF].istate = 0.54773[1], 0.45226999999999995[0]; +[RAGS].istate = 0[1], 1[0]; +[RAS].istate = 0.41583[1], 0.5841700000000001[0]; +[RB1].istate = 0.27189[1], 0.72811[0]; +[Rheb].istate = 0.32278[1], 0.6772199999999999[0]; +[RTK].istate = 0.23541[1], 0.76459[0]; +[RUNX2].istate = 0.03411[1], 0.96589[0]; +[SHH].istate = 0.0[1], 1.0[0]; +[Slug].istate = 2e-05[1], 0.99998[0]; +[SMAD].istate = 0.37612[1], 0.62388[0]; +[SMO].istate = 0.99818[1], 0.0018200000000000438[0]; +[Snail].istate = 0.2181[1], 0.7819[0]; +[SPOP].istate = 0.64375[1], 0.35624999999999996[0]; +[TAK1].istate = 0.04553[1], 0.95447[0]; +[TCF].istate = 0.04774[1], 0.95226[0]; +[TERT].istate = 0.34569[1], 0.65431[0]; +//[TGFb].istate = 0.01451[1], 0.98549[0]; +[TGFBR].istate = 0.13603[1], 0.86397[0]; +//[TNFalpha].istate = 0.28155[1], 0.71845[0]; +[TSC1_2].istate = 0.74271[1], 0.25729[0]; +[TWIST1].istate = 0.08772[1], 0.91228[0]; +[VEGF].istate = 0.35294[1], 0.64706[0]; +[VHL].istate = 0.71326[1], 0.28674[0]; +[YWHAZ].istate = 0.16098[1], 0.83902[0]; +[AMPK].istate=0[1], 1[0]; +//[APAF1].istate=0[1], 1[0]; +//[AR].istate=0[1], 1[0]; +[ATM].istate=0[1], 1[0]; +[BAX].istate=0[1], 1[0]; +[beta_catenin].istate=1[1], 0[0]; +[BRCA2].istate=0[1], 1[0]; +[CHK1_2].istate=0[1], 1[0]; +[Dsh].istate=0[1], 1[0]; +//[FGF].istate=0[1], 1[0]; +[MEK1_2].istate=1[1], 0[0]; +[NCOA3].istate=0[1], 1[0]; +[NF1].istate=0[1], 1[0]; +[PKC].istate=0[1], 1[0]; +[PTEN].istate=0[1], 1[0]; +[WNT].istate=0[1], 1[0]; diff --git a/config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd b/config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd new file mode 100644 index 0000000..f9d9bc3 --- /dev/null +++ b/config/boolean_network/LNCaP_mut_RNA_00_all_nodes.bnd @@ -0,0 +1,978 @@ +Node PI3K +{ + logic = ((TGFBR | FRS2 | EGFR)) AND NOT anti_PI3K; + rate_up = @logic ? $u_PI3K : 0; + rate_down = @logic ? 0 : $d_PI3K; +} + + +Node HSPs +{ + logic = (((PKC | FOXA1 | Carcinogen) & !BRCA1 & !E2F1 & !AKT)) AND NOT anti_HSPs; + rate_up = @logic ? $u_HSPs : 0; + rate_down = @logic ? 0 : $d_HSPs; +} + + +Node Acidosis +{ + logic = (Acidosis); + rate_up = @logic ? $u_Acidosis : 0; + rate_down = @logic ? 0 : $d_Acidosis; +} + +Node AKT +{ + logic = ((HSPs | (PDK1 & PIP3) | PIP3 | (SHH & PIP3)) & !PTCH1); + rate_up = @logic ? $u_AKT : 0; + rate_down = @logic ? 0 : $d_AKT; +} + +Node AMPK +{ + logic = (ATR | HIF1 | AMP_ATP | ATM) & !FGFR3; + rate_up = @logic ? $u_AMPK : 0; + rate_down = @logic ? 0 : $d_AMPK; +} + +Node AMP_ATP +{ + logic = (!Nutrients); + rate_up = @logic ? $u_AMP_ATP : 0; + rate_down = @logic ? 0 : $d_AMP_ATP; +} + +Node Androgen +{ + logic = (Androgen); + rate_up = @logic ? $u_Androgen : 0; + rate_down = @logic ? 0 : $d_Androgen; +} + +Node Angiogenesis +{ + logic = (VEGF & (NF_kB | SMAD)); + rate_up = @logic ? $u_Angiogenesis : 0; + rate_down = @logic ? 0 : $d_Angiogenesis; +} + +Node APAF1 +{ + logic = ((Caspase8 | BAX | p53 | Bak | HSPs) & !Bcl_XL & !BCL2 & !AKT); + rate_up = @logic ? $u_APAF1 : 0; + rate_down = @logic ? 0 : $d_APAF1; +} + +Node Apoptosis +{ + logic = (Caspase9 | Caspase8 | Caspase3); + rate_up = @logic ? $u_Apoptosis : 0; + rate_down = @logic ? 0 : $d_Apoptosis; +} + +Node AR +{ + logic = ((GLI | EP300 | HSPs | NKX3_1 | EZH2 | NCOA3 | PKC | SMAD | Androgen) & !PTEN & !NCOR1 & !NCOR2 & !MDM2); + rate_up = @logic ? $u_AR : 0; + rate_down = @logic ? 0 : $d_AR; +} + +Node AR_ERG +{ + logic = ((AR & fused_event) | (AR & fused_event & !NKX3_1)); + rate_up = @logic ? $u_AR_ERG : 0; + rate_down = @logic ? 0 : $d_AR_ERG; +} + +Node ATM +{ + logic = (DNA_Damage); + rate_up = @logic ? $u_ATM : 0; + rate_down = @logic ? 0 : $d_ATM; +} + +Node ATR +{ + logic = (DNA_Damage); + rate_up = @logic ? $u_ATR : 0; + rate_down = @logic ? 0 : $d_ATR; +} + +Node AXIN1 +{ + logic = (GSK3); + rate_up = @logic ? $u_AXIN1 : 0; + rate_down = @logic ? 0 : $d_AXIN1; +} + +Node BAD +{ + logic = (!AKT & !HIF1 & !p90RSK & !RAF & !YWHAZ); + rate_up = @logic ? $u_BAD : 0; + rate_down = @logic ? 0 : $d_BAD; +} + +Node Bak +{ + logic = (Caspase8); + rate_up = @logic ? $u_Bak : 0; + rate_down = @logic ? 0 : $d_Bak; +} + +Node BAX +{ + logic = (!BCL2 & !HIF1 & !YWHAZ & (JNK | HIF1 | p53) & !ETS1); + rate_up = @logic ? $u_BAX : 0; + rate_down = @logic ? 0 : $d_BAX; +} + +Node BCL2 +{ + logic = ((HSPs | NF_kB | RUNX2) & !BAX & !p53 & !DAXX & !BAD); + rate_up = @logic ? $u_BCL2 : 0; + rate_down = @logic ? 0 : $d_BCL2; +} + +Node Bcl_XL +{ + logic = (AR & !BAD & !p53); + rate_up = @logic ? $u_Bcl_XL : 0; + rate_down = @logic ? 0 : $d_Bcl_XL; +} + +Node beta_catenin +{ + logic = (!GSK3 & !p53 & !AXIN1 & (EZH2 | YWHAZ)); + rate_up = @logic ? $u_beta_catenin : 0; + rate_down = @logic ? 0 : $d_beta_catenin; +} + +Node BIRC5 +{ + logic = (HSPs & !p53); + rate_up = @logic ? $u_BIRC5 : 0; + rate_down = @logic ? 0 : $d_BIRC5; +} + +Node BMP2 +{ + logic = (GLI & (TGFBR | FGFR3)); + rate_up = @logic ? $u_BMP2 : 0; + rate_down = @logic ? 0 : $d_BMP2; +} + +Node BRCA1 +{ + logic = ((E2F1 | ATM | ATR | (E2F1 & EP300) | (ATM & EP300) | (ATR & EP300)) & !CyclinD & !Bak); + rate_up = @logic ? $u_BRCA1 : 0; + rate_down = @logic ? 0 : $d_BRCA1; +} + +Node BRCA2 +{ + logic = (ATM & ATR & EP300); + rate_up = @logic ? $u_BRCA2 : 0; + rate_down = @logic ? 0 : $d_BRCA2; +} + +Node Carcinogen +{ + logic = (Carcinogen); + rate_up = @logic ? $u_Carcinogen : 0; + rate_down = @logic ? 0 : $d_Carcinogen; +} + +Node Caspase3 +{ + logic = (Caspase8 | Caspase9); + rate_up = @logic ? $u_Caspase3 : 0; + rate_down = @logic ? 0 : $d_Caspase3; +} + +Node Caspase8 +{ + logic = (!cFLAR & FADD); + rate_up = @logic ? $u_Caspase8 : 0; + rate_down = @logic ? 0 : $d_Caspase8; +} + +Node Caspase9 +{ + logic = ((!AKT & CytoC & APAF1 & PTCH1) | (!AKT & BIRC5 & CytoC & APAF1) | (!AKT & BIRC5 & CytoC & APAF1 & PTCH1)); + rate_up = @logic ? $u_Caspase9 : 0; + rate_down = @logic ? 0 : $d_Caspase9; +} + +Node CDH2 +{ + logic = (TWIST1); + rate_up = @logic ? $u_CDH2 : 0; + rate_down = @logic ? 0 : $d_CDH2; +} + +Node cFLAR +{ + logic = ((AKT | AR) & !JNK); + rate_up = @logic ? $u_cFLAR : 0; + rate_down = @logic ? 0 : $d_cFLAR; +} + +Node CHK1_2 +{ + logic = (ATM & ATR & BRCA1); + rate_up = @logic ? $u_CHK1_2 : 0; + rate_down = @logic ? 0 : $d_CHK1_2; +} + +Node COX4I2 +{ + logic = (HIF1); + rate_up = @logic ? $u_COX4I2 : 0; + rate_down = @logic ? 0 : $d_COX4I2; +} + +Node CyclinB +{ + logic = (E2F1 & !p21); + rate_up = @logic ? $u_CyclinB : 0; + rate_down = @logic ? 0 : $d_CyclinB; +} + +Node CyclinD +{ + logic = ((GLI | TCF | NF_kB) & MYC_MAX & !p21 & !FOXO & !p15 & !GSK3); + rate_up = @logic ? $u_CyclinD : 0; + rate_down = @logic ? 0 : $d_CyclinD; +} + +Node CytoC +{ + logic = ((Caspase8 | BAX | p53 | Bak) & !Bcl_XL & !BCL2 & !AKT); + rate_up = @logic ? $u_CytoC : 0; + rate_down = @logic ? 0 : $d_CytoC; +} + +Node DAXX +{ + logic = (!ATM & !ATR & !SPOP); + rate_up = @logic ? $u_DAXX : 0; + rate_down = @logic ? 0 : $d_DAXX; +} + +Node DNA_Damage +{ + logic = ((Carcinogen | (Carcinogen & ROS)) & !SPOP); + rate_up = @logic ? $u_DNA_Damage : 0; + rate_down = @logic ? 0 : $d_DNA_Damage; +} + +Node DNA_Repair +{ + logic = (ATM & ATR & (BRCA1 | BRCA2)); + rate_up = @logic ? $u_DNA_Repair : 0; + rate_down = @logic ? 0 : $d_DNA_Repair; +} + +Node Dsh +{ + logic = (WNT); + rate_up = @logic ? $u_Dsh : 0; + rate_down = @logic ? 0 : $d_Dsh; +} + +Node E2F1 +{ + logic = ((AR | (FOXA1 & AR) | (ATR & AR) | (ATM & AR)) & !RB1 & !MXI1); + rate_up = @logic ? $u_E2F1 : 0; + rate_down = @logic ? 0 : $d_E2F1; +} + +Node eEF2 +{ + logic = (!eEF2K); + rate_up = @logic ? $u_eEF2 : 0; + rate_down = @logic ? 0 : $d_eEF2; +} + +Node eEF2K +{ + logic = (p90RSK | p70S6kab); + rate_up = @logic ? $u_eEF2K : 0; + rate_down = @logic ? 0 : $d_eEF2K; +} + +Node EGF +{ + logic = (EGF); + rate_up = @logic ? $u_EGF : 0; + rate_down = @logic ? 0 : $d_EGF; +} + +Node EGFR +{ + logic = ((!TGFBR & !FRS2 & !EGFR & !PKC & (ERK | Androgen | EGF)) | (!TGFBR & !FRS2 & (Androgen | EGF))); + rate_up = @logic ? $u_EGFR : 0; + rate_down = @logic ? 0 : $d_EGFR; +} + +Node EMT +{ + logic = (CDH2 & !E_cadherin & !FOXA1); + rate_up = @logic ? $u_EMT : 0; + rate_down = @logic ? 0 : $d_EMT; +} + +Node EP300 +{ + logic = (AKT); + rate_up = @logic ? $u_EP300 : 0; + rate_down = @logic ? 0 : $d_EP300; +} + +Node ERG +{ + logic = (ERK); + rate_up = @logic ? $u_ERG : 0; + rate_down = @logic ? 0 : $d_ERG; +} + +Node ERK +{ + logic = (RAF | MEK1_2); + rate_up = @logic ? $u_ERK : 0; + rate_down = @logic ? 0 : $d_ERK; +} + +Node ETS1 +{ + logic = (!p53 & (RTK | ERK)); + rate_up = @logic ? $u_ETS1 : 0; + rate_down = @logic ? 0 : $d_ETS1; +} + +Node ETV1 +{ + logic = (ETS1 | MEK1_2); + rate_up = @logic ? $u_ETV1 : 0; + rate_down = @logic ? 0 : $d_ETV1; +} + +Node EZH2 +{ + logic = (AKT & E2F1 & (ERG | AR_ERG)); + rate_up = @logic ? $u_EZH2 : 0; + rate_down = @logic ? 0 : $d_EZH2; +} + +Node E_cadherin +{ + logic = ((beta_catenin | AR_ERG) & !Snail & !ERG & !NF_kB & !Slug); + rate_up = @logic ? $u_E_cadherin : 0; + rate_down = @logic ? 0 : $d_E_cadherin; +} + +Node FADD +{ + logic = (PTCH1 | TNFalpha) & !SHH & !MAP3K1_3; + rate_up = @logic ? $u_FADD : 0; + rate_down = @logic ? 0 : $d_FADD; +} + +Node FGF +{ + logic = (FGF); + rate_up = @logic ? $u_FGF : 0; + rate_down = @logic ? 0 : $d_FGF; +} + +Node FGFR3 +{ + logic = ((FGF | (FGF & RUNX2)) & !FRS2 & !PKC & !TGFBR); + rate_up = @logic ? $u_FGFR3 : 0; + rate_down = @logic ? 0 : $d_FGFR3; +} + +Node FOS +{ + logic = (ERK | ETS1 | AR); + rate_up = @logic ? $u_FOS : 0; + rate_down = @logic ? 0 : $d_FOS; +} + +Node FOXA1 +{ + logic = (FOXO); + rate_up = @logic ? $u_FOXA1 : 0; + rate_down = @logic ? 0 : $d_FOXA1; +} + +Node FOXO +{ + logic = (MAP3K1_3 & JNK & !AKT); + rate_up = @logic ? $u_FOXO : 0; + rate_down = @logic ? 0 : $d_FOXO; +} + +Node FRS2 +{ + logic = (!ERK & !EGFR & FGFR3 & !FRS2 & !TGFBR); + rate_up = @logic ? $u_FRS2 : 0; + rate_down = @logic ? 0 : $d_FRS2; +} + +Node fused_event +{ + logic = (fused_event); + rate_up = @logic ? $u_fused_event : 0; + rate_down = @logic ? 0 : $d_fused_event; +} + +Node GADD45 +{ + logic = (SMAD | p53); + rate_up = @logic ? $u_GADD45 : 0; + rate_down = @logic ? 0 : $d_GADD45; +} + +Node GLI +{ + logic = ((WNT | SMO) & !SPOP); + rate_up = @logic ? $u_GLI : 0; + rate_down = @logic ? 0 : $d_GLI; +} + +Node GLUT1 +{ + logic = ((HIF1 | MYC_MAX | AKT) & Nutrients); + rate_up = @logic ? $u_GLUT1 : 0; + rate_down = @logic ? 0 : $d_GLUT1; +} + +Node Glycolysis +{ + logic = (GLUT1 & Lactic_acid); + rate_up = @logic ? $u_Glycolysis : 0; + rate_down = @logic ? 0 : $d_Glycolysis; +} + +Node GSH +{ + logic = (MYC_MAX | NF_kB | p21); + rate_up = @logic ? $u_GSH : 0; + rate_down = @logic ? 0 : $d_GSH; +} + +Node GSK3 +{ + logic = (!AKT & !Dsh & MEK1_2 & !mTORC2 & !p90RSK); + rate_up = @logic ? $u_GSK3 : 0; + rate_down = @logic ? 0 : $d_GSK3; +} + +Node HIF1 +{ + logic = (!PHDs & !VHL & !p53 & !FOXO & (HSPs | mTORC1 | mTORC2 | Hypoxia | MYC_MAX)); + rate_up = @logic ? $u_HIF1 : 0; + rate_down = @logic ? 0 : $d_HIF1; +} + +Node Hypermethylation +{ + logic = (!IDH1); + rate_up = @logic ? $u_Hypermethylation : 0; + rate_down = @logic ? 0 : $d_Hypermethylation; +} + +Node Hypoxia +{ + logic = (Hypoxia); + rate_up = @logic ? $u_Hypoxia : 0; + rate_down = @logic ? 0 : $d_Hypoxia; +} + +Node IDH1 +{ + logic = (SMAD | FOXA1 | mTORC2 | ((SMAD | mTORC2 | FOXA1) & MEK1_2)); + rate_up = @logic ? $u_IDH1 : 0; + rate_down = @logic ? 0 : $d_IDH1; +} + +Node IKK +{ + logic = ((mTORC2 | AKT) & (PKC | TAK1 | ETS1) & !PHDs & !p53); + rate_up = @logic ? $u_IKK : 0; + rate_down = @logic ? 0 : $d_IKK; +} + +Node Invasion +{ + logic = ((CDH2 & SMAD) | (CDH2 & SMAD & EZH2) | beta_catenin); + rate_up = @logic ? $u_Invasion : 0; + rate_down = @logic ? 0 : $d_Invasion; +} + +Node JNK +{ + logic = ((ATM | GADD45 | TAK1 | MAP3K1_3) & !ERK & !p38); + rate_up = @logic ? $u_JNK : 0; + rate_down = @logic ? 0 : $d_JNK; +} + +Node JUN +{ + logic = (AR | ETS1 | JNK); + rate_up = @logic ? $u_JUN : 0; + rate_down = @logic ? 0 : $d_JUN; +} + +Node Lactic_acid +{ + logic = (LDHA); + rate_up = @logic ? $u_Lactic_acid : 0; + rate_down = @logic ? 0 : $d_Lactic_acid; +} + +Node LDHA +{ + logic = (HIF1 | MYC_MAX | IDH1); + rate_up = @logic ? $u_LDHA : 0; + rate_down = @logic ? 0 : $d_LDHA; +} + +Node MAP3K1_3 +{ + logic = (RAS & !(FOS | JUN | JNK | p38)); + rate_up = @logic ? $u_MAP3K1_3 : 0; + rate_down = @logic ? 0 : $d_MAP3K1_3; +} + + +Node MDM2 +{ + logic = (((AKT | p53) & DAXX & !p14ARF & !ATR & !ATM)); + rate_up = @logic ? $u_MDM2 : 0; + rate_down = @logic ? 0 : $d_MDM2; +} + +Node MED12 +{ + logic = (!GLI); + rate_up = @logic ? $u_MED12 : 0; + rate_down = @logic ? 0 : $d_MED12; +} + +Node MEK1_2 +{ + logic = (MAP3K1_3 | RAF | PDK1); + rate_up = @logic ? $u_MEK1_2 : 0; + rate_down = @logic ? 0 : $d_MEK1_2; +} + +Node Metastasis +{ + logic = (Migration & AR_ERG); + rate_up = @logic ? $u_Metastasis : 0; + rate_down = @logic ? 0 : $d_Metastasis; +} + +Node Migration +{ + logic = (((AKT & ERK) | (AKT & ETV1)) & Invasion & EMT); + rate_up = @logic ? $u_Migration : 0; + rate_down = @logic ? 0 : $d_Migration; +} + +Node mTORC1 +{ + logic = ((AKT | (AKT & Rheb)) & !AMPK & !TSC1_2); + rate_up = @logic ? $u_mTORC1 : 0; + rate_down = @logic ? 0 : $d_mTORC1; +} + +Node mTORC2 +{ + logic = ((!AMPK & RAGS & Rheb) | (AKT & !AMPK & Rheb) | (AKT & !AMPK & RAGS) | (AKT & RAGS & Rheb & !AMPK)); + rate_up = @logic ? $u_mTORC2 : 0; + rate_down = @logic ? 0 : $d_mTORC2; +} + +Node MXI1 +{ + logic = (HIF1); + rate_up = @logic ? $u_MXI1 : 0; + rate_down = @logic ? 0 : $d_MXI1; +} + +Node MYC +{ + logic = ((GLI | ERK | TCF | JUN | FOS | AR_ERG | ERG | NF_kB | SHH) & (mTORC1 | E2F1 | p38) & !HIF1); + rate_up = @logic ? $u_MYC : 0; + rate_down = @logic ? 0 : $d_MYC; +} + +Node MYC_MAX +{ + logic = (MYC & !MXI1 & !TGFBR & !SMAD); + rate_up = @logic ? $u_MYC_MAX : 0; + rate_down = @logic ? 0 : $d_MYC_MAX; +} + +Node NCOA3 +{ + logic = (!SPOP & p38); + rate_up = @logic ? $u_NCOA3 : 0; + rate_down = @logic ? 0 : $d_NCOA3; +} + +Node NCOR1 +{ + logic = (!AKT); + rate_up = @logic ? $u_NCOR1 : 0; + rate_down = @logic ? 0 : $d_NCOR1; +} + +Node NCOR2 +{ + logic = (!AKT & !FOXO); + rate_up = @logic ? $u_NCOR2 : 0; + rate_down = @logic ? 0 : $d_NCOR2; +} + +Node NF1 +{ + logic = (!PKC); + rate_up = @logic ? $u_NF1 : 0; + rate_down = @logic ? 0 : $d_NF1; +} + +Node NF_kB +{ + logic = ((E_cadherin | IKK | DNA_Damage) & (NCOA3 | PIP3 | Snail)); + rate_up = @logic ? $u_NF_kB : 0; + rate_down = @logic ? 0 : $d_NF_kB; +} + +Node NKX3_1 +{ + logic = (PKC & AR & !ERG & !AR_ERG); + rate_up = @logic ? $u_NKX3_1 : 0; + rate_down = @logic ? 0 : $d_NKX3_1; +} + +Node Nutrients +{ + logic = (Nutrients); + rate_up = @logic ? $u_Nutrients : 0; + rate_down = @logic ? 0 : $d_Nutrients; +} + +Node p14ARF +{ + logic = (E2F1 & MYC_MAX & RAS); + rate_up = @logic ? $u_p14ARF : 0; + rate_down = @logic ? 0 : $d_p14ARF; +} + +Node p15 +{ + logic = (ZBTB17); + rate_up = @logic ? $u_p15 : 0; + rate_down = @logic ? 0 : $d_p15; +} + +Node p21 +{ + logic = ((p53 | SMAD | HIF1 | ZBTB17) & !TERT & !MYC_MAX & !MDM2 & !AKT & !ERK); + rate_up = @logic ? $u_p21 : 0; + rate_down = @logic ? 0 : $d_p21; +} + +Node p38 +{ + logic = (MAP3K1_3 & !ERK & !GADD45); + rate_up = @logic ? $u_p38 : 0; + rate_down = @logic ? 0 : $d_p38; +} + +Node p53 +{ + logic = ((Acidosis | CHK1_2 | p38 | HIF1) & !BCL2 & !MDM2 & !HSPs & !Snail); + rate_up = @logic ? $u_p53 : 0; + rate_down = @logic ? 0 : $d_p53; +} + +Node p70S6kab +{ + logic = (mTORC2 | PDK1); + rate_up = @logic ? $u_p70S6kab : 0; + rate_down = @logic ? 0 : $d_p70S6kab; +} + +Node p90RSK +{ + logic = (PDK1 | ERK); + rate_up = @logic ? $u_p90RSK : 0; + rate_down = @logic ? 0 : $d_p90RSK; +} + +Node PDK1 +{ + logic = (HIF1 | MYC_MAX | PIP3); + rate_up = @logic ? $u_PDK1 : 0; + rate_down = @logic ? 0 : $d_PDK1; +} + +Node PHDs +{ + logic = (!Hypoxia & ROS); + rate_up = @logic ? $u_PHDs : 0; + rate_down = @logic ? 0 : $d_PHDs; +} + +Node PIP3 +{ + logic = ((!p53 & PI3K & !PTEN) | (!p53 & PI3K)); + rate_up = @logic ? $u_PIP3 : 0; + rate_down = @logic ? 0 : $d_PIP3; +} + +Node PKC +{ + logic = (RTK & WNT); + rate_up = @logic ? $u_PKC : 0; + rate_down = @logic ? 0 : $d_PKC; +} + +Node Proliferation +{ + logic = (CyclinB | CyclinD); + rate_up = @logic ? $u_Proliferation : 0; + rate_down = @logic ? 0 : $d_Proliferation; +} + +Node PTCH1 +{ + logic = (!SHH & GLI); + rate_up = @logic ? $u_PTCH1 : 0; + rate_down = @logic ? 0 : $d_PTCH1; +} + +Node PTEN +{ + logic = ((!NF_kB & !Snail & FGFR3 & p53) | (!NF_kB & !Snail & p53)); + rate_up = @logic ? $u_PTEN : 0; + rate_down = @logic ? 0 : $d_PTEN; +} + +Node Quiescence +{ + logic = (!Apoptosis & !Proliferation); + rate_up = @logic ? $u_Quiescence : 0; + rate_down = @logic ? 0 : $d_Quiescence; +} + +Node RAF +{ + logic = (!EZH2 & RAS) | (EZH2); + rate_up = @logic ? $u_RAF : 0; + rate_down = @logic ? 0 : $d_RAF; +} + +Node RAGS +{ + logic = (!Hypoxia & Nutrients); + rate_up = @logic ? $u_RAGS : 0; + rate_down = @logic ? 0 : $d_RAGS; +} + +Node RAS +{ + logic = (EGFR | TGFBR | FRS2 | FGFR3); + rate_up = @logic ? $u_RAS : 0; + rate_down = @logic ? 0 : $d_RAS; +} + +Node RB1 +{ + logic = (!CyclinB & !CyclinD & CHK1_2 & !p53 & !AMPK); + rate_up = @logic ? $u_RB1 : 0; + rate_down = @logic ? 0 : $d_RB1; +} + +Node Rheb +{ + logic = (!TSC1_2); + rate_up = @logic ? $u_Rheb : 0; + rate_down = @logic ? 0 : $d_Rheb; +} + +Node ROS +{ + logic = ((COX4I2 | Hypoxia) & !GSH); + rate_up = @logic ? $u_ROS : 0; + rate_down = @logic ? 0 : $d_ROS; +} + +Node RTK +{ + logic = (EGFR | FGFR3); + rate_up = @logic ? $u_RTK : 0; + rate_down = @logic ? 