okadalabipr / Imoto_Cancers_2020

Imoto, H., Zhang, S. & Okada, M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway. Cancers (Basel). 12, 2878 (2020).

Imoto_Cancers_2020

This repository contains modeling code for the following paper:

• Imoto, H.; Zhang, S.; Okada, M. A Computational Framework for Prediction and Analysis of Cancer Signaling Dynamics from RNA Sequencing Data—Application to the ErbB Receptor Signaling Pathway. Cancers 2020, 12, 2878. https://doi.org/10.3390/cancers12102878

The paper can be accessed at the Cancers website.

Description

• training/

  • Training model parameters using ParamEstim (v0.2.0).
  • Requirements

    • Sundials
    • SteadyStateDiffEq
    • StatsBase
    • CSV
    • DataFrames
    • PyPlot
    • Seaborn

• python/

  • Validation and prediction using BioMASS (v0.1.0).
  • Requirements

    • numpy
    • scipy
    • matplotlib
    • seaborn

• gene_expression/

  • CCLE RNA-seq gene expression data used for the model individualization

Usage

1. Parameter estimation

   $ cd trainig
   $ mkdir logs
   $ sh optimize_parallel.sh

2. Visualization of simulation results

   $ cd python

   import SKBR3
   from biomass import run_simulation
   run_simulation(SKBR3, viz_type='average', show_all=False, stdev=True)

3. Sensitivity analysis

   from biomass import run_analysis
   run_analysis(SKBR3, target='initial_condition', metric='integral', style='heatmap')

License

MIT