Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format

• Published in: Bioinformatics advances Vol. 3; no. 1; p. vbad039
• Main Authors: Mutsuddy, Arnab, Erdem, Cemal, Huggins, Jonah R, Salim, Misha, Cook, Daniel, Hobbs, Nicole, Feltus, F Alex, Birtwistle, Marc R
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 2023
• Subjects: Application Note
• ISSN: 2635-0041; 2635-0041
• DOI: 10.1093/bioadv/vbad039

Abstract Summary 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. Availability and implementation 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).