0 : $d_RTK; +} + +Node RUNX2 +{ + logic = (!ETS1 & !FOXO & Slug) | (ETS1 & !FOXO); + rate_up = @logic ? $u_RUNX2 : 0; + rate_down = @logic ? 0 : $d_RUNX2; +} + +Node SHH +{ + logic = (!BMP2 & !ATR); + rate_up = @logic ? $u_SHH : 0; + rate_down = @logic ? 0 : $d_SHH; +} + +Node Slug +{ + logic = ((TCF | NF_kB) & !DAXX & !p53 & !MDM2); + rate_up = @logic ? $u_Slug : 0; + rate_down = @logic ? 0 : $d_Slug; +} + +Node SMAD +{ + logic = ((TNFalpha | TGFBR) & !AR); + rate_up = @logic ? $u_SMAD : 0; + rate_down = @logic ? 0 : $d_SMAD; +} + +Node SMO +{ + logic = (!PTCH1 & SHH); + rate_up = @logic ? $u_SMO : 0; + rate_down = @logic ? 0 : $d_SMO; +} + +Node Snail +{ + logic = ((GLI | NF_kB | SMAD | beta_catenin) & !GSK3 & !p53); + rate_up = @logic ? $u_Snail : 0; + rate_down = @logic ? 0 : $d_Snail; +} + +Node SPOP +{ + logic = (SPOP); + rate_up = @logic ? $u_SPOP : 0; + rate_down = @logic ? 0 : $d_SPOP; +} + +Node TAK1 +{ + logic = (TGFBR | TNFalpha); + rate_up = @logic ? $u_TAK1 : 0; + rate_down = @logic ? 0 : $d_TAK1; +} + +Node TCF +{ + logic = ((beta_catenin | AR_ERG | ERG) & !TAK1); + rate_up = @logic ? $u_TCF : 0; + rate_down = @logic ? 0 : $d_TCF; +} + +Node TERT +{ + logic = ((GLI | AKT | HIF1 | MYC_MAX | NF1 | NF_kB | MYC_MAX) & !ZBTB17 & !p53 & !eEF2 & !SMAD); + rate_up = @logic ? $u_TERT : 0; + rate_down = @logic ? 0 : $d_TERT; +} + +Node TGFb +{ + logic = (TGFb); + rate_up = @logic ? $u_TGFb : 0; + rate_down = @logic ? 0 : $d_TGFb; +} + +Node TGFBR +{ + logic = ((TGFb | (HIF1 & TGFb)) & !MED12); + rate_up = @logic ? $u_TGFBR : 0; + rate_down = @logic ? 0 : $d_TGFBR; +} + +Node TNFalpha +{ + logic = (TNFalpha); + rate_up = @logic ? $u_TNFalpha : 0; + rate_down = @logic ? 0 : $d_TNFalpha; +} + +Node TSC1_2 +{ + logic = ((AMPK | p53 | HIF1) & !AKT & !ERK & !RAF & !p90RSK); + rate_up = @logic ? $u_TSC1_2 : 0; + rate_down = @logic ? 0 : $d_TSC1_2; +} + +Node TWIST1 +{ + logic = (Snail | beta_catenin); + rate_up = @logic ? $u_TWIST1 : 0; + rate_down = @logic ? 0 : $d_TWIST1; +} + +Node VEGF +{ + logic = (HIF1 & MYC_MAX); + rate_up = @logic ? $u_VEGF : 0; + rate_down = @logic ? 0 : $d_VEGF; +} + +Node VHL +{ + logic = (!Hypoxia & !ROS); + rate_up = @logic ? $u_VHL : 0; + rate_down = @logic ? 0 : $d_VHL; +} + +Node WNT +{ + logic = ((((ERG | AR_ERG) & GLI) | ERG | AR_ERG) & !p53); + rate_up = @logic ? $u_WNT : 0; + rate_down = @logic ? 0 : $d_WNT; +} + +Node YWHAZ +{ + logic = (AR); + rate_up = @logic ? $u_YWHAZ : 0; + rate_down = @logic ? 0 : $d_YWHAZ; +} + +Node ZBTB17 +{ + logic = (!MYC_MAX & AR); + rate_up = @logic ? $u_ZBTB17 : 0; + rate_down = @logic ? 0 : $d_ZBTB17; +} + +Node anti_PI3K +{ + logic = (anti_PI3K); + rate_up = @logic ? 0 : 0; + rate_down = @logic ? 0 : 0; +} + + +Node anti_HSPs +{ + logic = (anti_HSPs); + rate_up = @logic ? 0 : 0; + rate_down = @logic ? 0 : 0; +} + + diff --git a/config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg b/config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg new file mode 100644 index 0000000..6981df9 --- /dev/null +++ b/config/boolean_network/LNCaP_mut_RNA_00_all_nodes.cfg @@ -0,0 +1,584 @@ +$u_Acidosis = 1; +$d_Acidosis = 1; +$u_AKT = 1.15285; +$d_AKT = 0.86742; +$u_AMP_ATP = 0.06407; +$d_AMP_ATP = 15.60793; +$u_AMPK = 0; +$d_AMPK = 0.91263; +$u_Androgen = 1; +$d_Androgen = 1; +$u_Angiogenesis = 1; +$d_Angiogenesis = 1; +$u_Apoptosis = 1; +$d_Apoptosis = 1; +$u_AR = 100.0; +$d_AR = 0; +$u_AR_ERG = 1; +$d_AR_ERG = 1; +$u_ATM = 0; +$d_ATM = 5.81395; +$u_ATR = 0.1955; +$d_ATR = 5.11509; +$u_AXIN1 = 0.3574; +$d_AXIN1 = 2.79799; +$u_BAD = 4.09692; +$d_BAD = 0.24409; +$u_Bak = 0.1955; +$d_Bak = 5.11509; +$u_BAX = 0; +$d_BAX = 10.39933; +$u_BCL2 = 0.01; +$d_BCL2 = 100.0; +$u_Bcl_XL = 0.28221; +$d_Bcl_XL = 3.54346; +$u_beta_catenin = 0.60475; +$d_beta_catenin = 0; +$u_BIRC5 = 0.23826; +$d_BIRC5 = 4.1971; +$u_BMP2 = 1; +$d_BMP2 = 1; +$u_BRCA1 = 0.49177; +$d_BRCA1 = 2.03347; +$u_BRCA2 = 0; +$d_BRCA2 = 5.11509; +$u_Carcinogen = 1; +$d_Carcinogen = 1; +$u_Caspase3 = 1; +$d_Caspase3 = 1; +$u_Caspase8 = 0.07573; +$d_Caspase8 = 13.20481; +$u_Caspase9 = 0.1955; +$d_Caspase9 = 5.11509; +$u_CDH2 = 0.01; +$d_CDH2 = 100.0; +$u_cFLAR = 1.0; +$d_cFLAR = 1.0; +$u_CHK1_2 = 0; +$d_CHK1_2 = 0.74346; +$u_COX4I2 = 1; +$d_COX4I2 = 1; +$u_CyclinB = 0.08593; +$d_CyclinB = 11.63738; +$u_CyclinD = 0.72702; +$d_CyclinD = 1.37548; +$u_CytoC = 0.47463; +$d_CytoC = 2.1069; +$u_APAF1 = 0; +$d_APAF1 = 65.74622; +$u_DAXX = 0.73662; +$d_DAXX = 1.35755; +$u_DNA_Damage = 1; +$d_DNA_Damage = 1; +$u_DNA_Repair = 1; +$d_DNA_Repair = 1; +$u_Dsh = 0; +$d_Dsh = 0.40456; +$u_E2F1 = 0.43193; +$d_E2F1 = 2.31519; +$u_E_cadherin = 100.0; +$d_E_cadherin = 0.01; +$u_eEF2 = 22.1308; +$d_eEF2 = 0.04519; +$u_eEF2K = 5.11518; +$d_eEF2K = 0.1955; +$u_EMT = 1; +$d_EMT = 1; +$u_EP300 = 1.59488; +$d_EP300 = 0.62701; +$u_ERG = 0.01; +$d_ERG = 100.0; +$u_ERK = 1.46443; +$d_ERK = 0.68286; +$u_ETS1 = 0.0303; +$d_ETS1 = 33.0033; +$u_ETV1 = 99.99718; +$d_ETV1 = 0.01; +$u_EZH2 = 0.25411; +$d_EZH2 = 3.9353; +$u_FADD = 0.13852; +$d_FADD = 7.21917; +$u_FOS = 0.01086; +$d_FOS = 92.08103; +$u_JUN = 0.3012; +$d_JUN = 3.32005; +$u_FOXA1 = 99.99995; +$d_FOXA1 = 0.01; +$u_FOXO = 0.70855; +$d_FOXO = 1.41133; +$u_EGF = 8.51034; +$d_EGF = 0.1175; +$u_EGFR = 87.28647; +$d_EGFR = 0.01146; +$u_FGF = 0; +$d_FGF = 100.0; +$u_FGFR3 = 0.01043; +$d_FGFR3 = 95.87728; +$u_FRS2 = 1.0; +$d_FRS2 = 1.0; +$u_GADD45 = 0.57004; +$d_GADD45 = 1.75426; +$u_GLI = 0.01; +$d_GLI = 100.0; +$u_GLUT1 = 0.38474; +$d_GLUT1 = 2.59916; +$u_Glycolysis = 1; +$d_Glycolysis = 1; +$u_GSH = 34.85754; +$d_GSH = 0.02869; +$u_GSK3 = 0.36889; +$d_GSK3 = 2.71084; +$u_HIF1 = 0.50171; +$d_HIF1 = 1.99318; +$u_HSPs = 1.04632; +$d_HSPs = 0.95573; +$u_Hypermethylation = 1; +$d_Hypermethylation = 1; +$u_Hypoxia = 1; +$d_Hypoxia = 1; +$u_IDH1 = 24.23845; +$d_IDH1 = 0.04126; +$u_Invasion = 1; +$d_Invasion = 1; +$u_IKK = 0.47494; +$d_IKK = 2.10553; +$u_JNK = 0.70598; +$d_JNK = 1.41647; +$u_Lactic_acid = 6.08374; +$d_Lactic_acid = 0.16437; +$u_LDHA = 6.08374; +$d_LDHA = 0.16437; +$u_MAP3K1_3 = 0.22457; +$d_MAP3K1_3 = 4.45295; +$u_MDM2 = 3.70371; +$d_MDM2 = 0.27; +$u_MED12 = 0.49995; +$d_MED12 = 2.0002; +$u_MEK1_2 = 2.74199; +$d_MEK1_2 = 0; +$u_Metastasis = 1; +$d_Metastasis = 1; +$u_Migration = 1; +$d_Migration = 1; +$u_mTORC1 = 1.03682; +$d_mTORC1 = 0.96449; +$u_mTORC2 = 1.73313; +$d_mTORC2 = 0.57699; +$u_MXI1 = 16.71485; +$d_MXI1 = 0.05983; +$u_MYC = 4.81493; +$d_MYC = 0.20769; +$u_MYC_MAX = 2.19429; +$d_MYC_MAX = 0.45573; +$u_NCOA3 = 0; +$d_NCOA3 = 13.75138; +$u_NCOR1 = 5.11518; +$d_NCOR1 = 0.1955; +$u_NCOR2 = 4.09692; +$d_NCOR2 = 0.24409; +$u_NF1 = 0; +$d_NF1 = 1.0; +$u_NF_kB = 0.23915; +$d_NF_kB = 4.18148; +$u_NKX3_1 = 100.0; +$d_NKX3_1 = 0.01; +$u_Nutrients = 1; +$d_Nutrients = 1; +$u_p14ARF = 0.01565; +$d_p14ARF = 63.89776; +$u_p15 = 0.01001; +$d_p15 = 99.9001; +$u_p21 = 10.60511; +$d_p21 = 0.09429; +$u_p38 = 0.04421; +$d_p38 = 22.61932; +$u_p53 = 4.77257; +$d_p53 = 0.20953; +$u_p70S6kab = 2.97053; +$d_p70S6kab = 0.33664; +$u_p90RSK = 1.92967; +$d_p90RSK = 0.51822; +$u_PDK1 = 26.84952; +$d_PDK1 = 0.03724; +$u_PHDs = 0.37349; +$d_PHDs = 2.67745; +$u_PI3K = 0.51668; +$d_PI3K = 1.93543; +$u_PIP3 = 0.01521; +$d_PIP3 = 65.74622; +$u_PKC = 0; +$d_PKC = 2.5697; +$u_Proliferation = 1; +$d_Proliferation = 1; +$u_PTCH1 = 0.33025; +$d_PTCH1 = 3.02801; +$u_PTEN = 0; +$d_PTEN = 2.07753; +$u_Quiescence = 1; +$d_Quiescence = 1; +$u_RAF = 1.55217; +$d_RAF = 0.64426; +$u_RAGS = 0.01; +$d_RAGS = 100.0; +$u_RAS = 0.4606; +$d_RAS = 2.17108; +$u_RB1 = 0.12234; +$d_RB1 = 8.17394; +$u_Rheb = 0.1955; +$d_Rheb = 5.11509; +$u_ROS = 1; +$d_ROS = 1; +$u_RTK = 0.08743; +$d_RTK = 11.43772; +$u_RUNX2 = 0.01369; +$d_RUNX2 = 73.04602; +$u_SHH = 0.01; +$d_SHH = 100.0; +$u_Slug = 0.01; +$d_Slug = 100.0; +$u_SMAD = 0.31951; +$d_SMAD = 3.12979; +$u_SMO = 98.34156; +$d_SMO = 0.01017; +$u_Snail = 0.07454; +$d_Snail = 13.41562; +$u_SPOP = 3.75839; +$d_SPOP = 0.26607; +$u_TAK1 = 0.01521; +$d_TAK1 = 65.74622; +$u_TCF = 0.01552; +$d_TCF = 64.43299; +$u_TERT = 0.24141; +$d_TERT = 4.14233; +$u_TGFb = 0.01143; +$d_TGFb = 87.48906; +$u_TGFBR = 0.035; +$d_TGFBR = 28.57143; +$u_fused_event = 1; +$d_fused_event = 1; +$u_TNFalpha = 0.13372; +$d_TNFalpha = 7.47831; +$u_TSC1_2 = 9.35066; +$d_TSC1_2 = 0.10694; +$u_TWIST1 = 0.02243; +$d_TWIST1 = 44.58315; +$u_VEGF = 0.25808; +$d_VEGF = 3.87477; +$u_VHL = 7.12902; +$d_VHL = 0.14027; +$u_WNT = 0; +$d_WNT = 27.5786; +$u_YWHAZ = 0.04405; +$d_YWHAZ = 22.70148; +$u_ZBTB17 = 0.0441; +$d_ZBTB17 = 22.67574; + +discrete_time = 0; +use_physrandgen = FALSE; +seed_pseudorandom = 100; +sample_count = 1; +max_time = 1; //10 min: 1 in Maboss = 10 in Physicell +time_tick = 0.01; +thread_count = 1; +statdist_traj_count = 100; +statdist_cluster_threshold = 0.9; + + +Acidosis.is_internal = TRUE; +AKT.is_internal = TRUE; +AMPK.is_internal = TRUE; +AMP_ATP.is_internal = TRUE; +Androgen.is_internal = TRUE; +Angiogenesis.is_internal = FALSE; +APAF1.is_internal = TRUE; +Apoptosis.is_internal = FALSE; +AR.is_internal = TRUE; +AR_ERG.is_internal = TRUE; +ATM.is_internal = TRUE; +ATR.is_internal = TRUE; +AXIN1.is_internal = TRUE; +BAD.is_internal = TRUE; +Bak.is_internal = TRUE; +BAX.is_internal = TRUE; +BCL2.is_internal = TRUE; +Bcl_XL.is_internal = TRUE; +beta_catenin.is_internal = TRUE; +BIRC5.is_internal = TRUE; +BMP2.is_internal = TRUE; +BRCA1.is_internal = TRUE; +BRCA2.is_internal = TRUE; +Carcinogen.is_internal = TRUE; +Caspase3.is_internal = TRUE; +Caspase8.is_internal = TRUE; +Caspase9.is_internal = TRUE; +CDH2.is_internal = TRUE; +cFLAR.is_internal = TRUE; +CHK1_2.is_internal = TRUE; +COX4I2.is_internal = TRUE; +CyclinB.is_internal = TRUE; +CyclinD.is_internal = TRUE; +CytoC.is_internal = TRUE; +DAXX.is_internal = TRUE; +DNA_Damage.is_internal = TRUE; +DNA_Repair.is_internal = FALSE; +Dsh.is_internal = TRUE; +E2F1.is_internal = TRUE; +eEF2.is_internal = TRUE; +eEF2K.is_internal = TRUE; +EGF.is_internal = TRUE; +EGFR.is_internal = TRUE; +EMT.is_internal = TRUE; +EP300.is_internal = TRUE; +ERG.is_internal = TRUE; +ERK.is_internal = TRUE; +ETS1.is_internal = TRUE; +ETV1.is_internal = TRUE; +EZH2.is_internal = TRUE; +E_cadherin.is_internal = TRUE; +FADD.is_internal = TRUE; +FGF.is_internal = TRUE; +FGFR3.is_internal = TRUE; +FOS.is_internal = TRUE; +FOXA1.is_internal = TRUE; +FOXO.is_internal = TRUE; +FRS2.is_internal = TRUE; +fused_event.is_internal = TRUE; +GADD45.is_internal = TRUE; +GLI.is_internal = TRUE; +GLUT1.is_internal = TRUE; +Glycolysis.is_internal = FALSE; +GSH.is_internal = TRUE; +GSK3.is_internal = TRUE; +HIF1.is_internal = TRUE; +HSPs.is_internal = TRUE; +Hypermethylation.is_internal = TRUE; +Hypoxia.is_internal = TRUE; +IDH1.is_internal = TRUE; +IKK.is_internal = TRUE; +Invasion.is_internal = FALSE; +JNK.is_internal = TRUE; +JUN.is_internal = TRUE; +Lactic_acid.is_internal = TRUE; +LDHA.is_internal = TRUE; +MAP3K1_3.is_internal = TRUE; +MDM2.is_internal = TRUE; +MED12.is_internal = TRUE; +MEK1_2.is_internal = TRUE; +Metastasis.is_internal = FALSE; +Migration.is_internal = FALSE; +mTORC1.is_internal = TRUE; +mTORC2.is_internal = TRUE; +MXI1.is_internal = TRUE; +MYC.is_internal = TRUE; +MYC_MAX.is_internal = TRUE; +NCOA3.is_internal = TRUE; +NCOR1.is_internal = TRUE; +NCOR2.is_internal = TRUE; +NF1.is_internal = TRUE; +NF_kB.is_internal = TRUE; +NKX3_1.is_internal = TRUE; +Nutrients.is_internal = TRUE; +p14ARF.is_internal = TRUE; +p15.is_internal = TRUE; +p21.is_internal = TRUE; +p38.is_internal = TRUE; +p53.is_internal = TRUE; +p70S6kab.is_internal = TRUE; +p90RSK.is_internal = TRUE; +PDK1.is_internal = TRUE; +PHDs.is_internal = TRUE; +PI3K.is_internal = TRUE; +PIP3.is_internal = TRUE; +PKC.is_internal = TRUE; +Proliferation.is_internal = FALSE; +PTCH1.is_internal = TRUE; +PTEN.is_internal = TRUE; +Quiescence.is_internal = FALSE; +RAF.is_internal = TRUE; +RAGS.is_internal = TRUE; +RAS.is_internal = TRUE; +RB1.is_internal = TRUE; +Rheb.is_internal = TRUE; +ROS.is_internal = TRUE; +RTK.is_internal = TRUE; +RUNX2.is_internal = TRUE; +SHH.is_internal = TRUE; +Slug.is_internal = TRUE; +SMAD.is_internal = TRUE; +SMO.is_internal = TRUE; +Snail.is_internal = TRUE; +SPOP.is_internal = TRUE; +TAK1.is_internal = TRUE; +TCF.is_internal = TRUE; +TERT.is_internal = TRUE; +TGFb.is_internal = TRUE; +TGFBR.is_internal = TRUE; +TNFalpha.is_internal = TRUE; +TSC1_2.is_internal = TRUE; +TWIST1.is_internal = TRUE; +VEGF.is_internal = TRUE; +VHL.is_internal = TRUE; +WNT.is_internal = TRUE; +YWHAZ.is_internal = TRUE; +ZBTB17.is_internal = TRUE; + +//LNCAP initial conditions: +[Androgen].istate = 0 [1], 1 [0]; +[Acidosis].istate = 0 [1], 1 [0]; +[Carcinogen].istate = 0 [1], 1 [0]; +[Hypoxia].istate = 0 [1], 1 [0]; +[TGFb].istate = 0 [1], 1 [0]; +[TNFalpha].istate = 0 [1], 1 [0]; +[HSPs].istate = 0.5 [1], 0.5 [0]; +[anti_HSPs].istate = 0 [1], 1 [0]; + +//Pro-proliferative conditions: +[EGF].istate = 1 [1], 0 [0]; +[FGF].istate = 1 [1], 0 [0]; +[Nutrients].istate = 1 [1], 0 [0]; +[Caspase8].istate = 0 [1], 1 [0]; +[Caspase9].istate = 0 [1], 1 [0]; +[Caspase3].istate = 0 [1], 1 [0]; +[FADD].istate = 0 [1], 1 [0]; +[CytoC].istate = 0 [1], 1 [0]; +[APAF1].istate = 0 [1], 1 [0]; +[Bak].istate = 0 [1], 1 [0]; + + +//orphan nodes initial conditions: +[fused_event].istate = 0 [1], 1 [0]; +[AR_ERG].istate = 0 [1], 1 [0]; + +//outputs: +[Angiogenesis].istate = 0 [1], 1 [0]; +[Apoptosis].istate = 0 [1], 1 [0]; +[DNA_Repair].istate = 0 [1], 1 [0]; +[Glycolysis].istate = 0 [1], 1 [0]; +[Hypermethylation].istate = 0 [1], 1 [0]; +[Invasion].istate = 0 [1], 1 [0]; +[Metastasis].istate = 0 [1], 1 [0]; +[Migration].istate = 0 [1], 1 [0]; +[Proliferation].istate = 0 [1], 1 [0]; +[Quiescence].istate = 0 [1], 1 [0]; + +[AKT].istate = 0.51544 [1], 0.48456 [0]; +[AMP_ATP].istate = 0.20167 [1], 0.79833 [0]; +[ATR].istate = 0.32278 [1], 0.6772199999999999 [0]; +[AXIN1].istate = 0.38829 [1], 0.61171 [0]; +[BAD].istate = 0.65311 [1], 0.34689000000000003 [0]; +//[Bak].istate = 0.32278 [1], 0.6772199999999999 [0]; +[Bcl_XL].istate = 0.36264 [1], 0.6373599999999999 [0]; +[BCL2].istate = 1e-05 [1], 0.99999 [0]; +[BIRC5].istate = 0.34426 [1], 0.65574 [0]; +[BRCA1].istate = 0.42294 [1], 0.57706 [0]; +//[Caspase8].istate = 0.21981 [1], 0.7801899999999999 [0]; +//[Caspase9].istate = 0.32278 [1], 0.6772199999999999 [0]; +[CDH2].istate = 0.0 [1], 1.0 [0]; +[cFLAR].istate = 0.5 [1], 0.5 [0]; +[CyclinB].istate = 0.23353 [1], 0.76647 [0]; +[CyclinD].istate = 0.46539 [1], 0.53461 [0]; +//[CytoC].istate = 0.41909 [1], 0.58091 [0]; +[DAXX].istate = 0.46681 [1], 0.53319 [0]; +[E_cadherin].istate = 1.0 [1], 0.0 [0]; +[E2F1].istate = 0.40885 [1], 0.5911500000000001 [0]; +[eEF2].istate = 0.83625 [1], 0.16374999999999995 [0]; +[eEF2K].istate = 0.67722 [1], 0.32277999999999996 [0]; +[EGFR].istate = 0.98524 [1], 0.014759999999999995 [0]; +[EP300].istate = 0.55068 [1], 0.44932000000000005 [0]; +[ERG].istate = 0.0 [1], 1.0 [0]; +[ERK].istate = 0.54142 [1], 0.45858 [0]; +[ETS1].istate = 0.12035 [1], 0.87965 [0]; +[ETV1].istate = 1.0 [1], 0.0 [0]; +[EZH2].istate = 0.35125 [1], 0.6487499999999999 [0]; +//[FADD].istate = 0.28538 [1], 0.71462 [0]; +[FGFR3].istate = 0.00457 [1], 0.99543 [0]; +[FOS].istate = 0.00892 [1], 0.99108 [0]; +[FOXA1].istate = 1.0 [1], 0.0 [0]; +[FOXO].istate = 0.46259 [1], 0.5374099999999999 [0]; +[FRS2].istate = 0.5 [1], 0.5 [0]; +[GADD45].istate = 0.43898 [1], 0.5610200000000001 [0]; +[GLI].istate = 0.0 [1], 1.0 [0]; +[GLUT1].istate = 0.39629 [1], 0.60371 [0]; +[GSH].istate = 0.88557 [1], 0.11443000000000003 [0]; +[GSK3].istate = 0.39173 [1], 0.60827 [0]; +[HIF1].istate = 0.42511 [1], 0.57489 [0]; +//[HSPs].istate = 0.50492 [1], 0.49507999999999996 [0]; +[IDH1].istate = 0.84613 [1], 0.15386999999999995 [0]; +[IKK].istate = 0.41916 [1], 0.58084 [0]; +[JNK].istate = 0.4622 [1], 0.5378000000000001 [0]; +[JUN].istate = 0.36971 [1], 0.63029 [0]; +[Lactic_acid].istate = 0.69604 [1], 0.30396 [0]; +[LDHA].istate = 0.69604 [1], 0.30396 [0]; +[MAP3K1_3].istate = 0.33784 [1], 0.6621600000000001 [0]; +[MDM2].istate = 0.64216 [1], 0.35784000000000005 [0]; +[MED12].istate = 0.42473 [1], 0.57527 [0]; +[ZBTB17].istate = 0.1611 [1], 0.8389 [0]; +[mTORC1].istate = 0.50393 [1], 0.49607 [0]; +[mTORC2].istate = 0.55971 [1], 0.44028999999999996 [0]; +[MXI1].istate = 0.80578 [1], 0.19421999999999995 [0]; +[MYC].istate = 0.67065 [1], 0.32935000000000003 [0]; +[MYC_MAX].istate = 0.58532 [1], 0.41468000000000005 [0]; +[NCOR1].istate = 0.67722 [1], 0.32277999999999996 [0]; +[NCOR2].istate = 0.65311 [1], 0.34689000000000003 [0]; +[NF_kB].istate = 0.34467 [1], 0.65533 [0]; +[NKX3_1].istate = 1.0 [1], 0.0 [0]; +[p14ARF].istate = 0.04863 [1], 0.95137 [0]; +[p15].istate = 8e-05 [1], 0.99992 [0]; +[p21].istate = 0.75638 [1], 0.24361999999999995 [0]; +[p38].istate = 0.16139 [1], 0.83861 [0]; +[p53].istate = 0.66969 [1], 0.33031 [0]; +[p70S6kab].istate = 0.61821 [1], 0.38178999999999996 [0]; +[p90RSK].istate = 0.57137 [1], 0.42862999999999996 [0]; +[PDK1].istate = 0.85723 [1], 0.14276999999999995 [0]; +[PHDs].istate = 0.39307 [1], 0.60693 [0]; +[PI3K].istate = 0.5 [1], 0.5 [0]; +[anti_PI3K].istate = 0 [1], 1 [0]; +[PIP3].istate = 0.04553 [1], 0.95447 [0]; +[PTCH1].istate = 0.37971 [1], 0.62029 [0]; +[RAF].istate = 0.54773 [1], 0.45226999999999995 [0]; +[RAGS].istate = 0 [1], 1 [0]; +[RAS].istate = 0.41583 [1], 0.5841700000000001 [0]; +[RB1].istate = 0.27189 [1], 0.72811 [0]; +[Rheb].istate = 0.32278 [1], 0.6772199999999999 [0]; +[RTK].istate = 0.23541 [1], 0.76459 [0]; +[RUNX2].istate = 0.03411 [1], 0.96589 [0]; +[SHH].istate = 0.0 [1], 1.0 [0]; +[Slug].istate = 2e-05 [1], 0.99998 [0]; +[SMAD].istate = 0.37612 [1], 0.62388 [0]; +[SMO].istate = 0.99818 [1], 0.0018200000000000438 [0]; +[Snail].istate = 0.2181 [1], 0.7819 [0]; +[SPOP].istate = 0.64375 [1], 0.35624999999999996 [0]; +[TAK1].istate = 0.04553 [1], 0.95447 [0]; +[TCF].istate = 0.04774 [1], 0.95226 [0]; +[TERT].istate = 0.34569 [1], 0.65431 [0]; +//[TGFb].istate = 0.01451 [1], 0.98549 [0]; +[TGFBR].istate = 0.13603 [1], 0.86397 [0]; +//[TNFalpha].istate = 0.28155 [1], 0.71845 [0]; +[TSC1_2].istate = 0.74271 [1], 0.25729 [0]; +[TWIST1].istate = 0.08772 [1], 0.91228 [0]; +[VEGF].istate = 0.35294 [1], 0.64706 [0]; +[VHL].istate = 0.71326 [1], 0.28674 [0]; +[YWHAZ].istate = 0.16098 [1], 0.83902 [0]; +[AMPK].istate = 0 [1], 1 [0]; +//[APAF1].istate = 0 [1], 1 [0]; +//[AR].istate = 0 [1], 1 [0]; +[ATM].istate = 0 [1], 1 [0]; +[BAX].istate = 0 [1], 1 [0]; +[beta_catenin].istate = 1 [1], 0 [0]; +[BRCA2].istate = 0 [1], 1 [0]; +[CHK1_2].istate = 0 [1], 1 [0]; +[Dsh].istate = 0 [1], 1 [0]; +//[FGF].istate = 0 [1], 1 [0]; +[MEK1_2].istate = 1 [1], 0 [0]; +[NCOA3].istate = 0 [1], 1 [0]; +[NF1].istate = 0 [1], 1 [0]; +[PKC].istate = 0 [1], 1 [0]; +[PTEN].istate = 0 [1], 1 [0]; +[WNT].istate = 0 [1], 1 [0]; + +[AR].istate = 0.5 [0] , 0.5 [1]; +[BMP2].istate = 0.5 [0] , 0.5 [1]; +[COX4I2].istate = 0.5 [0] , 0.5 [1]; +[DNA_Damage].istate = 0.5 [0] , 0.5 [1]; +[EMT].istate = 0.5 [0] , 0.5 [1]; +[ROS].istate = 0.5 [0] , 0.5 [1]; diff --git a/config/boolean_network/Montagud2022_Prostate_Cancer.bnd b/config/boolean_network/Montagud2022_Prostate_Cancer.bnd new file mode 100644 index 0000000..5f51576 --- /dev/null +++ b/config/boolean_network/Montagud2022_Prostate_Cancer.bnd @@ -0,0 +1,803 @@ +Node Acidosis { + logic = (Acidosis); + rate_up = @logic ? $u_Acidosis : 0; + rate_down = @logic ? 