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

Erdem, Cemal; Mutsuddy, Arnab; Bensman, Ethan M.; Dodd, William B.; Saint-Antoine, Michael M.; Bouhaddou, Mehdi; Blake, Robert C.; Gross, Sean M.; Heiser, Laura M.; Feltus, F. Alex; Birtwistle, Marc R.

doi:10.1038/s41467-022-31138-1

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

Journal Article · 20 June 2022 · Nature Communications

DOI: https://doi.org/10.1038/s41467-022-31138-1 · OSTI ID:1883443

^[1]; Mutsuddy, Arnab ^[1]; Bensman, Ethan M. ^[1];

^[1]; Saint-Antoine, Michael M. ^[2]; Bouhaddou, Mehdi ^[3]; Blake, Robert C. ^[4]; Gross, Sean M. ^[5];

^[5];

^[1]; Birtwistle, Marc R. ^[1]

Clemson Univ., SC (United States)
Univ. of Delaware, Newark, DE (United States)
Univ. of California, San Francisco, CA (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Oregon Health & Science Univ., Portland, OR (United States)

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.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: National Institutes of Health (NIH); National Science Foundation (NSF); USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1883443

Report Number(s):: LLNL-JRNL-816326; 1025869

Journal Information:: Nature Communications, Journal Name: Nature Communications Journal Issue: 1 Vol. 13; ISSN 2041-1723

Publisher:: Nature Publishing GroupCopyright Statement

Country of Publication:: United States

Language:: English

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The Cellosaurus, a Cell-Line Knowledge Resource Bairoch, Amos Journal of Biomolecular Techniques : JBT, Vol. 29, Issue 2 https://doi.org/10.7171/jbt.18-2902-002	journal	July 2018

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