A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

Abstract

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

Predicting Anti-Cancer Drug Combination Responses with a Temporal Cell State Network Model

“…In other cases, such mapping may not be known a priori and/or more complex, i.e., a single drug may influence multiple transition rates. Biochemical network models that capture such complexities or mapping may prove useful in such situations [26][27][28][29][30]45,78 . Assumptions regarding the additivity (or not) of multi-drug action on transition rates would have to be asserted.…”

“…In principle, more comprehensive exploration of drug combination space could be achieved in silico. Various computational methods including mechanistic models and machine learning approaches have shown promise in predicting drug combination responses, especially taking into consideration context specific pathology and omics data as well as identifying specific biomarkers and drugtargets [44][45][46][47][48][49] . Regardless of the modeling methods being used, there is a widespread focus on using information about biochemical networks to facilitate drug combination response prediction 31,[50][51][52] .…”

Expanding large-scale mechanistic models with machine learned associations and big datasets

“…The starting mechanistic model used in this work is obtained from the SPARCED repository (github.com/birtwistlelab/SPARCED/tree/develop) (17). It is a recent framework for large-scale mechanistic modeling that enables model file creation using simple text files as input with minimal coding requirements.…”

“…Then, Jupyter notebooks are used to process these files and create community-standard model file type called SBML (21,22). The software was first built to replicate the largest mammalian single-cell mechanistic model of proliferation and death signaling (9,17). Then, an expanded SPARCED model was created to include IFNγ signaling sub-module and the new model was named as SPARCED-I-SOCS1 (17).…”

Computational Speed-Up of Large-Scale, Single-Cell Model Simulations Via a Fully-Integrated SBML-Based Format

“…Algorithmic developments included rule-based modeling to specify reactions more compactly (Faeder et al ., 2009), and model composition tools, (Lopez et al ., 2013; Gyori et al .,2017; Hoops et al ., 2006; Somogyi et al ., 2015) but large-scale models often still presented challenges. More recent work has provided such tools like AMICI that enables SBML-specified models to be simulated quickly, PEtab (Stapor et al ., 2018; Schmiester et al ., 2021) and Datanator (Roth et al ., 2021) that specifies data formats for parameter estimation, formalisms that can help with unambiguous species naming, (Lang et al ., 2020) and composition approaches such as ours that simplify model aggregation and expansion in ways that are compatible with efficient large-scale simulation algorithms and easy to reuse (Erdem et al ., 2022). Not unexpectedly, however, there remains much work to be done to even technically enable large-scale and whole-cell modeling.…”