0 : $d_Acidosis; +} + +Node AKT { + logic = ((HSPs | (PDK1 & PIP3) | PIP3 | (SHH & PIP3)) & !PTCH1); + rate_up = @logic ? $u_AKT : 0; + rate_down = @logic ? 0 : $d_AKT; +} + +Node AMPK { + logic = (ATR | HIF1 | AMP_ATP | ATM) & !FGFR3; + rate_up = @logic ? $u_AMPK : 0; + rate_down = @logic ? 0 : $d_AMPK; +} + +Node AMP_ATP { + logic = (!Nutrients); + rate_up = @logic ? $u_AMP_ATP : 0; + rate_down = @logic ? 0 : $d_AMP_ATP; +} + +Node Androgen { + logic = (Androgen); + rate_up = @logic ? $u_Androgen : 0; + rate_down = @logic ? 0 : $d_Androgen; +} + +Node APAF1 { + logic = ((Caspase8 | BAX | p53 | Bak | HSPs) & !Bcl_XL & !BCL2 & !AKT); + rate_up = @logic ? $u_APAF1 : 0; + rate_down = @logic ? 0 : $d_APAF1; +} + +Node Apoptosis { + logic = (Caspase9 | Caspase8 | Caspase3); + rate_up = @logic ? $u_Apoptosis : 0; + rate_down = @logic ? 0 : $d_Apoptosis; +} + +Node AR { + logic = ((GLI | EP300 | HSPs | NKX3_1 | EZH2 | NCOA3 | PKC | SMAD | Androgen) & !PTEN & !NCOR1 & !NCOR2 & !MDM2); + rate_up = @logic ? $u_AR : 0; + rate_down = @logic ? 0 : $d_AR; +} + +Node AR_ERG { + logic = ((AR & fused_event) | (AR & fused_event & !NKX3_1)); + rate_up = @logic ? $u_AR_ERG : 0; + rate_down = @logic ? 0 : $d_AR_ERG; +} + +Node ATM { + logic = (DNA_Damage); + rate_up = @logic ? $u_ATM : 0; + rate_down = @logic ? 0 : $d_ATM; +} + +Node ATR { + logic = (DNA_Damage); + rate_up = @logic ? $u_ATR : 0; + rate_down = @logic ? 0 : $d_ATR; +} + +Node AXIN1 { + logic = (GSK3); + rate_up = @logic ? $u_AXIN1 : 0; + rate_down = @logic ? 0 : $d_AXIN1; +} + +Node BAD { + logic = (!AKT & !HIF1 & !p90RSK & !RAF & !YWHAZ); + rate_up = @logic ? $u_BAD : 0; + rate_down = @logic ? 0 : $d_BAD; +} + +Node Bak { + logic = (Caspase8); + rate_up = @logic ? $u_Bak : 0; + rate_down = @logic ? 0 : $d_Bak; +} + +Node BAX { + logic = (!BCL2 & !HIF1 & !YWHAZ & (JNK | HIF1 | p53) & !ETS1); + rate_up = @logic ? $u_BAX : 0; + rate_down = @logic ? 0 : $d_BAX; +} + +Node BCL2 { + logic = ((HSPs | NF_kB | RUNX2) & !BAX & !p53 & !DAXX & !BAD); + rate_up = @logic ? $u_BCL2 : 0; + rate_down = @logic ? 0 : $d_BCL2; +} + +Node Bcl_XL { + logic = (AR & !BAD & !p53); + rate_up = @logic ? $u_Bcl_XL : 0; + rate_down = @logic ? 0 : $d_Bcl_XL; +} + +Node beta_catenin { + logic = (!GSK3 & !p53 & !AXIN1 & (EZH2 | YWHAZ)); + rate_up = @logic ? $u_beta_catenin : 0; + rate_down = @logic ? 0 : $d_beta_catenin; +} + +Node BIRC5 { + logic = (HSPs & !p53); + rate_up = @logic ? $u_BIRC5 : 0; + rate_down = @logic ? 0 : $d_BIRC5; +} + +Node BMP2 { + logic = (GLI & (TGFBR | FGFR3)); + rate_up = @logic ? $u_BMP2 : 0; + rate_down = @logic ? 0 : $d_BMP2; +} + +Node BRCA1 { + logic = ((E2F1 | ATM | ATR | (E2F1 & EP300) | (ATM & EP300) | (ATR & EP300)) & !CyclinD & !Bak); + rate_up = @logic ? $u_BRCA1 : 0; + rate_down = @logic ? 0 : $d_BRCA1; +} + +Node BRCA2 { + logic = (ATM & ATR & EP300); + rate_up = @logic ? $u_BRCA2 : 0; + rate_down = @logic ? 0 : $d_BRCA2; +} + +Node Carcinogen { + logic = (Carcinogen); + rate_up = @logic ? $u_Carcinogen : 0; + rate_down = @logic ? 0 : $d_Carcinogen; +} + +Node Caspase3 { + logic = (Caspase8 | Caspase9); + rate_up = @logic ? $u_Caspase3 : 0; + rate_down = @logic ? 0 : $d_Caspase3; +} + +Node Caspase8 { + logic = (!cFLAR & FADD); + rate_up = @logic ? $u_Caspase8 : 0; + rate_down = @logic ? 0 : $d_Caspase8; +} + +Node Caspase9 { + logic = ((!AKT & CytoC & APAF1 & PTCH1) | (!AKT & BIRC5 & CytoC & APAF1) | (!AKT & BIRC5 & CytoC & APAF1 & PTCH1)); + rate_up = @logic ? $u_Caspase9 : 0; + rate_down = @logic ? 0 : $d_Caspase9; +} + +Node CDH2 { + logic = (TWIST1); + rate_up = @logic ? $u_CDH2 : 0; + rate_down = @logic ? 0 : $d_CDH2; +} + +Node cFLAR { + logic = ((AKT | AR) & !JNK); + rate_up = @logic ? $u_cFLAR : 0; + rate_down = @logic ? 0 : $d_cFLAR; +} + +Node CHK1_2 { + logic = (ATM & ATR & BRCA1); + rate_up = @logic ? $u_CHK1_2 : 0; + rate_down = @logic ? 0 : $d_CHK1_2; +} + +Node COX4I2 { + logic = (HIF1); + rate_up = @logic ? $u_COX4I2 : 0; + rate_down = @logic ? 0 : $d_COX4I2; +} + +Node CyclinB { + logic = (E2F1 & !p21); + rate_up = @logic ? $u_CyclinB : 0; + rate_down = @logic ? 0 : $d_CyclinB; +} + +Node CyclinD { + logic = ((GLI | TCF | NF_kB) & MYC_MAX & !p21 & !FOXO & !p15 & !GSK3); + rate_up = @logic ? $u_CyclinD : 0; + rate_down = @logic ? 0 : $d_CyclinD; +} + +Node CytoC { + logic = ((Caspase8 | BAX | p53 | Bak) & !Bcl_XL & !BCL2 & !AKT); + rate_up = @logic ? $u_CytoC : 0; + rate_down = @logic ? 0 : $d_CytoC; +} + +Node DAXX { + logic = (!ATM & !ATR & !SPOP); + rate_up = @logic ? $u_DAXX : 0; + rate_down = @logic ? 0 : $d_DAXX; +} + +Node DNA_Damage { + logic = ((Carcinogen | (Carcinogen & ROS)) & !SPOP); + rate_up = @logic ? $u_DNA_Damage : 0; + rate_down = @logic ? 0 : $d_DNA_Damage; +} + +Node DNA_Repair { + logic = (ATM & ATR & (BRCA1 | BRCA2)); + rate_up = @logic ? $u_DNA_Repair : 0; + rate_down = @logic ? 0 : $d_DNA_Repair; +} + +Node Dsh { + logic = (WNT); + rate_up = @logic ? $u_Dsh : 0; + rate_down = @logic ? 0 : $d_Dsh; +} + +Node E2F1 { + logic = ((AR | (FOXA1 & AR) | (ATR & AR) | (ATM & AR)) & !RB1 & !MXI1); + rate_up = @logic ? $u_E2F1 : 0; + rate_down = @logic ? 0 : $d_E2F1; +} + +Node eEF2 { + logic = (!eEF2K); + rate_up = @logic ? $u_eEF2 : 0; + rate_down = @logic ? 0 : $d_eEF2; +} + +Node eEF2K { + logic = (p90RSK | p70S6kab); + rate_up = @logic ? $u_eEF2K : 0; + rate_down = @logic ? 0 : $d_eEF2K; +} + +Node EGF { + logic = (EGF); + rate_up = @logic ? $u_EGF : 0; + rate_down = @logic ? 0 : $d_EGF; +} + +Node EGFR { + logic = ((!TGFBR & !FRS2 & !EGFR & !PKC & (ERK | Androgen | EGF)) | (!TGFBR & !FRS2 & (Androgen | EGF))); + rate_up = @logic ? $u_EGFR : 0; + rate_down = @logic ? 0 : $d_EGFR; +} + +Node EMT { + logic = (CDH2 & !E_cadherin & !FOXA1); + rate_up = @logic ? $u_EMT : 0; + rate_down = @logic ? 0 : $d_EMT; +} + +Node EP300 { + logic = (AKT); + rate_up = @logic ? $u_EP300 : 0; + rate_down = @logic ? 0 : $d_EP300; +} + +Node ERG { + logic = (ERK); + rate_up = @logic ? $u_ERG : 0; + rate_down = @logic ? 0 : $d_ERG; +} + +Node ERK { + logic = (RAF | MEK1_2); + rate_up = @logic ? $u_ERK : 0; + rate_down = @logic ? 0 : $d_ERK; +} + +Node ETS1 { + logic = (!p53 & (RTK | ERK)); + rate_up = @logic ? $u_ETS1 : 0; + rate_down = @logic ? 0 : $d_ETS1; +} + +Node ETV1 { + logic = (ETS1 | MEK1_2); + rate_up = @logic ? $u_ETV1 : 0; + rate_down = @logic ? 0 : $d_ETV1; +} + +Node EZH2 { + logic = (AKT & E2F1 & (ERG | AR_ERG)); + rate_up = @logic ? $u_EZH2 : 0; + rate_down = @logic ? 0 : $d_EZH2; +} + +Node E_cadherin { + logic = ((beta_catenin | AR_ERG) & !Snail & !ERG & !NF_kB & !Slug); + rate_up = @logic ? $u_E_cadherin : 0; + rate_down = @logic ? 0 : $d_E_cadherin; +} + +Node FADD { + logic = (PTCH1 | TNFalpha) & !SHH & !MAP3K1_3; + rate_up = @logic ? $u_FADD : 0; + rate_down = @logic ? 0 : $d_FADD; +} + +Node FGF { + logic = (FGF); + rate_up = @logic ? $u_FGF : 0; + rate_down = @logic ? 0 : $d_FGF; +} + +Node FGFR3 { + logic = ((FGF | (FGF & RUNX2)) & !FRS2 & !PKC & !TGFBR); + rate_up = @logic ? $u_FGFR3 : 0; + rate_down = @logic ? 0 : $d_FGFR3; +} + +Node FOS { + logic = (ERK | ETS1 | AR); + rate_up = @logic ? $u_FOS : 0; + rate_down = @logic ? 0 : $d_FOS; +} + +Node FOXA1 { + logic = (FOXO); + rate_up = @logic ? $u_FOXA1 : 0; + rate_down = @logic ? 0 : $d_FOXA1; +} + +Node FOXO { + logic = (MAP3K1_3 & JNK & !AKT); + rate_up = @logic ? $u_FOXO : 0; + rate_down = @logic ? 0 : $d_FOXO; +} + +Node FRS2 { + logic = (!ERK & !EGFR & FGFR3 & !FRS2 & !TGFBR); + rate_up = @logic ? $u_FRS2 : 0; + rate_down = @logic ? 0 : $d_FRS2; +} + +Node fused_event { + logic = (fused_event); + rate_up = @logic ? $u_fused_event : 0; + rate_down = @logic ? 0 : $d_fused_event; +} + +Node GADD45 { + logic = (SMAD | p53); + rate_up = @logic ? $u_GADD45 : 0; + rate_down = @logic ? 0 : $d_GADD45; +} + +Node GLI { + logic = ((WNT | SMO) & !SPOP); + rate_up = @logic ? $u_GLI : 0; + rate_down = @logic ? 0 : $d_GLI; +} + +Node GLUT1 { + logic = ((HIF1 | MYC_MAX | AKT) & Nutrients); + rate_up = @logic ? $u_GLUT1 : 0; + rate_down = @logic ? 0 : $d_GLUT1; +} + + + +Node GSH { + logic = (MYC_MAX | NF_kB | p21); + rate_up = @logic ? $u_GSH : 0; + rate_down = @logic ? 0 : $d_GSH; +} + +Node GSK3 { + logic = (!AKT & !Dsh & MEK1_2 & !mTORC2 & !p90RSK); + rate_up = @logic ? $u_GSK3 : 0; + rate_down = @logic ? 0 : $d_GSK3; +} + +Node HIF1 { + logic = (!PHDs & !VHL & !p53 & !FOXO & (HSPs | mTORC1 | mTORC2 | Hypoxia | MYC_MAX)); + rate_up = @logic ? $u_HIF1 : 0; + rate_down = @logic ? 0 : $d_HIF1; +} + +Node HSPs { + logic = ((PKC | FOXA1 | Carcinogen) & !BRCA1 & !E2F1 & !AKT); + rate_up = @logic ? $u_HSPs : 0; + rate_down = @logic ? 0 : $d_HSPs; +} + + +Node Hypoxia { + logic = (Hypoxia); + rate_up = @logic ? $u_Hypoxia : 0; + rate_down = @logic ? 0 : $d_Hypoxia; +} + +Node IDH1 { + logic = (SMAD | FOXA1 | mTORC2 | ((SMAD | mTORC2 | FOXA1) & MEK1_2)); + rate_up = @logic ? $u_IDH1 : 0; + rate_down = @logic ? 0 : $d_IDH1; +} + +Node IKK { + logic = ((mTORC2 | AKT) & (PKC | TAK1 | ETS1) & !PHDs & !p53); + rate_up = @logic ? $u_IKK : 0; + rate_down = @logic ? 0 : $d_IKK; +} + +Node Invasion { + logic = ((CDH2 & SMAD) | (CDH2 & SMAD & EZH2) | beta_catenin); + rate_up = @logic ? $u_Invasion : 0; + rate_down = @logic ? 0 : $d_Invasion; +} + +Node JNK { + logic = ((ATM | GADD45 | TAK1 | MAP3K1_3) & !ERK & !p38); + rate_up = @logic ? $u_JNK : 0; + rate_down = @logic ? 0 : $d_JNK; +} + +Node JUN { + logic = (AR | ETS1 | JNK); + rate_up = @logic ? $u_JUN : 0; + rate_down = @logic ? 0 : $d_JUN; +} + +Node Lactic_acid { + logic = (LDHA); + rate_up = @logic ? $u_Lactic_acid : 0; + rate_down = @logic ? 0 : $d_Lactic_acid; +} + +Node LDHA { + logic = (HIF1 | MYC_MAX | IDH1); + rate_up = @logic ? $u_LDHA : 0; + rate_down = @logic ? 0 : $d_LDHA; +} + +Node MAP3K1_3 { + logic = (RAS & !(FOS | JUN | JNK | p38)); + rate_up = @logic ? $u_MAP3K1_3 : 0; + rate_down = @logic ? 0 : $d_MAP3K1_3; +} + + +Node MDM2 { + logic = (((AKT | p53) & DAXX & !p14ARF & !ATR & !ATM)); + rate_up = @logic ? $u_MDM2 : 0; + rate_down = @logic ? 0 : $d_MDM2; +} + +Node MED12 { + logic = (!GLI); + rate_up = @logic ? $u_MED12 : 0; + rate_down = @logic ? 0 : $d_MED12; +} + +Node MEK1_2 { + logic = (MAP3K1_3 | RAF | PDK1); + rate_up = @logic ? $u_MEK1_2 : 0; + rate_down = @logic ? 0 : $d_MEK1_2; +} + +Node Metastasis { + logic = (Migration & AR_ERG); + rate_up = @logic ? $u_Metastasis : 0; + rate_down = @logic ? 0 : $d_Metastasis; +} + +Node Migration { + logic = (((AKT & ERK) | (AKT & ETV1)) & Invasion & EMT); + rate_up = @logic ? $u_Migration : 0; + rate_down = @logic ? 0 : $d_Migration; +} + +Node mTORC1 { + logic = ((AKT | (AKT & Rheb)) & !AMPK & !TSC1_2); + rate_up = @logic ? $u_mTORC1 : 0; + rate_down = @logic ? 0 : $d_mTORC1; +} + +Node mTORC2 { + logic = ((!AMPK & RAGS & Rheb) | (AKT & !AMPK & Rheb) | (AKT & !AMPK & RAGS) | (AKT & RAGS & Rheb & !AMPK)); + rate_up = @logic ? $u_mTORC2 : 0; + rate_down = @logic ? 0 : $d_mTORC2; +} + +Node MXI1 { + logic = (HIF1); + rate_up = @logic ? $u_MXI1 : 0; + rate_down = @logic ? 0 : $d_MXI1; +} + +Node MYC { + logic = ((GLI | ERK | TCF | JUN | FOS | AR_ERG | ERG | NF_kB | SHH) & (mTORC1 | E2F1 | p38) & !HIF1); + rate_up = @logic ? $u_MYC : 0; + rate_down = @logic ? 0 : $d_MYC; +} + +Node MYC_MAX { + logic = (MYC & !MXI1 & !TGFBR & !SMAD); + rate_up = @logic ? $u_MYC_MAX : 0; + rate_down = @logic ? 0 : $d_MYC_MAX; +} + +Node NCOA3 { + logic = (!SPOP & p38); + rate_up = @logic ? $u_NCOA3 : 0; + rate_down = @logic ? 0 : $d_NCOA3; +} + +Node NCOR1 { + logic = (!AKT); + rate_up = @logic ? $u_NCOR1 : 0; + rate_down = @logic ? 0 : $d_NCOR1; +} + +Node NCOR2 { + logic = (!AKT & !FOXO); + rate_up = @logic ? $u_NCOR2 : 0; + rate_down = @logic ? 0 : $d_NCOR2; +} + +Node NF1 { + logic = (!PKC); + rate_up = @logic ? $u_NF1 : 0; + rate_down = @logic ? 0 : $d_NF1; +} + +Node NF_kB { + logic = ((E_cadherin | IKK | DNA_Damage) & (NCOA3 | PIP3 | Snail)); + rate_up = @logic ? $u_NF_kB : 0; + rate_down = @logic ? 0 : $d_NF_kB; +} + +Node NKX3_1 { + logic = (PKC & AR & !ERG & !AR_ERG); + rate_up = @logic ? $u_NKX3_1 : 0; + rate_down = @logic ? 0 : $d_NKX3_1; +} + +Node Nutrients { + logic = (Nutrients); + rate_up = @logic ? $u_Nutrients : 0; + rate_down = @logic ? 0 : $d_Nutrients; +} + +Node p14ARF { + logic = (E2F1 & MYC_MAX & RAS); + rate_up = @logic ? $u_p14ARF : 0; + rate_down = @logic ? 0 : $d_p14ARF; +} + +Node p15 { + logic = (ZBTB17); + rate_up = @logic ? $u_p15 : 0; + rate_down = @logic ? 0 : $d_p15; +} + +Node p21 { + logic = ((p53 | SMAD | HIF1 | ZBTB17) & !TERT & !MYC_MAX & !MDM2 & !AKT & !ERK); + rate_up = @logic ? $u_p21 : 0; + rate_down = @logic ? 0 : $d_p21; +} + +Node p38 { + logic = (MAP3K1_3 & !ERK & !GADD45); + rate_up = @logic ? $u_p38 : 0; + rate_down = @logic ? 0 : $d_p38; +} + +Node p53 { + logic = ((Acidosis | CHK1_2 | p38 | HIF1) & !BCL2 & !MDM2 & !HSPs & !Snail); + rate_up = @logic ? $u_p53 : 0; + rate_down = @logic ? 0 : $d_p53; +} + +Node p70S6kab { + logic = (mTORC2 | PDK1); + rate_up = @logic ? $u_p70S6kab : 0; + rate_down = @logic ? 0 : $d_p70S6kab; +} + +Node p90RSK { + logic = (PDK1 | ERK); + rate_up = @logic ? $u_p90RSK : 0; + rate_down = @logic ? 0 : $d_p90RSK; +} + +Node PDK1 { + logic = (HIF1 | MYC_MAX | PIP3); + rate_up = @logic ? $u_PDK1 : 0; + rate_down = @logic ? 0 : $d_PDK1; +} + +Node PHDs { + logic = (!Hypoxia & ROS); + rate_up = @logic ? $u_PHDs : 0; + rate_down = @logic ? 0 : $d_PHDs; +} + +Node PI3K { + logic = (TGFBR | FRS2 | EGFR); + rate_up = @logic ? $u_PI3K : 0; + rate_down = @logic ? 0 : $d_PI3K; +} + +Node PIP3 { + logic = ((!p53 & PI3K & !PTEN) | (!p53 & PI3K)); + rate_up = @logic ? $u_PIP3 : 0; + rate_down = @logic ? 0 : $d_PIP3; +} + +Node PKC { + logic = (RTK & WNT); + rate_up = @logic ? $u_PKC : 0; + rate_down = @logic ? 0 : $d_PKC; +} + +Node Proliferation { + logic = (CyclinB | CyclinD); + rate_up = @logic ? $u_Proliferation : 0; + rate_down = @logic ? 0 : $d_Proliferation; +} + +Node PTCH1 { + logic = (!SHH & GLI); + rate_up = @logic ? $u_PTCH1 : 0; + rate_down = @logic ? 0 : $d_PTCH1; +} + +Node PTEN { + logic = ((!NF_kB & !Snail & FGFR3 & p53) | (!NF_kB & !Snail & p53)); + rate_up = @logic ? $u_PTEN : 0; + rate_down = @logic ? 0 : $d_PTEN; +} + + +Node RAF { + logic = (!EZH2 & RAS) | (EZH2); + rate_up = @logic ? $u_RAF : 0; + rate_down = @logic ? 0 : $d_RAF; +} + +Node RAGS { + logic = (!Hypoxia & Nutrients); + rate_up = @logic ? $u_RAGS : 0; + rate_down = @logic ? 0 : $d_RAGS; +} + +Node RAS { + logic = (EGFR | TGFBR | FRS2 | FGFR3); + rate_up = @logic ? $u_RAS : 0; + rate_down = @logic ? 0 : $d_RAS; +} + +Node RB1 { + logic = (!CyclinB & !CyclinD & CHK1_2 & !p53 & !AMPK); + rate_up = @logic ? $u_RB1 : 0; + rate_down = @logic ? 0 : $d_RB1; +} + +Node Rheb { + logic = (!TSC1_2); + rate_up = @logic ? $u_Rheb : 0; + rate_down = @logic ? 0 : $d_Rheb; +} + +Node ROS { + logic = ((COX4I2 | Hypoxia) & !GSH); + rate_up = @logic ? $u_ROS : 0; + rate_down = @logic ? 0 : $d_ROS; +} + +Node RTK { + logic = (EGFR | FGFR3); + rate_up = @logic ? $u_RTK : 0; + rate_down = @logic ? 0 : $d_RTK; +} + +Node RUNX2 { + logic = (!ETS1 & !FOXO & Slug) | (ETS1 & !FOXO); + rate_up = @logic ? $u_RUNX2 : 0; + rate_down = @logic ? 0 : $d_RUNX2; +} + +Node SHH { + logic = (!BMP2 & !ATR); + rate_up = @logic ? $u_SHH : 0; + rate_down = @logic ? 0 : $d_SHH; +} + +Node Slug { + logic = ((TCF | NF_kB) & !DAXX & !p53 & !MDM2); + rate_up = @logic ? $u_Slug : 0; + rate_down = @logic ? 0 : $d_Slug; +} + +Node SMAD { + logic = ((TNFalpha | TGFBR) & !AR); + rate_up = @logic ? $u_SMAD : 0; + rate_down = @logic ? 0 : $d_SMAD; +} + +Node SMO { + logic = (!PTCH1 & SHH); + rate_up = @logic ? $u_SMO : 0; + rate_down = @logic ? 0 : $d_SMO; +} + +Node Snail { + logic = ((GLI | NF_kB | SMAD | beta_catenin) & !GSK3 & !p53); + rate_up = @logic ? $u_Snail : 0; + rate_down = @logic ? 0 : $d_Snail; +} + +Node SPOP { + logic = (SPOP); + rate_up = @logic ? $u_SPOP : 0; + rate_down = @logic ? 