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- Title
A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.
- Authors
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.
- 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. Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. Here the authors develop a scalable, open-source pipeline for constructing and simulating large-scale, single-cell mechanistic models, an important building block for clinically-predictive mechanistic models and interpretable big data integration.
- Subjects
CELL proliferation; EPIDERMAL growth factor receptors; DATA integration; TEXT files; BIG data; CELL death
- Publication
Nature Communications, 2022, Vol 13, Issue 1, p1
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-022-31138-1