Nathaniel Linden-Santangeli

University of California San Diego 2024

Repository for: Increasing certainty in systems biology models using Bayesian multimodel inference

Authors: Nathaniel Linden-Santangeli, Jin Zhang, Boris Kramer, Padmini Rangamani

Date: June 2024

This repository provides all code and data that is necessary to reproduce all results shown in the manuscript available at [URL GOES HERE].

Instructions to reproduce results and figures

Source code is located in the src/MAPK/ directory and results and data are located in the results/ directory.

Local Identifiability analysis

The Julia code to run identifiability analysis for each model is located in src/MAPK/identifiability/. Each script runs the analysis for every model independently.

Supplemental Figure S1 (Global sensitivity analysis)

1. Run src/MAPK/gsa/gsa_sampling.sh to generate samples of model outputs.
2. Run src/MAPK/gsa/gsa_analyze.py to perform Morris analysis and make plots.

Synthetic data generation and Keyes et al. eLife. 2020. data normalization.

All synthetic data generation and preprocessing occurs in src/MAPK/process_data.ipynb.

Figure 2, 3; Supplemental Figures S2 and S5 (Synthetic dose-response data)

Figure 2

0. (optional to rerun SMC) Run src/MAPK/param_est/HF96_DR_inference.sh without the --skip_sample flag on each call. Rerunning with this flag enabled regenerates posterior predictive samples from previous SMC results
1. Run src/MAPK/param_est/HF96_DR_analyze.py to plot posterior predictive trajectories
2. Run src/MAPK/multimodel_inference/HF96_DR_MMI.ipynb to perform MMI, run error analysis, and make plots

Figure 3

1. Run src/MAPK/multimodel_inference/HF95_DR_model_perturb.ipynb for panels X, Y, Z
2. Run src/MAPK/multimodel_inference/HF95_DR_model_combinatorics.ipynb for panels X, Y, Z

Supplemental Figures S3, S4, and S6 (Synthetic trajectory data)

Supplemental Figure S3, S4

0. (optional to rerun SMC) Run src/MAPK/param_est/HF96_traj_inference.sh without the --skip_sample flag on each call. Rerunning with this flag enabled regenerates posterior predictive samples from previous SMC results
1. Run src/MAPK/param_est/HF96_traj_analyze.py to plot posterior predictive trajectories
2. Run src/MAPK/multimodel_inference/HF96_traj_MMI.ipynb to perform MMI, run error analysis, and make plots

Supplemental Figure S6

1. Run src/MAPK/multimodel_inference/HF95_traj_model_perturb.ipynb for panels X, Y, Z
2. Run src/MAPK/multimodel_inference/HF95_traj_model_combinatorics.ipynb for panels X, Y, Z

Figure 4 and Supplemental Figure S7 and S8 (Keyes et al. eLife. 2020. data shortening and quality reduction

data shortening

0. (optional to rerun SMC) Run src/MAPK/param_est/Keyes_CYTO_10min_inference.sh, src/MAPK/param_est/Keyes_CYTO_20min_inference.sh, src/MAPK/param_est/Keyes_CYTO_30min_inference.sh, src/MAPK/param_est/Keyes_PM-10min_inference.sh, src/MAPK/param_est/Keyes_PM-20min_inference.sh, and src/MAPK/param_est/Keyes_PM-30min_inference.sh without the --skip_sample flag on each call. Rerunning with this flag enabled regenerates posterior predictive samples from previous SMC results
1. Run src/MAPK/param_est/Keyes_data_len_analyze.py and src/MAPK/param_est/Keyes_analyze.py to plot posterior predictive trajectories
2. Run src/MAPK/multimodel_inference/Keyes_MMI.ipynb to perform MMI, run error analysis, and make plots

data quality reduction

1. [Requires SMC rerun--possible long runtime] Run src/MAPK/param_est/Keyes_data_quality_inference.py
2. Run src/MAPK/multimodel_inference/Keyes_data_quality_MMI.ipynb

Supplemental Figures S9 (Keyes et al. eLife. 2020. data inference/MMI)

0. (optional to rerun SMC) Run src/MAPK/param_est/Keyes_CYTO_inference.sh and src/MAPK/param_est/Keyes_PM_inference.sh without the --skip_sample flag on each call. Rerunning with this flag enabled regenerates posterior predictive samples from previous SMC results
1. Run src/MAPK/param_est/Keyes_analyze.py and src/MAPK/param_est/Keyes_data_len_analyze.py to plot posterior predictive trajectories
2. Run src/MAPK/multimodel_inference/Keyes_MMI.ipynb to perform MMI, run error analysis, and make plots
3. Run src/MAPK/param_est/Keyes_data_params_compDiff_analyze.ipynb to plot marginal eCDFs

Figure 5 and Supplemental Figure S10 (Keyes et al. eLife. 2020. Rap1/ERK negative feedback modifications)

0. (optional to rerun SMC) Run src/MAPK/param_est/Keyes_Rap1_negFeed_inference.sh without the --skip_sample flag on each call. Rerunning with this flag enabled regenerates posterior predictive samples from previous SMC results
1. Run src/MAPK/param_est/Keyes_Rap1_negFeed_analyze.py to plot posterior predictive trajectories
2. Run src/MAPK/multimodel_inference/Keyes_Rap1_negFeed_MMI.ipynb to perform MMI, run error analysis, and make plots

PSIS-LOO-CV vs. LOO-CV

The notebook src/MAPK/LOOCV_analyze.ipynb compares PSIS-LOO-CV to LOO-CV.

Dependencies

Julia

Julia version 1.10.0 is used for local identifiability analysis.

Packages

• StructuralIdentifiability.jl - v0.5.6
• DifferentialEquations.jl - v7.10.0

Python

Python version 3.11.6 is used for all other analysis.

Packages

• Jax - 0.4.25 (CPU computing)
• Jaxlib - 0.4.20 (CPU computing)
• numpy - 1.26.1
• pandas - 2.1.2
• matplotlib - 3.8.1
• seaborn - 0.13.0
• PyMC - 5.10.4
• numpyro - 0.13.2
• pytensor - 2.18.6
• ArViz - 0.16.1
• Preliz - 0.3.6
• Diffrax - 0.5.0
• Optimistix - 0.0.6
• Lineax - 0.0.4
• tqdm - 4.66.1
• func_timeout - 4.3.5