0 : $d_SPOP; +} + +Node TAK1 { + logic = (TGFBR | TNFalpha); + rate_up = @logic ? $u_TAK1 : 0; + rate_down = @logic ? 0 : $d_TAK1; +} + +Node TCF { + logic = ((beta_catenin | AR_ERG | ERG) & !TAK1); + rate_up = @logic ? $u_TCF : 0; + rate_down = @logic ? 0 : $d_TCF; +} + +Node TERT { + logic = ((GLI | AKT | HIF1 | MYC_MAX | NF1 | NF_kB | MYC_MAX) & !ZBTB17 & !p53 & !eEF2 & !SMAD); + rate_up = @logic ? $u_TERT : 0; + rate_down = @logic ? 0 : $d_TERT; +} + +Node TGFb { + logic = (TGFb); + rate_up = @logic ? $u_TGFb : 0; + rate_down = @logic ? 0 : $d_TGFb; +} + +Node TGFBR { + logic = ((TGFb | (HIF1 & TGFb)) & !MED12); + rate_up = @logic ? $u_TGFBR : 0; + rate_down = @logic ? 0 : $d_TGFBR; +} + +Node TNFalpha { + logic = (TNFalpha); + rate_up = @logic ? $u_TNFalpha : 0; + rate_down = @logic ? 0 : $d_TNFalpha; +} + +Node TSC1_2 { + logic = ((AMPK | p53 | HIF1) & !AKT & !ERK & !RAF & !p90RSK); + rate_up = @logic ? $u_TSC1_2 : 0; + rate_down = @logic ? 0 : $d_TSC1_2; +} + +Node TWIST1 { + logic = (Snail | beta_catenin); + rate_up = @logic ? $u_TWIST1 : 0; + rate_down = @logic ? 0 : $d_TWIST1; +} + +Node VEGF { + logic = (HIF1 & MYC_MAX); + rate_up = @logic ? $u_VEGF : 0; + rate_down = @logic ? 0 : $d_VEGF; +} + +Node VHL { + logic = (!Hypoxia & !ROS); + rate_up = @logic ? $u_VHL : 0; + rate_down = @logic ? 0 : $d_VHL; +} + +Node WNT { + logic = ((((ERG | AR_ERG) & GLI) | ERG | AR_ERG) & !p53); + rate_up = @logic ? $u_WNT : 0; + rate_down = @logic ? 0 : $d_WNT; +} + +Node YWHAZ { + logic = (AR); + rate_up = @logic ? $u_YWHAZ : 0; + rate_down = @logic ? 0 : $d_YWHAZ; +} + +Node ZBTB17 { + logic = (!MYC_MAX & AR); + rate_up = @logic ? $u_ZBTB17 : 0; + rate_down = @logic ? 0 : $d_ZBTB17; +} + diff --git a/config/boolean_network/Montagud2022_Prostate_Cancer.cfg b/config/boolean_network/Montagud2022_Prostate_Cancer.cfg new file mode 100644 index 0000000..7ddde6c --- /dev/null +++ b/config/boolean_network/Montagud2022_Prostate_Cancer.cfg @@ -0,0 +1,431 @@ +$u_Acidosis = 1; +$d_Acidosis = 1; +$u_AKT = 1; +$d_AKT = 1; +$u_AMP_ATP = 1; +$d_AMP_ATP = 1; +$u_AMPK = 1; +$d_AMPK = 1; +$u_Androgen = 1; +$d_Androgen = 1; +$u_Apoptosis = 1; +$d_Apoptosis = 1; +$u_AR = 1; +$d_AR = 1; +$u_AR_ERG = 1; +$d_AR_ERG = 1; +$u_ATM = 1; +$d_ATM = 1; +$u_ATR = 1; +$d_ATR = 1; +$u_AXIN1 = 1; +$d_AXIN1 = 1; +$u_BAD = 1; +$d_BAD = 1; +$u_Bak = 1; +$d_Bak = 1; +$u_BAX = 1; +$d_BAX = 1; +$u_BCL2 = 1; +$d_BCL2 = 1; +$u_Bcl_XL = 1; +$d_Bcl_XL = 1; +$u_beta_catenin = 1; +$d_beta_catenin = 1; +$u_BIRC5 = 1; +$d_BIRC5 = 1; +$u_BMP2 = 1; +$d_BMP2 = 1; +$u_BRCA1 = 1; +$d_BRCA1 = 1; +$u_BRCA2 = 1; +$d_BRCA2 = 1; +$u_Carcinogen = 1; +$d_Carcinogen = 1; +$u_Caspase3 = 1; +$d_Caspase3 = 1; +$u_Caspase8 = 1; +$d_Caspase8 = 1; +$u_Caspase9 = 1; +$d_Caspase9 = 1; +$u_CDH2 = 1; +$d_CDH2 = 1; +$u_cFLAR = 1; +$d_cFLAR = 1; +$u_CHK1_2 = 1; +$d_CHK1_2 = 1; +$u_COX4I2 = 1; +$d_COX4I2 = 1; +$u_CyclinB = 1; +$d_CyclinB = 1; +$u_CyclinD = 1; +$d_CyclinD = 1; +$u_CytoC = 1; +$d_CytoC = 1; +$u_APAF1 = 1; +$d_APAF1 = 1; +$u_DAXX = 1; +$d_DAXX = 1; +$u_DNA_Damage = 1; +$d_DNA_Damage = 1; +$u_DNA_Repair = 1; +$d_DNA_Repair = 1; +$u_Dsh = 1; +$d_Dsh = 1; +$u_E2F1 = 1; +$d_E2F1 = 1; +$u_E_cadherin = 1; +$d_E_cadherin = 1; +$u_eEF2 = 1; +$d_eEF2 = 1; +$u_eEF2K = 1; +$d_eEF2K = 1; +$u_EMT = 1; +$d_EMT = 1; +$u_EP300 = 1; +$d_EP300 = 1; +$u_ERG = 1; +$d_ERG = 1; +$u_ERK = 1; +$d_ERK = 1; +$u_ETS1 = 1; +$d_ETS1 = 1; +$u_ETV1 = 1; +$d_ETV1 = 1; +$u_EZH2 = 1; +$d_EZH2 = 1; +$u_FADD = 1; +$d_FADD = 1; +$u_FOS = 1; +$d_FOS = 1; +$u_JUN = 1; +$d_JUN = 1; +$u_FOXA1 = 1; +$d_FOXA1 = 1; +$u_FOXO = 1; +$d_FOXO = 1; +$u_EGF = 1; +$d_EGF = 1; +$u_EGFR = 1; +$d_EGFR = 1; +$u_FGF = 1; +$d_FGF = 1; +$u_FGFR3 = 1; +$d_FGFR3 = 1; +$u_FRS2 = 1; +$d_FRS2 = 1; +$u_GADD45 = 1; +$d_GADD45 = 1; +$u_GLI = 1; +$d_GLI = 1; +$u_GLUT1 = 1; +$d_GLUT1 = 1; +$u_GSH = 1; +$d_GSH = 1; +$u_GSK3 = 1; +$d_GSK3 = 1; +$u_HIF1 = 1; +$d_HIF1 = 1; +$u_HSPs = 1; +$d_HSPs = 1; +$u_Hypoxia = 1; +$d_Hypoxia = 1; +$u_IDH1 = 1; +$d_IDH1 = 1; +$u_Invasion = 1; +$d_Invasion = 1; +$u_IKK = 1; +$d_IKK = 1; +$u_JNK = 1; +$d_JNK = 1; +$u_Lactic_acid = 1; +$d_Lactic_acid = 1; +$u_LDHA = 1; +$d_LDHA = 1; +$u_MAP3K1_3 = 1; +$d_MAP3K1_3 = 1; +$u_MDM2 = 1; +$d_MDM2 = 1; +$u_MED12 = 1; +$d_MED12 = 1; +$u_MEK1_2 = 1; +$d_MEK1_2 = 1; +$u_Metastasis = 1; +$d_Metastasis = 1; +$u_Migration = 1; +$d_Migration = 1; +$u_mTORC1 = 1; +$d_mTORC1 = 1; +$u_mTORC2 = 1; +$d_mTORC2 = 1; +$u_MXI1 = 1; +$d_MXI1 = 1; +$u_MYC = 1; +$d_MYC = 1; +$u_MYC_MAX = 1; +$d_MYC_MAX = 1; +$u_NCOA3 = 1; +$d_NCOA3 = 1; +$u_NCOR1 = 1; +$d_NCOR1 = 1; +$u_NCOR2 = 1; +$d_NCOR2 = 1; +$u_NF1 = 1; +$d_NF1 = 1; +$u_NF_kB = 1; +$d_NF_kB = 1; +$u_NKX3_1 = 1; +$d_NKX3_1 = 1; +$u_Nutrients = 1; +$d_Nutrients = 1; +$u_p14ARF = 1; +$d_p14ARF = 1; +$u_p15 = 1; +$d_p15 = 1; +$u_p21 = 1; +$d_p21 = 1; +$u_p38 = 1; +$d_p38 = 1; +$u_p53 = 1; +$d_p53 = 1; +$u_p70S6kab = 1; +$d_p70S6kab = 1; +$u_p90RSK = 1; +$d_p90RSK = 1; +$u_PDK1 = 1; +$d_PDK1 = 1; +$u_PHDs = 1; +$d_PHDs = 1; +$u_PI3K = 1; +$d_PI3K = 1; +$u_PIP3 = 1; +$d_PIP3 = 1; +$u_PKC = 1; +$d_PKC = 1; +$u_Proliferation = 1; +$d_Proliferation = 1; +$u_PTCH1 = 1; +$d_PTCH1 = 1; +$u_PTEN = 1; +$d_PTEN = 1; +$u_RAF = 1; +$d_RAF = 1; +$u_RAGS = 1; +$d_RAGS = 1; +$u_RAS = 1; +$d_RAS = 1; +$u_RB1 = 1; +$d_RB1 = 1; +$u_Rheb = 1; +$d_Rheb = 1; +$u_ROS = 1; +$d_ROS = 1; +$u_RTK = 1; +$d_RTK = 1; +$u_RUNX2 = 1; +$d_RUNX2 = 1; +$u_SHH = 1; +$d_SHH = 1; +$u_Slug = 1; +$d_Slug = 1; +$u_SMAD = 1; +$d_SMAD = 1; +$u_SMO = 1; +$d_SMO = 1; +$u_Snail = 1; +$d_Snail = 1; +$u_SPOP = 1; +$d_SPOP = 1; +$u_TAK1 = 1; +$d_TAK1 = 1; +$u_TCF = 1; +$d_TCF = 1; +$u_TERT = 1; +$d_TERT = 1; +$u_TGFb = 1; +$d_TGFb = 1; +$u_TGFBR = 1; +$d_TGFBR = 1; +$u_fused_event = 1; +$d_fused_event = 1; +$u_TNFalpha = 1; +$d_TNFalpha = 1; +$u_TSC1_2 = 1; +$d_TSC1_2 = 1; +$u_TWIST1 = 1; +$d_TWIST1 = 1; +$u_VEGF = 1; +$d_VEGF = 1; +$u_VHL = 1; +$d_VHL = 1; +$u_WNT = 1; +$d_WNT = 1; +$u_YWHAZ = 1; +$d_YWHAZ = 1; +$u_ZBTB17 = 1; +$d_ZBTB17 = 1; + +discrete_time = 0; +use_physrandgen = FALSE; +seed_pseudorandom = 100; +// sample_count = 500000; +sample_count = 5000; + +max_time = 50; +//time_tick = 0.01; +time_tick = 0.1; +thread_count = 1; + +statdist_traj_count = 100; +statdist_cluster_threshold = 0.9; +display_traj = 0; +statdist_similarity_cache_max_size = 20000; + + + +Acidosis.is_internal=1; +AKT.is_internal=1; +AMPK.is_internal=1; +AMP_ATP.is_internal=1; +Androgen.is_internal=1; +APAF1.is_internal=1; +Apoptosis.is_internal=0; +AR.is_internal=1; +AR_ERG.is_internal=1; +ATM.is_internal=1; +ATR.is_internal=1; +AXIN1.is_internal=1; +BAD.is_internal=1; +Bak.is_internal=1; +BAX.is_internal=1; +BCL2.is_internal=1; +Bcl_XL.is_internal=1; +beta_catenin.is_internal=1; +BIRC5.is_internal=1; +BMP2.is_internal=1; +BRCA1.is_internal=1; +BRCA2.is_internal=1; +Carcinogen.is_internal=1; +Caspase3.is_internal=1; +Caspase8.is_internal=1; +Caspase9.is_internal=1; +CDH2.is_internal=1; +cFLAR.is_internal=1; +CHK1_2.is_internal=1; +COX4I2.is_internal=1; +CyclinB.is_internal=1; +CyclinD.is_internal=1; +CytoC.is_internal=1; +DAXX.is_internal=1; +DNA_Damage.is_internal=1; +DNA_Repair.is_internal=1; +Dsh.is_internal=1; +E2F1.is_internal=1; +eEF2.is_internal=1; +eEF2K.is_internal=1; +EGF.is_internal=1; +EGFR.is_internal=1; +EMT.is_internal=1; +EP300.is_internal=1; +ERG.is_internal=1; +ERK.is_internal=1; +ETS1.is_internal=1; +ETV1.is_internal=1; +EZH2.is_internal=1; +E_cadherin.is_internal=1; +FADD.is_internal=1; +FGF.is_internal=1; +FGFR3.is_internal=1; +FOS.is_internal=1; +FOXA1.is_internal=1; +FOXO.is_internal=1; +FRS2.is_internal=1; +fused_event.is_internal=1; +GADD45.is_internal=1; +GLI.is_internal=1; +GLUT1.is_internal=1; +GSH.is_internal=1; +GSK3.is_internal=1; +HIF1.is_internal=1; +HSPs.is_internal=1; +Hypoxia.is_internal=1; +IDH1.is_internal=1; +IKK.is_internal=1; +Invasion.is_internal=1; +JNK.is_internal=1; +JUN.is_internal=1; +Lactic_acid.is_internal=1; +LDHA.is_internal=1; +MAP3K1_3.is_internal=1; +MDM2.is_internal=1; +MED12.is_internal=1; +MEK1_2.is_internal=1; +Metastasis.is_internal=0; +Migration.is_internal=1; +mTORC1.is_internal=1; +mTORC2.is_internal=1; +MXI1.is_internal=1; +MYC.is_internal=1; +MYC_MAX.is_internal=1; +NCOA3.is_internal=1; +NCOR1.is_internal=1; +NCOR2.is_internal=1; +NF1.is_internal=1; +NF_kB.is_internal=1; +NKX3_1.is_internal=1; +Nutrients.is_internal=1; +p14ARF.is_internal=1; +p15.is_internal=1; +p21.is_internal=1; +p38.is_internal=1; +p53.is_internal=1; +p70S6kab.is_internal=1; +p90RSK.is_internal=1; +PDK1.is_internal=1; +PHDs.is_internal=1; +PI3K.is_internal=1; +PIP3.is_internal=1; +PKC.is_internal=1; +Proliferation.is_internal=0; +PTCH1.is_internal=1; +PTEN.is_internal=1; +RAF.is_internal=1; +RAGS.is_internal=1; +RAS.is_internal=1; +RB1.is_internal=1; +Rheb.is_internal=1; +ROS.is_internal=1; +RTK.is_internal=1; +RUNX2.is_internal=1; +SHH.is_internal=1; +Slug.is_internal=1; +SMAD.is_internal=1; +SMO.is_internal=1; +Snail.is_internal=1; +SPOP.is_internal=1; +TAK1.is_internal=1; +TCF.is_internal=1; +TERT.is_internal=1; +TGFb.is_internal=1; +TGFBR.is_internal=1; +TNFalpha.is_internal=1; +TSC1_2.is_internal=1; +TWIST1.is_internal=1; +VEGF.is_internal=1; +VHL.is_internal=1; +WNT.is_internal=1; +YWHAZ.is_internal=1; +ZBTB17.is_internal=1; + + +//[EGF].istate= 0 [0] , 1 [1]; +//[FGF].istate= 1 [0] , 0 [1]; +//[TGFb].istate= 1 [0] , 0 [1]; +//[Nutrients].istate= 1 [0] , 0 [1]; +//[Hypoxia].istate= 1 [0] , 0 [1]; +//[Acidosis].istate= 1 [0] , 0 [1]; +//[Androgen].istate= 1 [0] , 0 [1]; +//[fused_event].istate= 1 [0] , 0 [1]; +//[TNFalpha].istate= 1 [0] , 0 [1]; +//[SPOP].istate= 1 [0] , 0 [1]; +//[Carcinogen].istate= 1 [0] , 0 [1]; \ No newline at end of file diff --git a/config/init2.csv b/config/init2.csv new file mode 100644 index 0000000..bcb5d10 --- /dev/null +++ b/config/init2.csv @@ -0,0 +1,1138 @@ +x,y,z,type,volume +-51.2137,-4.55442,-83.2907,default,8.5 +-37.5888,-33.2832,-84.4271,default,9.8791 +-37.6541,-13.6737,-86.5638,default,8.5 +-38.7093,0.223641,-85.6339,default,8.93259 +-35.4237,20.7316,-84.4495,default,10.0373 +-21.6863,-40.8023,-87.282,default,9.31441 +-20.457,-22.107,-86.0417,default,8.72424 +-21.191,-8.58699,-84.1779,default,8.68751 +-20.1745,10.7166,-86.0735,default,8.9461 +-22.5775,28.6424,-83.8703,default,9.1147 +-9.02799,-49.3179,-83.452,default,10.3831 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microenvironment.density_names[i]; + if (drug_name != "oxygen") { + int drug_index = microenvironment.find_density_index(drug_name); + + // Cache the string operations + std::string conc_var_name = drug_name + "_concentration"; + std::string node_var_name = drug_name + "_node"; + + int index_drug_conc = pCell->custom_data.find_variable_index(conc_var_name); + int index_drug_node = pCell->custom_data.find_variable_index(node_var_name); + + const string& drug_target = get_value(drug_targets, drug_name); + std::string anti_target = "anti_" + drug_target; + + pCell->custom_data.variables.at(index_drug_conc).value = pCell->nearest_density_vector()[drug_index]; + pCell->custom_data.variables.at(index_drug_node).value = pCell->phenotype.intracellular->get_boolean_variable_value(anti_target); + } + } +} + + +// void set_boolean_node (Cell* pCell, std::string drug_name, int drug_index, double threshold) { +void set_boolean_node (Cell* pCell, std::string drug_name, int drug_index) { + if (drug_index != -1) + { + string drug_target = get_value(drug_targets, drug_name); + std::string node_name = "anti_" + drug_target; + // get internalized substrate concentration + double drug_conc = pCell->nearest_density_vector()[drug_index]; + double cell_viability = get_cell_viability_for_drug_conc(drug_conc, parameters.strings("cell_line"), drug_name); + double cell_inhibition = 1 - cell_viability; + double random_num = (double) rand()/RAND_MAX; + if (random_num <= cell_inhibition) + { + pCell->phenotype.intracellular->set_boolean_variable_value(node_name, 1); + } + else + { + pCell->phenotype.intracellular->set_boolean_variable_value(node_name, 0); + } + + } +} + +void set_input_nodes(Cell* pCell) { + + if (PhysiCell::parameters.ints("simulation_mode") == 0) + { + // single inhibition - just one drug is present + + std::string drug_name = microenvironment.density_names[1]; + int drug_index = microenvironment.find_density_index(drug_name); + // int drug_threshold = PhysiCell::parameters.doubles( "threshold_" + drug_name); + + if (pCell->type_name == drug_name + "_sensitive") + { + // cell is sensitive to the drug -> set boolean node + // set_boolean_node(pCell, drug_name, drug_index, drug_threshold); + set_boolean_node(pCell, drug_name, drug_index); + } + } + else if (PhysiCell::parameters.ints("simulation_mode") == 1) + { + // double inhibition - two drugs are present - we have 4 cell strains + + std::string drug1_name = microenvironment.density_names[1]; + int drug1_index = microenvironment.find_density_index(drug1_name); + // int drug1_threshold = PhysiCell::parameters.doubles( "threshold_" + drug1_name); + + std::string drug2_name = microenvironment.density_names[2]; + int drug2_index = microenvironment.find_density_index(drug2_name); + // int drug2_threshold = PhysiCell::parameters.doubles( "threshold_" + drug2_name); + + // if (pCell->type_name == drug1_name + "_sensitive") + // { + // cell is sensitive to both drugs + // set_boolean_node(pCell, drug1_name, drug1_index, drug1_threshold); + // set_boolean_node(pCell, drug2_name, drug2_index, drug2_threshold); + set_boolean_node(pCell, drug1_name, drug1_index); + set_boolean_node(pCell, drug2_name, drug2_index); + // } + // else if (pCell->type_name == drug2_name + "_resistant") + // { + // cell is only sensitive to the first drug + // set_boolean_node(pCell, drug1_name, drug1_index, drug1_threshold); + // set_boolean_node(pCell, drug1_name, drug1_index); + // } + // else if (pCell->type_name == drug2_name + "_sensitive") + // { + // cell is only sensitive to the second drug + // set_boolean_node(pCell, drug2_name, drug2_index, drug2_threshold); + // set_boolean_node(pCell, drug2_name, drug2_index); + // } + + // else: cell is sensitive to no drug --> no boolean node is set + } +} + +void from_nodes_to_cell(Cell* pCell, Phenotype& phenotype, double dt) +{ + + // Prostate live model + // map apoptosis, proliferation and invasion values to agent-based model + + if( pCell->phenotype.cycle.model().code == PhysiCell_constants::live_cells_cycle_model ) + { + int start_phase_index = phenotype.cycle.model().find_phase_index( PhysiCell_constants::live ); + int end_phase_index = phenotype.cycle.model().find_phase_index( PhysiCell_constants::live ); + int apoptosis_index = phenotype.death.find_death_model_index(PhysiCell_constants::apoptosis_death_model); + + // Update Apoptosis + if(pCell->phenotype.intracellular->get_boolean_variable_value("Apoptosis")) + { + // simple implementation, just lead immediately to death + // pCell->start_death(apoptosis_index); + + // increase death rate whenever the node is ON + pCell->phenotype.death.rates[apoptosis_index] = PhysiCell::parameters.doubles("apoptosis_rate_multiplier") * pCell->phenotype.death.rates[apoptosis_index]; + } + + // Update Adhesion + // if( pCell->phenotype.intracellular->get_boolean_variable_value("EMT")){ // nothing happens for now } + + // Update pReference_live_phenotype for proliferation node + if (pCell->phenotype.intracellular->get_boolean_variable_value("Proliferation")) + { + // multiplier implementation + //pCell->parameters.pReference_live_phenotype->cycle.data.transition_rate(start_phase_index,end_phase_index) *= 2.5; + //std::cout << "Rate up! " << std::endl; + + //switch implementation + double high_transition_rate = PhysiCell::parameters.doubles("base_transition_rate") * PhysiCell::parameters.doubles("transition_rate_multiplier"); + pCell->parameters.pReference_live_phenotype->cycle.data.transition_rate(start_phase_index,end_phase_index) = high_transition_rate; + + // pCell->parameters.pReference_live_phenotype->cycle.data.transition_rate(start_phase_index,end_phase_index) = PhysiCell::parameters.doubles("base_transition_rate"); + } + else + { + //multiplier implementation + //pCell->parameters.pReference_live_phenotype->cycle.data.transition_rate(start_phase_index,end_phase_index) *= 0.4; + //std::cout << "Rate down! " << std::endl; + + //switch implementation + pCell->parameters.pReference_live_phenotype->cycle.data.transition_rate(start_phase_index,end_phase_index) = PhysiCell::parameters.doubles("base_transition_rate"); + } + + // Update Migration + if(pCell->phenotype.intracellular->get_boolean_variable_value("Migration")) + { + pCell->phenotype.motility.is_motile = true; + pCell->phenotype.motility.migration_speed = PhysiCell::parameters.doubles("migration_speed"); + pCell->phenotype.motility.migration_bias = PhysiCell::parameters.doubles("migration_bias"); + pCell->phenotype.motility.persistence_time = PhysiCell::parameters.doubles("persistence"); + + // pCell->evolve_coef(pCell->phenotype.intracellular->get_boolean_variable_value("Migration"), phenotype.motility.migration_speed, dt ); + // pCell->phenotype.motility.migration_speed = PhysiCell::parameters.doubles("max_motility_speed") * migration_coeff + } + else + { + pCell->phenotype.motility.is_motile = false; + } + + // Update Invasion + // if(pCell->phenotype.intracellular->get_boolean_variable_value("Invasion")){// nothing happens for now } + } + + pCell->set_internal_uptake_constants(dt); +} + +void boolean_model_interface_main (Cell* pCell, Phenotype& phenotype, double dt){ + + if( phenotype.death.dead == true ) + { + pCell->functions.update_phenotype = NULL; + return; + } +} + +void pre_update_intracellular(Cell* pCell, Phenotype& phenotype, double dt) +{ + set_input_nodes(pCell); +} + +void post_update_intracellular(Cell* pCell, Phenotype& phenotype, double dt) +{ + update_custom_variables(pCell); + from_nodes_to_cell(pCell, phenotype, dt); +} \ No newline at end of file diff --git a/custom_modules/boolean_model_interface.h b/custom_modules/boolean_model_interface.h new file mode 100644 index 0000000..b73c501 --- /dev/null +++ b/custom_modules/boolean_model_interface.h @@ -0,0 +1,35 @@ +#ifndef __BOOLEAN_MODEL_INTERFACE_H__ +#define __BOOLEAN_MODEL_INTERFACE_H__ + +#include "../core/PhysiCell.h" +#include "../modules/PhysiCell_standard_modules.h" +#include "../addons/PhysiBoSS/src/maboss_network.h" +#include "./drug_sensitivity.h" + + +/** + * \boolean network interface + * \brief custom module for prostate example + * + * \details Modules needed for the prostate example. This custom module can be used to study the inhibition of prostate cell lines. + * + * + * \date 21/08/2025 + * \author Annika Meert, Arnau Montagud, Gerard Pradas and Miguel Ponce de Leon, BSC-CNS + */ + +using namespace BioFVM; +using namespace PhysiCell; + +void update_custom_variables( Cell* pCell ); + +// void set_boolean_node (Cell* pCell, std::string drug_name, int index, double threshold); +void set_boolean_node (Cell* pCell, std::string drug_name, int drug_index); +void set_input_nodes(Cell* pCell); +void from_nodes_to_cell(Cell* pCell, Phenotype& phenotype, double dt); +void boolean_model_interface_main (Cell* pCell, Phenotype& phenotype, double dt); + +void pre_update_intracellular(Cell* pCell, Phenotype& phenotype, double dt); +void post_update_intracellular(Cell* pCell, Phenotype& phenotype, double dt); + +#endif \ No newline at end of file diff --git a/custom_modules/custom.cpp b/custom_modules/custom.cpp index d3b6fda..0872ead 100644 --- a/custom_modules/custom.cpp +++ b/custom_modules/custom.cpp @@ -1,74 +1,31 @@ -/* -############################################################################### -# If you use PhysiCell in your project, please cite PhysiCell and the version # -# number, such as below: # -# # -# We implemented and solved the model using PhysiCell (Version x.y.z) [1]. # -# # -# [1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, # -# PhysiCell: an Open Source Physics-Based Cell Simulator for Multicellu- # -# lar Systems, PLoS Comput. Biol. 14(2): e1005991, 2018 # -# DOI: 10.1371/journal.pcbi.1005991 # -# # -# See VERSION.txt or call get_PhysiCell_version() to get the current version # -# x.y.z. Call display_citations() to get detailed information on all cite-# -# able software used in your PhysiCell application. # -# # -# Because PhysiCell extensively uses BioFVM, we suggest you also cite BioFVM # -# as below: # -# # -# We implemented and solved the model using PhysiCell (Version x.y.z) [1], # -# with BioFVM [2] to solve the transport equations. # -# # -# [1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, # -# PhysiCell: an Open Source Physics-Based Cell Simulator for Multicellu- # -# lar Systems, PLoS Comput. Biol. 14(2): e1005991, 2018 # -# DOI: 10.1371/journal.pcbi.1005991 # -# # -# [2] A Ghaffarizadeh, SH Friedman, and P Macklin, BioFVM: an efficient para- # -# llelized diffusive transport solver for 3-D biological simulations, # -# Bioinformatics 32(8): 1256-8, 2016. DOI: 10.1093/bioinformatics/btv730 # -# # -############################################################################### -# # -# BSD 3-Clause License (see https://opensource.org/licenses/BSD-3-Clause) # -# # -# Copyright (c) 2015-2021, Paul Macklin and the PhysiCell Project # -# All rights reserved. # -# # -# Redistribution and use in source and binary forms, with or without # -# modification, are permitted provided that the following conditions are met: # -# # -# 1. Redistributions of source code must retain the above copyright notice, # -# this list of conditions and the following disclaimer. # -# # -# 2. Redistributions in binary form must reproduce the above copyright # -# notice, this list of conditions and the following disclaimer in the # -# documentation and/or other materials provided with the distribution. # -# # -# 3. Neither the name of the copyright holder nor the names of its # -# contributors may be used to endorse or promote products derived from this # -# software without specific prior written permission. # -# # -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # -# POSSIBILITY OF SUCH DAMAGE. # -# # -############################################################################### -*/ + +#define _USE_MATH_DEFINES +#include +#include #include "./custom.h" +#include "../BioFVM/BioFVM.h" + +/** + * \main prostate custom + * \brief Custom module file for prostate example + * + * \details Modules needed for the prostate example. + * + * + * \date 20/08/2025 + * \author Arnau Montagud, Annika Meert, Gerard Pradas and Miguel Ponce de Leon, BSC-CNS + */ + +// declare cell definitions here void create_cell_types( void ) { + // use the same random seed so that future experiments have the + // same initial histogram of oncoprotein, even if threading means + // that future division and other events are still not identical + // for all runs + // set the random seed if (parameters.ints.find_index("random_seed") != -1) { @@ -81,38 +38,52 @@ void create_cell_types( void ) This is a good place to set default functions. */ - + initialize_default_cell_definition(); cell_defaults.phenotype.secretion.sync_to_microenvironment( µenvironment ); - + cell_defaults.functions.volume_update_function = standard_volume_update_function; cell_defaults.functions.update_velocity = standard_update_cell_velocity; + // cell_defaults.functions.pre_update_intracellular = pre_update_intracellular; + // cell_defaults.functions.post_update_intracellular = post_update_intracellular; + cell_defaults.functions.update_migration_bias = NULL; - cell_defaults.functions.update_phenotype = NULL; // update_cell_and_death_parameters_O2_based; + cell_defaults.functions.update_phenotype = NULL; // tumor_cell_phenotype_with_signaling; cell_defaults.functions.custom_cell_rule = NULL; cell_defaults.functions.contact_function = NULL; cell_defaults.functions.add_cell_basement_membrane_interactions = NULL; cell_defaults.functions.calculate_distance_to_membrane = NULL; + // cell_defaults.functions.set_orientation = NULL; + /* This parses the cell definitions in the XML config file. */ - - initialize_cell_definitions_from_pugixml(); + + initialize_cell_definitions_from_pugixml(); /* This builds the map of cell definitions and summarizes the setup. */ - + + // set molecular properties for each drug defined in PhysiCell_settings.xml + // starts with the second density because the first density is oxygen + // for (int i = 0; i < microenvironment.number_of_densities(); i++) + // { + // if (microenvironment.density_names[i] != "oxygen") { + // cell_defaults.phenotype.molecular.fraction_released_at_death[i] = 0.0; + // } + // } + build_cell_definitions_maps(); /* This intializes cell signal and response dictionaries */ - setup_signal_behavior_dictionaries(); + setup_signal_behavior_dictionaries(); /* Cell rule definitions @@ -125,84 +96,186 @@ void create_cell_types( void ) This is a good place to set custom functions. */ + + cell_defaults.functions.pre_update_intracellular = pre_update_intracellular; + cell_defaults.functions.post_update_intracellular = post_update_intracellular; cell_defaults.functions.update_phenotype = phenotype_function; cell_defaults.functions.custom_cell_rule = custom_function; cell_defaults.functions.contact_function = contact_function; + + Cell_Definition* pCD = find_cell_definition( "default"); + + pCD->functions.pre_update_intracellular = pre_update_intracellular; + pCD->functions.post_update_intracellular = post_update_intracellular; + pCD->functions.custom_cell_rule = custom_function; + pCD->functions.contact_function = contact_function; + pCD->functions.update_velocity = standard_update_cell_velocity; + /* This builds the map of cell definitions and summarizes the setup. */ - + display_cell_definitions( std::cout ); - + return; } +double get_decay_rate(double half_life){ + // natural logarithm of 2 / half-life = k (reaction rate coefficient) + double decay_rate = log(2)/(half_life); + return decay_rate; +} + void setup_microenvironment( void ) { - // set domain parameters - - // put any custom code to set non-homogeneous initial conditions or - // extra Dirichlet nodes here. - + // // make sure to override and go back to 2D + // if( default_microenvironment_options.simulate_2D == true ) + // { + // std::cout << "Warning: overriding XML config option and setting to 3D!" << std::endl; + // default_microenvironment_options.simulate_2D = false; + // } + + // set intial conditions and dirichlet boundary conditions for the drugs; vector already contains the condition for oxygen + // double oxygen_condition = 160.0; + // vector condition_vector = {oxygen_condition}; + // vector activation_vector {1}; + // vector decay_vector {0.1}; + // for (int i = 0; i < microenvironment.number_of_densities(); i++) + // { + // std::string drug_name = microenvironment.density_names[i]; + // if (drug_name != "oxygen") { + // // int current_drug_level= parameters.ints("current_concentration_level_" + drug_name); + // // int total_drug_levels = parameters.ints("total_concentration_levels"); + // string cell_line = parameters.strings("cell_line"); + // int simulation_mode = parameters.ints("simulation_mode"); + + // //drug_conc can either be an IC value or an actual drug concentration + // string drug_conc = parameters.strings("drug_concentration_" + drug_name); + // double drug_concentration; + // // check if drug_conc contains the string "IC" + // if(drug_conc.find("IC") != string::npos) { + // drug_concentration = get_drug_concentration_from_IC(cell_line, drug_name, drug_conc, simulation_mode); + // } + // else { + // drug_concentration = stod(drug_conc); + // } + // // double drug_concentration = get_drug_concentration_from_level(cell_line, drug_name, current_drug_level, total_drug_levels, simulation_mode); + // condition_vector.push_back(drug_concentration); + // activation_vector.push_back(1); + + // // double half_life = get_value(half_lives, drug_name); + // // double decay_rate = get_decay_rate(half_life); + // // decay_vector.push_back(decay_rate); + + // } + // } + // default_microenvironment_options.Dirichlet_activation_vector = activation_vector; + // default_microenvironment_options.Dirichlet_condition_vector = condition_vector; + // default_microenvironment_options.Dirichlet_zmax_values = condition_vector; + // default_microenvironment_options.Dirichlet_zmin_values = condition_vector; + // default_microenvironment_options.Dirichlet_ymax_values = condition_vector; + // default_microenvironment_options.Dirichlet_ymin_values = condition_vector; + // default_microenvironment_options.Dirichlet_xmax_values = condition_vector; + // default_microenvironment_options.Dirichlet_xmin_values = condition_vector; + + // initialize BioFVM - initialize_microenvironment(); + + // microenvironment.decay_rates = decay_vector; return; } void setup_tissue( void ) { - double Xmin = microenvironment.mesh.bounding_box[0]; - double Ymin = microenvironment.mesh.bounding_box[1]; - double Zmin = microenvironment.mesh.bounding_box[2]; + // place a cluster of tumor cells at the center + load_cells_from_pugixml(); - double Xmax = microenvironment.mesh.bounding_box[3]; - double Ymax = microenvironment.mesh.bounding_box[4]; - double Zmax = microenvironment.mesh.bounding_box[5]; - - if( default_microenvironment_options.simulate_2D == true ) - { - Zmin = 0.0; - Zmax = 0.0; - } - - double Xrange = Xmax - Xmin; - double Yrange = Ymax - Ymin; - double Zrange = Zmax - Zmin; - - // create some of each type of cell - +} + +// old setup_tissue function, needs to be updated to new structure +void setup_tissue_resistant( void ) +{ Cell* pC; - - for( int k=0; k < cell_definitions_by_index.size() ; k++ ) + + std::vector cells = read_init_file(parameters.strings("init_cells_filename"), ';', true); + + for (int i = 0; i < cells.size(); i++) { - Cell_Definition* pCD = cell_definitions_by_index[k]; - std::cout << "Placing cells of type " << pCD->name << " ... " << std::endl; - for( int n = 0 ; n < parameters.ints("number_of_cells") ; n++ ) + float x = cells[i].x; + float y = cells[i].y; + float z = cells[i].z; + float radius = cells[i].radius; + int phase = cells[i].phase; + double elapsed_time = cells[i].elapsed_time; + + double random_num_1 = (double) rand()/RAND_MAX; + double random_num_2 = (double) rand()/RAND_MAX; + + if (PhysiCell::parameters.ints("simulation_mode") == 0) + { + // single inhibition - just one drug is present + if (random_num_1 < PhysiCell::parameters.doubles("prop_drug_resistant_" + microenvironment.density_names[1])) + { + // cell is sensitive to the drug + pC = create_cell(get_cell_definition(microenvironment.density_names[1] + "_resistant")); + } + else + { + // cell is not sensitive to the drug + pC = create_cell(get_cell_definition(microenvironment.density_names[1] + "_sensitive")); + } + } + else if (PhysiCell::parameters.ints("simulation_mode") == 1) { - std::vector position = {0,0,0}; - position[0] = Xmin + UniformRandom()*Xrange; - position[1] = Ymin + UniformRandom()*Yrange; - position[2] = Zmin + UniformRandom()*Zrange; + // double inhibition - two drugs are present - we have 4 cell strains + if (random_num_1 < PhysiCell::parameters.doubles("prop_drug_resistant_" + microenvironment.density_names[1])) + { + if (random_num_2 < PhysiCell::parameters.doubles("prop_drug_resistant_" + microenvironment.density_names[2])) + { + // cell is resistant to both drugs + pC = create_cell(get_cell_definition(microenvironment.density_names[1] + "_resistant")); + } + else + { + // cell is only resistant to the first drug + pC = create_cell(get_cell_definition(microenvironment.density_names[2] + "_sensitive")); + } + } + else + { + if (random_num_2 < PhysiCell::parameters.doubles("prop_drug_resistant_" + microenvironment.density_names[2])) + { + // cell is only resistant to the second drug + pC = create_cell(get_cell_definition(microenvironment.density_names[2] + "_resistant")); + } + else + { + // cell is resistant to no drug + pC = create_cell(get_cell_definition(microenvironment.density_names[1] + "_sensitive")); + } + + } - pC = create_cell( *pCD ); - pC->assign_position( position ); } + else + { + pC = create_cell(get_cell_definition("default")); + } + + pC->assign_position( x, y, z ); + + pC->phenotype.cycle.data.elapsed_time_in_phase = elapsed_time; + + update_custom_variables(pC); } - std::cout << std::endl; - - // load cells from your CSV file (if enabled) - load_cells_from_pugixml(); - + return; } -std::vector my_coloring_function( Cell* pCell ) -{ return paint_by_number_cell_coloring(pCell); } - void phenotype_function( Cell* pCell, Phenotype& phenotype, double dt ) { return; } @@ -213,34 +286,140 @@ void contact_function( Cell* pMe, Phenotype& phenoMe , Cell* pOther, Phenotype& { return; } void treatment_function () +{ return; } + +void tumor_cell_phenotype_with_signaling( Cell* pCell, Phenotype& phenotype, double dt) { - if (PhysiCell::parameters.bools.find_index("treatment") != -1) + update_cell_and_death_parameters_O2_based(pCell, phenotype, dt); + + // update motility state variable + static int index_motility_state = pCell->custom_data.find_variable_index("motility_state"); + pCell->custom_data.variables.at(index_motility_state).value = int(pCell->phenotype.motility.is_motile); + + boolean_model_interface_main (pCell, phenotype, dt); +} + +// coloring function, not used + +// std::vector prolif_apoptosis_coloring( Cell* pCell ) +// { +// std::vector output; +// if (pCell->phenotype.cycle.current_phase().code == PhysiCell_constants::apoptosis_death_model) +// { +// //apoptotic cells are colored red +// output = {"crimson", "black","darkred", "darkred"}; +// } + +// else if (pCell->phenotype.cycle.current_phase().code == PhysiCell_constants::necrosis_death_model) +// { +// //necrotic cells are colored brown +// output = {"peru", "black","saddlebrown", "saddlebrown"}; +// } + +// else if (PhysiCell::parameters.ints("simulation_mode") == 0) +// { +// std::string drug_name = microenvironment.density_names[1]; +// if (pCell->type_name == drug_name + "_sensitive") +// { +// //drug sensitive living cells are colored blue +// output = {"deepskyblue", "black", "darkblue", "darkblue"}; +// } +// else +// { +// //drug resistant living cells are colored green +// output = {"limegreen", "black", "darkgreen", "darkgreen"}; +// } +// } +// else if (PhysiCell::parameters.ints("simulation_mode") == 1) +// { +// // // color living cells just in one color +// // output = {"limegreen", "black", "darkgreen", "darkgreen"}; + +// // In case we want to color all 4 strains differently: +// // double inhibitions --> 4 cell strains +// std::string drug1_name = microenvironment.density_names[1]; +// std::string drug2_name = microenvironment.density_names[2]; +// if (pCell->type_name == drug1_name + "_sensitive") +// { +// //cells that are sensitive to both drugs are colored blue +// output = {"deepskyblue", "black", "darkblue", "darkblue"}; +// } +// else if (pCell->type_name == drug2_name + "_resistant") +// { +// // cells that are just sensitive to the first drug +// output = {"limegreen", "black", "darkgreen", "darkgreen"}; + +// } +// else if (pCell->type_name == drug2_name + "_sensitive") +// { +// // cells are just sensitive to the second drug +// output = {"gold", "black", "orange", "orange"}; +// } +// else +// { +// // cells aren't sensitive to any drug +// output = {"mediumorchid", "black", "mediumpurple", "mediumpurple"}; +// } +// } +// else +// { +// // no drug simulation - living cells are colored green +// output = {"limegreen", "black", "darkgreen", "darkgreen"}; +// } +// return output; + +// } + +// *********************************************************** +// * NOTE: Funtion to read init files created with PhysiBoSS * +// *********************************************************** +std::vector read_init_file(std::string filename, char delimiter, bool header) +{ + // File pointer + std::fstream fin; + std::vector result; + + // Open an existing file + fin.open(filename, std::ios::in); + + // Read the Data from the file + // as String Vector + std::vector row; + std::string line, word; + + if(header) + getline(fin, line); + + do { - int treatment_substrate_index = BioFVM::microenvironment.find_density_index(PhysiCell::parameters.strings("treatment_substrate")); + row.clear(); - if (PhysiCell::parameters.bools("treatment")){ - - if ( - (((int)PhysiCell::PhysiCell_globals.current_time) % PhysiCell::parameters.ints("treatment_period")) == 0 - && !BioFVM::microenvironment.get_substrate_dirichlet_activation(treatment_substrate_index) - ) - { - std::cout << PhysiCell::parameters.strings("treatment_substrate") << " activation at t=" << PhysiCell::PhysiCell_globals.current_time << std::endl; - BioFVM::microenvironment.set_substrate_dirichlet_activation(treatment_substrate_index, true); - } + // read an entire row and + // store it in a string variable 'line' + getline(fin, line); - if ( - (((int)PhysiCell::PhysiCell_globals.current_time) % PhysiCell::parameters.ints("treatment_period")) == PhysiCell::parameters.ints("treatment_duration") - && BioFVM::microenvironment.get_substrate_dirichlet_activation(treatment_substrate_index) - ) - { - std::cout << PhysiCell::parameters.strings("treatment_substrate") << " inactivation at t=" << PhysiCell::PhysiCell_globals.current_time << std::endl; - BioFVM::microenvironment.set_substrate_dirichlet_activation(treatment_substrate_index, false); - } - - } else if ( BioFVM::microenvironment.get_substrate_dirichlet_activation(treatment_substrate_index) ){ - std::cout << PhysiCell::parameters.strings("treatment_substrate") << " inactivation (NO TREATMENT) at t=" << PhysiCell::PhysiCell_globals.current_time << std::endl; - BioFVM::microenvironment.set_substrate_dirichlet_activation(treatment_substrate_index, false); + // used for breaking words + std::stringstream s(line); + + // read every column data of a row and + // store it in a string variable, 'word' + while (getline(s, word, delimiter)) { + + // add all the column data + // of a row to a vector + row.push_back(word); } - } + + init_record record; + record.x = std::stof(row[2]); + record.y = std::stof(row[3]); + record.z = std::stof(row[4]); + record.radius = std::stof(row[5]); + record.phase = std::stoi(row[13]); + record.elapsed_time = std::stod(row[14]); + + result.push_back(record); + } while (!fin.eof()); + + return result; } \ No newline at end of file diff --git a/custom_modules/custom.h b/custom_modules/custom.h index bb5843d..dd07b75 100644 --- a/custom_modules/custom.h +++ b/custom_modules/custom.h @@ -1,93 +1,58 @@ -/* -############################################################################### -# If you use PhysiCell in your project, please cite PhysiCell and the version # -# number, such as below: # -# # -# We implemented and solved the model using PhysiCell (Version x.y.z) [1]. # -# # -# [1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, # -# PhysiCell: an Open Source Physics-Based Cell Simulator for Multicellu- # -# lar Systems, PLoS Comput. Biol. 14(2): e1005991, 2018 # -# DOI: 10.1371/journal.pcbi.1005991 # -# # -# See VERSION.txt or call get_PhysiCell_version() to get the current version # -# x.y.z. Call display_citations() to get detailed information on all cite-# -# able software used in your PhysiCell application. # -# # -# Because PhysiCell extensively uses BioFVM, we suggest you also cite BioFVM # -# as below: # -# # -# We implemented and solved the model using PhysiCell (Version x.y.z) [1], # -# with BioFVM [2] to solve the transport equations. # -# # -# [1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, # -# PhysiCell: an Open Source Physics-Based Cell Simulator for Multicellu- # -# lar Systems, PLoS Comput. Biol. 14(2): e1005991, 2018 # -# DOI: 10.1371/journal.pcbi.1005991 # -# # -# [2] A Ghaffarizadeh, SH Friedman, and P Macklin, BioFVM: an efficient para- # -# llelized diffusive transport solver for 3-D biological simulations, # -# Bioinformatics 32(8): 1256-8, 2016. DOI: 10.1093/bioinformatics/btv730 # -# # -############################################################################### -# # -# BSD 3-Clause License (see https://opensource.org/licenses/BSD-3-Clause) # -# # -# Copyright (c) 2015-2021, Paul Macklin and the PhysiCell Project # -# All rights reserved. # -# # -# Redistribution and use in source and binary forms, with or without # -# modification, are permitted provided that the following conditions are met: # -# # -# 1. Redistributions of source code must retain the above copyright notice, # -# this list of conditions and the following disclaimer. # -# # -# 2. Redistributions in binary form must reproduce the above copyright # -# notice, this list of conditions and the following disclaimer in the # -# documentation and/or other materials provided with the distribution. # -# # -# 3. Neither the name of the copyright holder nor the names of its # -# contributors may be used to endorse or promote products derived from this # -# software without specific prior written permission. # -# # -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # -# POSSIBILITY OF SUCH DAMAGE. # -# # -############################################################################### -*/ +#ifndef __CUSTOM_H__ +#define __CUSTOM_H__ #include "../core/PhysiCell.h" -#include "../modules/PhysiCell_standard_modules.h" +#include "../modules/PhysiCell_standard_modules.h" +#include "drug_sensitivity.h" +#include "boolean_model_interface.h" + +/** + * \main drug custom + * \brief Custom module file for example + * + * \details Modules needed for the drug example. This custom module can be used to study the inhibition of cell lines with inhibitors. + * + * + * \date 20/08/2025 + * \author Arnau Montagud, Annika Meert, Gerard Pradas and Miguel Ponce de Leon, BSC-CNS + */ using namespace BioFVM; using namespace PhysiCell; -// setup functions to help us along - void create_cell_types( void ); -void setup_tissue( void ); +double get_decay_rate(double half_life); // set up the BioFVM microenvironment void setup_microenvironment( void ); -// custom pathology coloring function - -std::vector my_coloring_function( Cell* ); - -// custom functions can go here +void setup_tissue( void ); +void setup_tissue_resistant( void ); void phenotype_function( Cell* pCell, Phenotype& phenotype, double dt ); void custom_function( Cell* pCell, Phenotype& phenotype , double dt ); - void contact_function( Cell* pMe, Phenotype& phenoMe , Cell* pOther, Phenotype& phenoOther , double dt ); +void treatment_function (); + +// custom cell phenotype functions could go here +void tumor_cell_phenotype_with_signaling( Cell* pCell, Phenotype& phenotype, double dt ); + +// needed for setup_tissue_resistant +struct init_record +{ + float x; + float y; + float z; + float radius; + int phase; + double elapsed_time; +}; + +// custom pathology coloring function +// std::vector prolif_apoptosis_coloring( Cell* ); + +std::vector read_init_file(std::string filename, char delimiter, bool header); + +inline float sphere_volume_from_radius(float radius) {return 4/3 * PhysiCell_constants::pi * std::pow(radius, 3);} -void treatment_function (); \ No newline at end of file +#endif \ No newline at end of file diff --git a/custom_modules/drug_sensitivity.cpp b/custom_modules/drug_sensitivity.cpp new file mode 100644 index 0000000..a0c15e5 --- /dev/null +++ b/custom_modules/drug_sensitivity.cpp @@ -0,0 +1,172 @@ + +#include "./drug_sensitivity.h" + +/** + * \drug sensitivity + * \brief drug sensitivity module for prostate example + * + * \details Module needed for the prostate example. + * + * + * \date 15/02/2020 + * \author Annika Meert, BSC-CNS + */ + +using namespace std; + +vector> drug_targets = { + { "Ipatasertib", "AKT"}, + { "Afuresertib", "AKT"}, + { "Afatinib", "EGFR"}, + { "Erlotinib", "EGFR"}, + { "Ulixertinib", "ERK"}, + { "Luminespib", "HSPs"}, + { "Trametinib", "MEK1_2"}, + { "Selumetinib", "MEK1_2"}, + { "Pictilisib", "PI3K"}, + { "Alpelisib", "PI3K"}, + { "BIBR1532", "TERT"} +}; + +vector> half_lives = { + { "Ipatasertib", 2748}, + { "Afuresertib", 2448}, + { "Afatinib", 2220}, + { "Erlotinib", 2172}, + { "Ulixertinib", 105}, + { "Luminespib", 7200}, + { "Trametinib", 5760}, + { "Selumetinib", 822}, + { "Pictilisib", 1062}, + { "Alpelisib", 822}, + +}; + +// Update function definitions to match declarations +string get_value(const vector>& dict, const string& key) { + vector>::const_iterator dict_iterator = + find_if(dict.begin(), dict.end(), + [&key](const pair& element) -> bool { + return element.first == key; + }); + if (dict_iterator != dict.end()) { + return dict_iterator->second; + } + return ""; // or throw an exception +} + +int get_value(const vector>& dict, const string& key) { + vector>::const_iterator dict_iterator = + find_if(dict.begin(), dict.end(), + [&key](const pair& element) -> bool { + return element.first == key; + }); + if (dict_iterator != dict.end()) { + return dict_iterator->second; + } + return -1; // or throw an exception +} + +// string get_value (const vector> dict, string key) { + // vector< pair>::const_iterator dict_iterator = find_if( dict.begin(), dict.end(),[&key](const pair < string, string>& element){ return element.first == key;} ); + // return (*dict_iterator).second; +// } + +// int get_value (const vector> dict, string key) { +// vector< pair>::const_iterator dict_iterator = find_if( dict.begin(), dict.end(),[&key](const pair < string, int>& element){ return element.first == key;} ); +// return (*dict_iterator).second; +// } + + +vector get_drug_sensitivity_values (string drug_name) { + double max_conc = parameters.doubles(drug_name+"_maxc"); + double xmid = parameters.doubles(drug_name+"_xmid"); + double scale = parameters.doubles(drug_name+"_scal"); + + return {max_conc, xmid, scale}; +} + + +double get_drug_concentration_from_IC (string cell_line, string drug_name, string IC_value, int simulation_mode) { + std::stringstream ss; + ss << IC_value[3] << "." << IC_value[2]; + double inhibition_value = stod(ss.str()); + std::cout << "Inhibition value for "<< drug_name << " is: " << inhibition_value << '\n'; + double final_viability = 1.0 - inhibition_value; + + // call functions to retrieve data from datastructure cell line and drug + vector drug_sens_vals = get_drug_sensitivity_values(drug_name); + // call linear_mixed_model_function + double max_conc = drug_sens_vals[0]; + double xmid = drug_sens_vals[1]; + double scale = drug_sens_vals[2]; + + // get the drug concentration for the cell viability + double x = get_x_for_cell_viability(xmid, scale, final_viability); + double drug_conc = get_conc_from_x(x, max_conc); + + return drug_conc; +} + +// returns x: the concentration scaled for 9 different concentrations +double get_x_from_conc(double x_conc, double max_conc) { + double x = (log (x_conc / max_conc) / log (2) ) + 9; + return(x); +} + +// returns conc: concentration in micromolar +double get_conc_from_x (double x, double max_conc) { + double x_conc = max_conc * pow(2 , (x - 9)); + return(x_conc); +} + +// returns the natural logarithm for x +double get_lx_from_x (double x, double max_conc) { + double lx = log (get_conc_from_x(x, max_conc)); + return(lx); +} + +// two parameter logistic function used to model GDSC dose response data +// this model is described further in published in Vis, D.J. et al Pharmacogenomics 2016, 17(7):691-700) +// double logist3 (double x, double xmid, double scale) { +// //https://github.com/CancerRxGene/gdscIC50/blob/master/R/nlme_fit.R + +// return; +// } + + +// this function then has to be called whenever we translate concentration into maboss +// check in the datastructure for cell line and drug for the important values and enter concentration +// then we receive a y_hat that we can use to calculate the maboss activity +double get_cell_viability_for_x(double x, double max_conc, double xmid, double scale) +{ + // double y_hat = 1 - logist3(x, xmid, scale); + double y_hat = 1/(1 + pow( exp(1), (x- xmid) /scale)); + return(y_hat); +} + + +double get_x_for_cell_viability (double xmid, double scale, double cell_viability) { + double x = (log((1/cell_viability) - 1) * scale) + xmid; + return x; +} + +double get_cell_viability_for_drug_conc (double drug_conc, string cell_line, string drug_name) +{ + //std::cout << "Concentration of " << drug_name << ": " << drug_conc << std::endl; + // call functions to retrieve data from datastructure cell line and drug + vector drug_sens_vals = get_drug_sensitivity_values(drug_name); + // call linear_mixed_model_function + double max_conc = drug_sens_vals[0]; + double xmid = drug_sens_vals[1]; + double scale = drug_sens_vals[2]; + // transfor drug concentration into lx + double x = get_x_from_conc(drug_conc, max_conc); + double y_hat = get_cell_viability_for_x(x, max_conc, xmid, scale); + // return cell viability value y_hat + //std::cout << "Cell viability for " << drug_name << ": " << y_hat << std::endl; + return y_hat; +} + + + diff --git a/custom_modules/drug_sensitivity.h b/custom_modules/drug_sensitivity.h new file mode 100644 index 0000000..4febc44 --- /dev/null +++ b/custom_modules/drug_sensitivity.h @@ -0,0 +1,44 @@ +#include +#include +#include +#include // std::pair +#include // std::runtime_error +#include // std::stringstream +#include +#include +#include +#include "../core/PhysiCell.h" +#include "../modules/PhysiCell_standard_modules.h" + +/** + * \main rdrug:sensitivity + * \brief Custom module file for prostate example + * + * \details Modules needed for the prostate example. This custom module can be used to study the inhibition of prostate cell lines + * + * + * \date 19/10/2020 + * \author Annika Meert, BSC-CNS + */ + +using namespace std; +using namespace PhysiCell; +using namespace BioFVM; + +extern vector> drug_targets; +extern vector> half_lives; + +// Function declarations +string get_value(const vector>& dict, const string& key); +int get_value(const vector>& dict, const string& key); + +vector get_drug_sensitivity_values (string drug_name); +double get_drug_concentration_from_level (string cell_line, string drug_name, int conc_level, int num_of_conc_levels, int simulation_mode); +double get_drug_concentration_from_IC (string cell_line, string drug_name, string IC_value, int simulation_mode); + +double get_x_from_conc(double x_conc, double max_conc); +double get_conc_from_x (double x, double max_conc); +double get_lx_from_x (double x, double max_conc); +double get_x_for_cell_viability (double xmid, double scale, double cell_viability); +double linear_mixed_model_function(double x, double max_conc, double xmid, double scale); +double get_cell_viability_for_drug_conc (double drug_conc, string cell_line, string drug_name); \ No newline at end of file diff --git a/main.cpp b/main.cpp index e1dfd12..e07e6d2 100644 --- a/main.cpp +++ b/main.cpp @@ -1,70 +1,10 @@ -/* -############################################################################### -# If you use PhysiCell in your project, please cite PhysiCell and the version # -# number, such as below: # -# # -# We implemented and solved the model using PhysiCell (Version x.y.z) [1]. # -# # -# [1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, # -# PhysiCell: an Open Source Physics-Based Cell Simulator for Multicellu- # -# lar Systems, PLoS Comput. Biol. 14(2): e1005991, 2018 # -# DOI: 10.1371/journal.pcbi.1005991 # -# # -# See VERSION.txt or call get_PhysiCell_version() to get the current version # -# x.y.z. Call display_citations() to get detailed information on all cite-# -# able software used in your PhysiCell application. # -# # -# Because PhysiCell extensively uses BioFVM, we suggest you also cite BioFVM # -# as below: # -# # -# We implemented and solved the model using PhysiCell (Version x.y.z) [1], # -# with BioFVM [2] to solve the transport equations. # -# # -# [1] A Ghaffarizadeh, R Heiland, SH Friedman, SM Mumenthaler, and P Macklin, # -# PhysiCell: an Open Source Physics-Based Cell Simulator for Multicellu- # -# lar Systems, PLoS Comput. Biol. 14(2): e1005991, 2018 # -# DOI: 10.1371/journal.pcbi.1005991 # -# # -# [2] A Ghaffarizadeh, SH Friedman, and P Macklin, BioFVM: an efficient para- # -# llelized diffusive transport solver for 3-D biological simulations, # -# Bioinformatics 32(8): 1256-8, 2016. DOI: 10.1093/bioinformatics/btv730 # -# # -############################################################################### -# # -# BSD 3-Clause License (see https://opensource.org/licenses/BSD-3-Clause) # -# # -# Copyright (c) 2015-2022, Paul Macklin and the PhysiCell Project # -# All rights reserved. # -# # -# Redistribution and use in source and binary forms, with or without # -# modification, are permitted provided that the following conditions