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

Cemal Erdem (), Arnab Mutsuddy, Ethan M. Bensman, William B. Dodd, Michael M. Saint-Antoine, Mehdi Bouhaddou, Robert C. Blake, Sean M. Gross, Laura M. Heiser, F. Alex Feltus and Marc R. Birtwistle ()
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Cemal Erdem: Clemson University
Arnab Mutsuddy: Clemson University
Ethan M. Bensman: Clemson University
William B. Dodd: Clemson University
Michael M. Saint-Antoine: University of Delaware
Mehdi Bouhaddou: University of California San Francisco
Robert C. Blake: Lawrence Livermore National Laboratory
Sean M. Gross: Oregon Health & Science University
Laura M. Heiser: Oregon Health & Science University
F. Alex Feltus: Clemson University
Marc R. Birtwistle: Clemson University

Nature Communications, 2022, vol. 13, issue 1, 1-18

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-31138-1

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