This is a pipeline to generate lineage resolved cell population simulations using the SPARCED single cell model.

Requirements:

1. OpenMPI for Ubuntu/ MS-MPI for Microsoft Windows
2. Python 3.9 or higher Required python packages:
   • amici = 0.11.18
   • mpi4py = 3.1.3
   • scipy = 1.6.2
   • antimony = 2.12.0.3
   • python-libsbml = 5.19
   • biopython = 1.81
   • plotly = 5.15
   • seaborn = 0.11.2

Instructions for replicating results from the paper:

1. Verify input files
   • Species.txt
   • Ratelaws.txt
   • OmicsData.txt
   • GeneReg.txt
   • StoicMat.txt For detailed instruction on the input files visit: https://github.com/birtwistlelab/SPARCED
2. Go to scripts directory and run createModel.py to build model. Verify model sbml file (SPARCED.xml) and AMICI-compiled model (SPARCED folder in the main directory).
3. Once the model build process is complete, MPI can be used to run cell population simulations using the following command:
   mpirun -np [n_cpu] python cellpop_drs.py --arguments [argument_value]
   Here, n_cpu is the number of MPI processes that the user decides to use for parallelization.
4. Simulation code accepts the following command line arguments:
   • sim_name: An arbitrary string defined by the user to create a directory under sparced/output where simulation outputs will be saved.
   • cellpop: An integer specifying the number of starting cells for simulation
   • exp_time: Duration of experiment in hours
   • drug: String specifying species name for the drug of interest (alpel_EC, nerat_EC, trame_EC, palbo_EC)
   • rep: String identifier for the current replicate
   • dose: Applied concentration of the drug in μM
   • egf: Serum EGF concentration in nM (optional)
   • ins: Serum INS concentration in nM (optional)
   • hgf: Serum HGF concentration in nM (optional)
   • nrg: Serum Heregulin concentration in nM (optional)
   • pdgf: Serum PDGF concentration in nM (optional)
   • igf: Serum IGF concentration in nM (optional)
   • fgf: Serum FGF concentration in nM (optional)
     For example, to run the first replicate of cell population simulation with 0.003162 μM dose of trametinib for 72 hours, with 100 starting cells, using the name 'in_silico_drs' and 16 CPUs, the following command can be entered: mpirun -np 16 python cellpop_drs.py --sim_name in_silico_drs --cellpop 100 --exp_time 72 --drug trame_EC --dose 0.003162 --rep rep1
     Upon completion of simulations, the results are saved to disk in a folder structure corresponding to drug name, replicate identifier and drug dose respectively. For a single simulation with a specific replicate of a drug dose, outputs (temporal species trajectories) from all cells in each generation are saved in a python pickle object.
5. To generate all drug dose response simulation data, run cellpop_drs.py for:
   • 4 drugs (alpelisib, neratinib, trametinib, palbociclib)
   • 10 dose levels (including control)
   • 10 replicates of each dose
6. To generate cell population dynamics (number of alive cells over time) from dose response simulation outputs, run analysis_popdyn.py For this, results from all drug dose response simulations in step 5 need to be placed in the "output" folder in the main directory. Alternatively, outputs may be placed at a secondary locations and the path must be updated in line 68 of analysis_popdyn.py script. Outputs for the cell population dynamics will be saved in the "in_silico_drs_summary" folder under the output directory.
7. To calculate GR score from the cell population dynamics, input files must be prepared for the gr-score calculation pipeline. For this, step 6 must be completed first. After this, run analysis_grscore.py to generate the gr-score input file, which will be saved as 'drs_grcalc.tsv' in the "in_silico_drs_summary" folder.
8. Take the input file generated at step 7 and run the gr-score calculation pipeline. For this:
   • 8a. Clone the gr-score git repository: https://github.com/datarail/gr_metrics
   • 8b. Install all dependencies including Python 2.0
   • 8c. Go to gr_metrics/SRC/python/scripts
   • 8d. Run python add_gr_column.py [input_path] > [output_path]
9. Download all experimental dose response datasets (GR-scores) from here: https://www.synapse.org/#!Synapse:syn18456348/ and place them in 'in_silico_drs_summary/mcf10a_drs_exp'
10. Plots from figures 1,2 can be generated with jupyter notebooks included in the 'jupyter_notebooks' folder.