are met: # -# # -# 1. Redistributions of source code must retain the above copyright notice, # -# this list of conditions and the following disclaimer. # -# # -# 2. Redistributions in binary form must reproduce the above copyright # -# notice, this list of conditions and the following disclaimer in the # -# documentation and/or other materials provided with the distribution. # -# # -# 3. Neither the name of the copyright holder nor the names of its # -# contributors may be used to endorse or promote products derived from this # -# software without specific prior written permission. # -# # -# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # -# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # -# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # -# ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # -# LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # -# CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # -# SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # -# INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # -# CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # -# ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # -# POSSIBILITY OF SUCH DAMAGE. # -# # -############################################################################### -*/ +/** + * \main main-prostate file + * \brief Main file of the prostate example + * \date 20/08/2025 + * \author Arnau Montagud, Annika Meert, Gerard Pradas and Miguel Ponce de Leon, BSC-CNS + */ #include #include #include @@ -72,15 +12,16 @@ #include #include #include - #include "./core/PhysiCell.h" #include "./modules/PhysiCell_standard_modules.h" // put custom code modules here! -#include "./custom_modules/custom.h" -#include "./addons/PhysiBoSS/src/maboss_intracellular.h" - +#include "./addons/PhysiBoSS/src/maboss_intracellular.h" +#include "./custom_modules/custom.h" +#include "./custom_modules/drug_sensitivity.h" +#include "./custom_modules/boolean_model_interface.h" + using namespace BioFVM; using namespace PhysiCell; @@ -90,31 +31,51 @@ int main( int argc, char* argv[] ) bool XML_status = false; char copy_command [1024]; + char copy_command_2 [1024]; if( argc > 1 ) - { + { XML_status = load_PhysiCell_config_file( argv[1] ); sprintf( copy_command , "cp %s %s/PhysiCell_settings.xml" , argv[1] , PhysiCell_settings.folder.c_str() ); + sprintf( copy_command_2 , "cp %s %s" , argv[1] , PhysiCell_settings.folder.c_str() ); } else - { - XML_status = load_PhysiCell_config_file( "./config/PhysiCell_settings.xml" ); - sprintf( copy_command , "cp ./config/PhysiCell_settings.xml %s" , PhysiCell_settings.folder.c_str() ); + { + XML_status = load_PhysiCell_config_file( "./config/PhysiCell_settings_LNCaP.xml" ); + sprintf( copy_command , "cp ./config/PhysiCell_settings_LNCaP.xml %s" , PhysiCell_settings.folder.c_str() ); } if( !XML_status ) { exit(-1); } - + // copy config file to output directry system( copy_command ); + if ( argc > 1 ) + { + system( copy_command_2 ); + } + // OpenMP setup omp_set_num_threads(PhysiCell_settings.omp_num_threads); + // PNRG setup + SeedRandom( parameters.ints("random_seed") ); // or specify a seed here + // time setup std::string time_units = "min"; /* Microenvironment setup */ + // store each column of drug sensitivity csv file in a custom vector variable + // std::vector>> csv_file = read_csv( parameters.strings("drug_sensitivity_file") ); + // for (std::pair> &element : csv_file ) { + // if (element.first != "\"\""){ + // cell_defaults.custom_data.add_vector_variable( element.first, element.second ); + // } + // } + + /* Microenvironment setup */ setup_microenvironment(); // modify this in the custom code + /* PhysiCell setup */ @@ -142,6 +103,10 @@ int main( int argc, char* argv[] ) char filename[1024]; sprintf( filename , "%s/initial" , PhysiCell_settings.folder.c_str() ); save_PhysiCell_to_MultiCellDS_v2( filename , microenvironment , PhysiCell_globals.current_time ); + // save_PhysiCell_to_MultiCellDS_xml_pugi( filename , microenvironment , PhysiCell_globals.current_time ); + sprintf( filename , "%s/states_initial.csv", PhysiCell_settings.folder.c_str()); + MaBoSSIntracellular::save( filename); + // save a quick SVG cross section through z = 0, after setting its // length bar to 200 microns @@ -150,15 +115,18 @@ int main( int argc, char* argv[] ) // for simplicity, set a pathology coloring function - std::vector (*cell_coloring_function)(Cell*) = my_coloring_function; + std::vector (*cell_coloring_function)(Cell*) = paint_by_number_cell_coloring; + // std::string (*ECM_coloring_function)(double, double, double) = my_coloring_function_for_stroma; + // std::vector (*cell_coloring_function)(Cell*) = prolif_apoptosis_coloring; sprintf( filename , "%s/initial.svg" , PhysiCell_settings.folder.c_str() ); - SVG_plot( filename , microenvironment, 0.0 , PhysiCell_globals.current_time, cell_coloring_function ); + SVG_plot( filename , microenvironment, 0.0 , PhysiCell_globals.current_time, cell_coloring_function); sprintf( filename , "%s/legend.svg" , PhysiCell_settings.folder.c_str() ); create_plot_legend( filename , cell_coloring_function ); - + add_software_citation( "PhysiBoSS" , PhysiBoSS_Version , PhysiBoSS_DOI, PhysiBoSS_URL); + // SVG_plot( filename , microenvironment, 0.0 , PhysiCell_globals.current_time, cell_coloring_function ); display_citations(); @@ -183,7 +151,8 @@ int main( int argc, char* argv[] ) while( PhysiCell_globals.current_time < PhysiCell_settings.max_time + 0.1*diffusion_dt ) { // save data if it's time. - if( PhysiCell_globals.current_time > PhysiCell_globals.next_full_save_time - 0.5 * diffusion_dt ) + // if( PhysiCell_globals.current_time > PhysiCell_globals.next_full_save_time - 0.5 * diffusion_dt ) + if( fabs( PhysiCell_globals.current_time - PhysiCell_globals.next_full_save_time ) < 0.01 * diffusion_dt ) { display_simulation_status( std::cout ); if( PhysiCell_settings.enable_legacy_saves == true ) @@ -196,6 +165,9 @@ int main( int argc, char* argv[] ) sprintf( filename , "%s/output%08u" , PhysiCell_settings.folder.c_str(), PhysiCell_globals.full_output_index ); save_PhysiCell_to_MultiCellDS_v2( filename , microenvironment , PhysiCell_globals.current_time ); + sprintf( filename , "%s/states_%08u.csv", PhysiCell_settings.folder.c_str(), PhysiCell_globals.full_output_index); + MaBoSSIntracellular::save( filename); + } PhysiCell_globals.full_output_index++; @@ -203,7 +175,8 @@ int main( int argc, char* argv[] ) } // save SVG plot if it's time - if( PhysiCell_globals.current_time > PhysiCell_globals.next_SVG_save_time - 0.5 * diffusion_dt ) + // if( PhysiCell_globals.current_time > PhysiCell_globals.next_full_save_time - 0.5 * diffusion_dt ) + if( fabs( PhysiCell_globals.current_time - PhysiCell_globals.next_SVG_save_time ) < 0.01 * diffusion_dt ) { if( PhysiCell_settings.enable_SVG_saves == true ) { @@ -214,23 +187,22 @@ int main( int argc, char* argv[] ) PhysiCell_globals.next_SVG_save_time += PhysiCell_settings.SVG_save_interval; } } - // Configure treatments - treatment_function(); - + // treatment_function(); + // update the microenvironment microenvironment.simulate_diffusion_decay( diffusion_dt ); // run PhysiCell ((Cell_Container *)microenvironment.agent_container)->update_all_cells( PhysiCell_globals.current_time ); - + /* Custom add-ons could potentially go here. */ - + PhysiCell_globals.current_time += diffusion_dt; } - + if( PhysiCell_settings.enable_legacy_saves == true ) { log_output(PhysiCell_globals.current_time, PhysiCell_globals.full_output_index, microenvironment, report_file); @@ -246,9 +218,12 @@ int main( int argc, char* argv[] ) sprintf( filename , "%s/final" , PhysiCell_settings.folder.c_str() ); save_PhysiCell_to_MultiCellDS_v2( filename , microenvironment , PhysiCell_globals.current_time ); + sprintf( filename , "%s/states_final.csv", PhysiCell_settings.folder.c_str()); + MaBoSSIntracellular::save( filename ); sprintf( filename , "%s/final.svg" , PhysiCell_settings.folder.c_str() ); SVG_plot( filename , microenvironment, 0.0 , PhysiCell_globals.current_time, cell_coloring_function ); + // timer diff --git a/model.yml b/model.yml index 0245cd9..37481dc 100644 --- a/model.yml +++ b/model.yml @@ -1,5 +1,8 @@ -short_name: template-model +short_name: prostate version: 1.0.0 -binary: project +binary: prostate config: - - config/PhysiCell_settings.xml + - config/PhysiCell_settings_LNCaP.xml + - config/PhysiCell_settings_LNCaP_Luminespib_2.xml + - config/PhysiCell_settings_LNCaP_Pictilisib_2.xml + - config/PhysiCell_settings_LNCaP_Luminespib_Pictilisib_2.xml diff --git a/scripts/moviemaker.ipynb b/scripts/moviemaker.ipynb new file mode 100644 index 0000000..d39b48d --- /dev/null +++ b/scripts/moviemaker.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 17, + "id": "8ca35bfa", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import imageio\n", + "import cairosvg\n", + "import tempfile\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "92f57575", + "metadata": {}, + "outputs": [], + "source": [ + "# cairosvg.svg2png(url='../../../../output/initial.svg', write_to='../../../../output/initial.png')" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "f5700158", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/tmp/ipykernel_137330/3555308218.py:17: DeprecationWarning: Starting with ImageIO v3 the behavior of this function will switch to that of iio.v3.imread. To keep the current behavior (and make this warning disappear) use `import imageio.v2 as imageio` or call `imageio.v2.imread` directly.\n", + " images.append(imageio.imread(png_path))\n" + ] + } + ], + "source": [ + "# Path to the output folder containing SVGs\n", + "output_dir = \"../../../../output\"\n", + "\n", + "# Get all snapshot SVG files sorted numerically\n", + "svg_files = sorted(glob.glob(os.path.join(output_dir, \"snapshot*.svg\")))\n", + "\n", + "# Create a list to store images\n", + "images = []\n", + "\n", + "# Create a temporary directory for PNG conversions\n", + "with tempfile.TemporaryDirectory() as tmp_dir:\n", + " for svg_file in svg_files:\n", + " # Convert SVG to PNG\n", + " png_path = os.path.join(tmp_dir, os.path.basename(svg_file).replace('.svg', '.png'))\n", + " cairosvg.svg2png(url=svg_file, write_to=png_path)\n", + " # Read the PNG\n", + " images.append(imageio.imread(png_path))\n", + "\n", + " # Save the GIF\n", + " output_gif = os.path.join(output_dir, \"simulation.gif\")\n", + " imageio.mimsave(output_gif, images, duration=0.5, loop=0) # 0.5 seconds per frame\n", + "\n", + "# print(f\"Created GIF with {len(images)} frames at: {output_gif}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/scripts/time_course_cells.ipynb b/scripts/time_course_cells.ipynb new file mode 100644 index 0000000..c98c5f5 --- /dev/null +++ b/scripts/time_course_cells.ipynb @@ -0,0 +1,625 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### plot_time_course" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "#!/usr/bin/env python3\n", + "# coding: utf-8\n", + "\n", + "import os, re, sys\n", + "import glob, json\n", + "import argparse\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.io import loadmat\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from pctk import multicellds \n", + "\n", + "# import re, os, sys\n", + "# import numpy as np\n", + "# import pandas as pd\n", + "# import matplotlib.pyplot as plt\n", + "# from pctk import multicellds \n", + "\n", + "\n", + "# modules_path = \"../\"\n", + "# modules_path = os.path.dirname(os.path.realpath(__file__))\n", + "# modules_path= os.path.join(modules_path, 'scripts/')\n", + "# modules_path= os.path.join(modules_path, 'modules')\n", + "# sys.path.append(modules_path)\n", + "\n", + "sns.set(style=\"ticks\", palette=\"Paired\")\n", + "sns.set_context('paper')\n", + "\n", + "def create_parser():\n", + " parser = argparse.ArgumentParser(description=\"Plot total cell grouped as Alive/Necrotic/Apoptotic vs Time\")\n", + " \n", + " parser.add_argument(\"data_folder\", action=\"store\", help=\"folder were the data is stored\")\n", + " \n", + " parser.add_argument(\"--format\", action=\"store\", dest=\"format\", choices=(\"physicell\", \"physiboss\"),\n", + " help=\"Format of the input data\", default=\"physicell\")\n", + "\n", + " parser.add_argument(\"--figout\", action=\"store\", dest=\"fig_fname\", default=\"./cell_vs_time.png\",\n", + " help=\"File name to save the plot\")\n", + " \n", + " parser.add_argument(\"--csvout\", action=\"store\", dest=\"csv_fname\", default=None,\n", + " help=\"File name to store the summary table used for the plot\")\n", + "\n", + " return parser\n", + " \n", + "def pb_output_iterator(data_folder, sep=\";\"):\n", + " globing = os.path.join(data_folder, \"cells_[0-9]*.txt\")\n", + " for fname in sorted(glob.glob(globing)):\n", + " df = pd.read_csv(fname, sep=sep)\n", + " t = df.Time[0]\n", + " yield (t, df)\n", + "\n", + "def count_pb_files(data_folder):\n", + " globing = os.path.join(data_folder, \"cells_[0-9]*.txt\")\n", + " return len(glob.glob(globing))" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def get_timeserie_mean(mcds, filter_alive=True):\n", + " time = []\n", + " values = []\n", + " filter_alive = True\n", + " for t, df in mcds.cells_as_frames_iterator():\n", + " time.append(t)\n", + " df = df.iloc[:,3:]\n", + " if filter_alive:\n", + " mask = df['current_phase'] <= 14\n", + " df = df[mask]\n", + " values.append(df.mean(axis=0).values)\n", + " # if filter_alive:\n", + " # # Fix: Use boolean indexing properly\n", + " # mask = (df['current_phase'].astype(float) <= 14).values # Convert to numeric and create boolean mask\n", + " # df = df[mask]\n", + " # if len(df) > 0: # Check if any cells remain after filtering\n", + " # values.append(df.mean(axis=0).values)\n", + " # else:\n", + " # # Handle empty dataframe case\n", + " # empty_values = np.zeros(len(df.columns))\n", + " # values.append(empty_values)\n", + "\n", + " cell_columns = df.columns.tolist()\n", + " df = pd.DataFrame(values, columns=cell_columns)\n", + " df['time'] = time\n", + " return df[['time'] + cell_columns]\n", + "\n", + "\n", + "def get_timeserie_density(mcds):\n", + " data = []\n", + " for t,m in mcds.microenvironment_as_matrix_iterator():\n", + " data.append((t, m[5,:].sum()))\n", + " df = pd.DataFrame(data=data, columns=['time', 'drug_A_internal_density'])\n", + " return df\n", + "\n", + "def plot_molecular_model(df_cell_variables, list_of_variables, ax1):\n", + "\n", + " threshold = 0.5\n", + "\n", + " for label in list_of_variables:\n", + " y = df_cell_variables[label]\n", + " time = df_cell_variables[\"time\"]\n", + " ax1.plot(time, y, label= label)\n", + "\n", + " ax1.set_ylabel(\"Quantity\")\n", + " ax1.yaxis.grid(True)\n", + " # ax1.set_xlim((0,time.values[-1]))\n", + " # ax1.set_ylim((0,1))\n", + " # ax1.set_xlabel(\"time (min)\")\n", + " \n", + "def plot_cells(df_time_course, color_dict, ax):\n", + "\n", + " # Alive/Apoptotic/Necrotic vs Time\n", + " for k in color_dict:\n", + " axes[0].plot(df_time_course.time, df_time_course[k], \"-\", c=color_dict[k], label=k)\n", + " \n", + " # setting axes labels\n", + " # axes[0].set_xlabel(\"time (min)\")\n", + " ax.set_ylabel(\"Number of cells\")\n", + " \n", + " # Showing legend\n", + " ax.legend()\n", + " ax.yaxis.grid(True)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{0: 'Ki67_positive_premitotic', 1: 'Ki67_positive_postmitotic', 2: 'Ki67_positive', 3: 'Ki67_negative', 4: 'G0G1_phase', 5: 'G0_phase', 6: 'G1_phase', 7: 'G1a_phase', 8: 'G1b_phase', 9: 'G1c_phase', 10: 'S_phase', 11: 'G2M_phase', 12: 'G2_phase', 13: 'M_phase', 14: 'live', 100: 'apoptotic', 101: 'necrotic_swelling', 102: 'necrotic_lysed', 103: 'necrotic', 104: 'debris'}\n", + "{'Ki67_positive_premitotic': 'alive', 'Ki67_positive_postmitotic': 'alive', 'Ki67_positive': 'alive', 'Ki67_negative': 'alive', 'G0G1_phase': 'alive', 'G0_phase': 'alive', 'G1_phase': 'alive', 'G1a_phase': 'alive', 'G1b_phase': 'alive', 'G1c_phase': 'alive', 'S_phase': 'alive', 'G2M_phase': 'alive', 'G2_phase': 'alive', 'M_phase': 'alive', 'live': 'alive', 'apoptotic': 'apoptotic', 'necrotic_lysed': 'necrotic', 'necrotic_swelling': 'necrotic'}\n" + ] + } + ], + "source": [ + "# parser = create_parser()\n", + "# args = parser.parse_args()\n", + "# name = \"output_nodrug_new_dt6\"\n", + "name = \"output_lumi\"\n", + "# name = \"output_picti\"\n", + "# name = \"output_lumi_picti\"\n", + "data_folder = f\"../../../../{name}\"\n", + "# print (data_folder)\n", + "fig_fname = name\n", + "csv_fname = name\n", + "\n", + "phases_dict = multicellds.default_phases_dict\n", + "phase_grouping = multicellds.default_phase_grouping\n", + "print(phases_dict)\n", + "print(phase_grouping)\n", + "\n", + "# Globing output files according to the output format specified\n", + "# if args.format == 'physicell':\n", + "phase_col = \"current_phase\"\n", + "mcds = multicellds.MultiCellDS(output_folder=data_folder)\n", + "df_iterator = mcds.cells_as_frames_iterator()\n", + "num_of_files = mcds.cells_file_count()\n", + "# elif args.format == 'physiboss':\n", + "# phase_col = \"phase\"\n", + "# df_iterator = pb_output_iterator(args.data_folder)\n", + "# num_of_files = count_pb_files(args.data_folder)\n", + "\n", + "# Initializing a Pandas Databrafe to store the data\n", + "columns = [\"time\", \"alive\", \"apoptotic\", \"necrotic\"]\n", + "data = np.zeros((num_of_files, 4), dtype=int)\n", + "df_time_course = pd.DataFrame(columns=columns, data=data)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reading cell_output files from 35 input files from ../../../../output_lumi\n", + "\tProcessing time step: 