Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format.
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Mutsuddy A, Erdem C, Huggins JR, Salim M, Cook D, Hobbs N, Feltus FA, Birtwistle MR
Bioinform Adv 3 (1) vbad039 [2023-03-23; online 2023-03-23]
Large-scale and whole-cell modeling has multiple challenges, including scalable model building and module communication bottlenecks (e.g. between metabolism, gene expression, signaling, etc.). We previously developed an open-source, scalable format for a large-scale mechanistic model of proliferation and death signaling dynamics, but communication bottlenecks between gene expression and protein biochemistry modules remained. Here, we developed two solutions to communication bottlenecks that speed-up simulation by ∼4-fold for hybrid stochastic-deterministic simulations and by over 100-fold for fully deterministic simulations. Fully deterministic speed-up facilitates model initialization, parameter estimation and sensitivity analysis tasks. Source code is freely available at https://github.com/birtwistlelab/SPARCED/releases/tag/v1.3.0 implemented in python, and supported on Linux, Windows and MacOS (via Docker).
PubMed 37020976
DOI 10.1093/bioadv/vbad039
Crossref 10.1093/bioadv/vbad039
pmc: PMC10070034
pii: vbad039