0\n", + "\tProcessing time step: 240\n", + "\tProcessing time step: 480\n", + "\tProcessing time step: 720\n", + "\tProcessing time step: 960\n", + "\tProcessing time step: 1200\n", + "\tProcessing time step: 1680\n", + "\tProcessing time step: 1920\n", + "\tProcessing time step: 2160\n", + "\tProcessing time step: 2400\n", + "\tProcessing time step: 2640\n", + "\tProcessing time step: 2880\n", + "\tProcessing time step: 3120\n", + "\tProcessing time step: 3360\n", + "\tProcessing time step: 3600\n", + "\tProcessing time step: 3840\n", + "\tProcessing time step: 4080\n", + "\tProcessing time step: 4320\n", + "\tProcessing time step: 4560\n", + "\tProcessing time step: 4800\n", + "\tProcessing time step: 5040\n", + "\tProcessing time step: 5280\n", + "\tProcessing time step: 5520\n", + "\tProcessing time step: 5760\n", + "\tProcessing time step: 6000\n", + "\tProcessing time step: 6240\n", + "\tProcessing time step: 6480\n", + "\tProcessing time step: 6720\n", + "\tProcessing time step: 6960\n", + "\tProcessing time step: 7200\n", + "\tProcessing time step: 7440\n", + "\tProcessing time step: 7680\n", + "\tProcessing time step: 7920\n", + "\tProcessing time step: 8160\n", + "\tProcessing time step: 8400\n" + ] + } + ], + "source": [ + "print(\"Reading cell_output files from %i input files from %s\" % (num_of_files, data_folder))\n", + "# Iterating over all cell_output files\n", + "for i, (t, df) in enumerate(df_iterator):\n", + " print(\"\\tProcessing time step: %.0f\" % t)\n", + "\n", + " # Rename the phases integer codes using the phases_dict as the mapping\n", + " s = df[phase_col]\n", + " s.replace(to_replace=phases_dict, inplace=True)\n", + " \n", + " # Count the number of cells in each phase\n", + " counts = s.value_counts()\n", + "\n", + " df_time_course.loc[i, 'time'] = t\n", + " # group the previous phases count into the three general classes:\n", + " # Alive, Apoptotic, Necrotic\n", + " for k, v in counts.to_dict().items():\n", + " if k not in phase_grouping:\n", + " continue\n", + " df_time_course.loc[i, phase_grouping[k]] += v" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print(\"Finish processing files\") \n", + "# Set time column as the dataframe index\n", + "sns.set_context('paper')\n", + "patch_color = \"lightgrey\"\n", + "\n", + "# print(\"Creating figure\")\n", + "# Create a figure\n", + "# fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=300)\n", + "fig, axes = plt.subplots(3, 1, figsize=(12,12), dpi=150, sharex=True)\n", + "\n", + "# plot Alive vs Time\n", + "curve_params = {}\n", + "curve_params['alive'] = {'color': '#75db75', 'label': 'Alive'}\n", + "curve_params['apoptotic'] = {'color': '#ef4242', 'label': 'Apoptotic'}\n", + "curve_params['necrotic'] = {'color':'#97723d', 'label': 'Necrotic'}\n", + "line_width = 2.\n", + "for k,pdict in curve_params.items():\n", + " c = pdict['color']\n", + " l = pdict['label']\n", + " axes[0].plot(df_time_course.time, df_time_course[k], \"-\", c=c, label=l, linewidth=line_width)\n", + "\n", + "# setting axes labels\n", + "axes[0].set_xlabel(\"Time (min)\")\n", + "axes[0].set_ylabel(\"Number of cells\")\n", + "\n", + "axes[0].tick_params(axis='x', labelsize=12)\n", + "axes[0].tick_params(axis='y', labelsize=12)\n", + "\n", + "# Showing legend\n", + "axes[0].spines['right'].set_visible(False)\n", + "axes[0].spines['top'].set_visible(False)\n", + "axes[0].yaxis.grid(linestyle='dotted')\n", + "axes[0].legend()\n", + "\n", + "# fig.tight_layout()\n", + "# fig.savefig(fig_fname)\n", + "# print(\"Saving fig as %s\" % fig_fname)\n", + "\n", + "# if csv_fname:\n", + "# df_time_course.to_csv(csv_fname+\".csv\", sep=\"\\t\")\n", + "# print(\"Saving csv as %s\" % csv_fname)\n", + "\n", + "# name = \"output\"\n", + "\n", + "color_dict = {\"alive\": \"g\", \"apoptotic\": \"r\", \"necrotic\":\"k\"}\n", + "\n", + "# instance_folder = \"../../../../\"\n", + "# instance_folder = sys.argv[1]\n", + "\n", + "output_data = data_folder\n", + "\n", + "mcds = multicellds.MultiCellDS(output_folder=output_data)\n", + "\n", + "# df_time_course = mcds.get_cells_summary_frame()\n", + "# df_cell_variables = get_timeserie_mean(mcds)\n", + "# df_time_tnf = get_timeserie_density(mcds)\n", + "\n", + "# df_time_course.to_csv(instance_folder + name + \"_time_course.tsv\", sep=\"\\t\")\n", + "# df_cell_variables.to_csv(instance_folder + name + \"_cell_variables.tsv\", sep=\"\\t\")\n", + "# df_time_tnf.to_csv(instance_folder + name + \"_tnf_time.tsv\", sep=\"\\t\")\n", + "\n", + "# plot_cells(df_time_course, color_dict, axes[0])\n", + "\n", + "# list_of_variables = ['drug_A_internal_density', 'drug_A_external_density']\n", + "# plot_molecular_model(df_cell_variables, list_of_variables, axes[1])\n", + "# threshold = 0.5\n", + "\n", + "# axes[1].hlines(threshold, 0, df_time_course.time.iloc[-1], label=\"Activation threshold\")\n", + "# ax2 = axes[1].twinx()\n", + "# ax2.plot(df_time_tnf.time, df_time_tnf['drug_A_internal_density'], 'r', label=\"[drug_A]\")\n", + "# ax2.set_ylabel(\"[TNF]\")\n", + "# ax2.set_ylim([0, 1000])\n", + "# axes[1].legend(loc=\"center right\")\n", + "# ax2.legend(loc=\"upper right\")\n", + "\n", + "# list_of_variables = ['Proliferation_node', 'Apoptosis_node', 'Luminespib_node', 'Pictilisib_node']\n", + "# plot_molecular_model(df_cell_variables, list_of_variables, axes[2])\n", + "# axes[2].set_xlabel(\"time (min)\")\n", + "# axes[2].set_ylabel(\"Node mean activation\")\n", + "# ax2 = axes[2].twinx()\n", + "# ax2.plot(df_time_tnf.time, df_time_tnf['drug_A_internal_density'], 'r', label=\"[drug_A]\")\n", + "# axes[2].legend(loc=\"center right\")\n", + "# ax2.legend(loc=\"upper right\")\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'drug_A_internal_density'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pandas/core/indexes/base.py:3805\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3804\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 3805\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_engine\u001b[38;5;241m.\u001b[39mget_loc(casted_key)\n\u001b[1;32m 3806\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m err:\n", + "File \u001b[0;32mindex.pyx:167\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:191\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:234\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:242\u001b[0m, in \u001b[0;36mpandas._libs.index.IndexEngine._maybe_get_bool_indexer\u001b[0;34m()\u001b[0m\n", + "File \u001b[0;32mindex.pyx:134\u001b[0m, in \u001b[0;36mpandas._libs.index._unpack_bool_indexer\u001b[0;34m()\u001b[0m\n", + "\u001b[0;31mKeyError\u001b[0m: 'drug_A_internal_density'", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 65\u001b[0m\n\u001b[1;32m 58\u001b[0m \u001b[38;5;66;03m# df_time_course.to_csv(instance_folder + name + \"_time_course.tsv\", sep=\"\\t\")\u001b[39;00m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;66;03m# df_cell_variables.to_csv(instance_folder + name + \"_cell_variables.tsv\", sep=\"\\t\")\u001b[39;00m\n\u001b[1;32m 60\u001b[0m \u001b[38;5;66;03m# df_time_tnf.to_csv(instance_folder + name + \"_tnf_time.tsv\", sep=\"\\t\")\u001b[39;00m\n\u001b[1;32m 61\u001b[0m \n\u001b[1;32m 62\u001b[0m \u001b[38;5;66;03m# plot_cells(df_time_course, color_dict, axes[0])\u001b[39;00m\n\u001b[1;32m 64\u001b[0m list_of_variables \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrug_A_internal_density\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdrug_A_external_density\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m---> 65\u001b[0m plot_molecular_model(df_cell_variables, list_of_variables, axes[\u001b[38;5;241m1\u001b[39m])\n\u001b[1;32m 66\u001b[0m \u001b[38;5;66;03m# threshold = 0.5\u001b[39;00m\n\u001b[1;32m 67\u001b[0m \n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# axes[1].hlines(threshold, 0, df_time_course.time.iloc[-1], label=\"Activation threshold\")\u001b[39;00m\n\u001b[1;32m 69\u001b[0m ax2 \u001b[38;5;241m=\u001b[39m axes[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m.\u001b[39mtwinx()\n", + "Cell \u001b[0;32mIn[2], line 41\u001b[0m, in \u001b[0;36mplot_molecular_model\u001b[0;34m(df_cell_variables, list_of_variables, ax1)\u001b[0m\n\u001b[1;32m 38\u001b[0m threshold \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.5\u001b[39m\n\u001b[1;32m 40\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m label \u001b[38;5;129;01min\u001b[39;00m list_of_variables:\n\u001b[0;32m---> 41\u001b[0m y \u001b[38;5;241m=\u001b[39m df_cell_variables[label]\n\u001b[1;32m 42\u001b[0m time \u001b[38;5;241m=\u001b[39m df_cell_variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtime\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 43\u001b[0m ax1\u001b[38;5;241m.\u001b[39mplot(time, y, label\u001b[38;5;241m=\u001b[39m label)\n", + "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pandas/core/frame.py:4102\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 4100\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mnlevels \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 4101\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_getitem_multilevel(key)\n\u001b[0;32m-> 4102\u001b[0m indexer \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mget_loc(key)\n\u001b[1;32m 4103\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_integer(indexer):\n\u001b[1;32m 4104\u001b[0m indexer \u001b[38;5;241m=\u001b[39m [indexer]\n", + "File \u001b[0;32m~/anaconda3/lib/python3.12/site-packages/pandas/core/indexes/base.py:3812\u001b[0m, in \u001b[0;36mIndex.get_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m 3807\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(casted_key, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m 3808\u001b[0m \u001b[38;5;28misinstance\u001b[39m(casted_key, abc\u001b[38;5;241m.\u001b[39mIterable)\n\u001b[1;32m 3809\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28many\u001b[39m(\u001b[38;5;28misinstance\u001b[39m(x, \u001b[38;5;28mslice\u001b[39m) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m casted_key)\n\u001b[1;32m 3810\u001b[0m ):\n\u001b[1;32m 3811\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InvalidIndexError(key)\n\u001b[0;32m-> 3812\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merr\u001b[39;00m\n\u001b[1;32m 3813\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m 3814\u001b[0m \u001b[38;5;66;03m# If we have a listlike key, _check_indexing_error will raise\u001b[39;00m\n\u001b[1;32m 3815\u001b[0m \u001b[38;5;66;03m# InvalidIndexError. Otherwise we fall through and re-raise\u001b[39;00m\n\u001b[1;32m 3816\u001b[0m \u001b[38;5;66;03m# the TypeError.\u001b[39;00m\n\u001b[1;32m 3817\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_indexing_error(key)\n", + "\u001b[0;31mKeyError\u001b[0m: 'drug_A_internal_density'" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# print(\"Finish processing files\") \n", + "# Set time column as the dataframe index\n", + "sns.set_context('paper')\n", + "patch_color = \"lightgrey\"\n", + "\n", + "# print(\"Creating figure\")\n", + "# Create a figure\n", + "# fig, ax = plt.subplots(1, 1, figsize=(8,3), dpi=300)\n", + "fig, axes = plt.subplots(3, 1, figsize=(12,12), dpi=150, sharex=True)\n", + "\n", + "# plot Alive vs Time\n", + "curve_params = {}\n", + "curve_params['alive'] = {'color': '#75db75', 'label': 'Alive'}\n", + "curve_params['apoptotic'] = {'color': '#ef4242', 'label': 'Apoptotic'}\n", + "curve_params['necrotic'] = {'color':'#97723d', 'label': 'Necrotic'}\n", + "line_width = 2.\n", + "for k,pdict in curve_params.items():\n", + " c = pdict['color']\n", + " l = pdict['label']\n", + " axes[0].plot(df_time_course.time, df_time_course[k], \"-\", c=c, label=l, linewidth=line_width)\n", + "\n", + "# setting axes labels\n", + "axes[0].set_xlabel(\"Time (min)\")\n", + "axes[0].set_ylabel(\"Number of cells\")\n", + "\n", + "axes[0].tick_params(axis='x', labelsize=12)\n", + "axes[0].tick_params(axis='y', labelsize=12)\n", + "\n", + "# Showing legend\n", + "axes[0].spines['right'].set_visible(False)\n", + "axes[0].spines['top'].set_visible(False)\n", + "axes[0].yaxis.grid(linestyle='dotted')\n", + "axes[0].legend()\n", + "\n", + "# fig.tight_layout()\n", + "# fig.savefig(fig_fname)\n", + "# print(\"Saving fig as %s\" % fig_fname)\n", + "\n", + "# if csv_fname:\n", + "# df_time_course.to_csv(csv_fname+\".csv\", sep=\"\\t\")\n", + "# print(\"Saving csv as %s\" % csv_fname)\n", + "\n", + "# name = \"output\"\n", + "\n", + "color_dict = {\"alive\": \"g\", \"apoptotic\": \"r\", \"necrotic\":\"k\"}\n", + "\n", + "# instance_folder = \"../../../../\"\n", + "# instance_folder = sys.argv[1]\n", + "\n", + "output_data = data_folder\n", + "\n", + "mcds = multicellds.MultiCellDS(output_folder=output_data)\n", + "\n", + "# df_time_course = mcds.get_cells_summary_frame()\n", + "df_cell_variables = get_timeserie_mean(mcds)\n", + "df_time_tnf = get_timeserie_density(mcds)\n", + "\n", + "# df_time_course.to_csv(instance_folder + name + \"_time_course.tsv\", sep=\"\\t\")\n", + "# df_cell_variables.to_csv(instance_folder + name + \"_cell_variables.tsv\", sep=\"\\t\")\n", + "# df_time_tnf.to_csv(instance_folder + name + \"_tnf_time.tsv\", sep=\"\\t\")\n", + "\n", + "# plot_cells(df_time_course, color_dict, axes[0])\n", + "\n", + "list_of_variables = ['drug_A_internal_density', 'drug_A_external_density']\n", + "plot_molecular_model(df_cell_variables, list_of_variables, axes[1])\n", + "# threshold = 0.5\n", + "\n", + "# axes[1].hlines(threshold, 0, df_time_course.time.iloc[-1], label=\"Activation threshold\")\n", + "ax2 = axes[1].twinx()\n", + "ax2.plot(df_time_tnf.time, df_time_tnf['drug_A_internal_density'], 'r', label=\"[drug_A]\")\n", + "# ax2.set_ylabel(\"[TNF]\")\n", + "# ax2.set_ylim([0, 1000])\n", + "axes[1].legend(loc=\"center right\")\n", + "ax2.legend(loc=\"upper right\")\n", + "\n", + "list_of_variables = ['Proliferation_node', 'Apoptosis_node', 'Luminespib_node', 'Pictilisib_node']\n", + "plot_molecular_model(df_cell_variables, list_of_variables, axes[2])\n", + "axes[2].set_xlabel(\"time (min)\")\n", + "axes[2].set_ylabel(\"Node mean activation\")\n", + "# ax2 = axes[2].twinx()\n", + "# ax2.plot(df_time_tnf.time, df_time_tnf['drug_A_internal_density'], 'r', label=\"[drug_A]\")\n", + "axes[2].legend(loc=\"center right\")\n", + "# ax2.legend(loc=\"upper right\")\n", + "\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # fig, ax1 = plt.subplots(1, 1, figsize=(8,3), dpi=300)\n", + "\n", + "# list_of_variables = ['time','Proliferation_node', 'Apoptosis_node', 'TNFalpha_node']\n", + "\n", + "# df_nodes = df_cell_variables[list_of_variables]\n", + "\n", + "# fig, ax = plt.subplots(figsize=(10,6))\n", + "\n", + "# # Plot each node variable vs time\n", + "# a = 0\n", + "# for col in list_of_variables[1:]: # Skip 'time' column\n", + "# ax.plot(df_nodes['time'], df_nodes[col], label=col.replace('_node', ''))\n", + "# a=a+1\n", + "\n", + "\n", + "# # Customize plot\n", + "# ax.set_xlabel('Time (min)')\n", + "# ax.set_ylabel('Node Activation')\n", + "# ax.set_title('Boolean Node States Over Time')\n", + "# ax.grid(True, linestyle='--', alpha=0.7)\n", + "# ax.legend(loc='center right')\n", + "# plt.tight_layout()\n", + "# plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# def plot_columns_by_name(df, column_names, title=None, figsize=(10,6), ylabel=\"Value\"):\n", + "# \"\"\"\n", + "# Plot selected columns from a DataFrame\n", + " \n", + "# Args:\n", + "# df (pandas.DataFrame): DataFrame containing the data\n", + "# column_names (list): List of column names to plot\n", + "# title (str, optional): Plot title\n", + "# figsize (tuple, optional): Figure size (width, height)\n", + "# ylabel (str, optional): Y-axis label\n", + "# \"\"\"\n", + "# fig, ax = plt.subplots(figsize=figsize)\n", + " \n", + "# for col in column_names:\n", + "# if col in df.columns:\n", + "# ax.plot(df['time'], df[col], label=col)\n", + "# else:\n", + "# print(f\"Warning: Column '{col}' not found in DataFrame\")\n", + " \n", + "# ax.set_xlabel(\"Time (min)\")\n", + "# ax.set_ylabel(ylabel)\n", + "# ax.grid(True)\n", + " \n", + "# if title:\n", + "# ax.set_title(title)\n", + " \n", + "# ax.legend(loc='best')\n", + "# plt.tight_layout()\n", + "# return fig, ax\n", + "\n", + "# # Example usage:\n", + "# columns_to_plot = ['Proliferation_node']\n", + "# plot_columns_by_name(df_cell_variables, columns_to_plot, \n", + "# title=\"Node Activations Over Time\",\n", + "# ylabel=\"Node Activation\")\n", + "# plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# # df_time_course = mcds.get_cells_summary_frame()\n", + "# # df_cell_variables = get_timeserie_mean(mcds)\n", + "\n", + "# # instance_folder = \"../../../../output\"\n", + "# df_time_tnf = get_timeserie_density(mcds)\n", + "\n", + "# # df_time_course.to_csv(instance_folder + name + \"_time_course.tsv\", sep=\"\\t\")\n", + "# # df_cell_variables.to_csv(instance_folder + name + \"_cell_variables.tsv\", sep=\"\\t\")\n", + "# df_time_tnf.to_csv( name + \"_drug_time.tsv\", sep=\"\\t\")\n", + "# # plt.plot(df_time_tnf.time, df_time_tnf['drug_A'], 'r', label=\"[TNF]\")\n", + "\n", + "# fig, ax = plt.subplots(figsize=(8,3), dpi=300)\n", + "\n", + "# # Plot the data\n", + "# ax.plot(df_time_tnf.time, df_time_tnf['drug_A_internal_density'], 'r', label=\"[drug]\")\n", + "# # ax.set_yscale('log')\n", + "# # Add grid with log scale compatibility\n", + "# ax.grid(True, which=\"both\", ls=\"-\", alpha=0.2)\n", + "\n", + "# # Add labels and legend\n", + "# ax.set_xlabel(\"Time (min)\")\n", + "# ax.set_ylabel(\"Drug density\")\n", + "# ax.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +}