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

Arnab Mutsuddy; Cemal Erdem; Jonah R Huggins; Misha Salim; Daniel Cook; Nicole Hobbs; F Alex Feltus; Marc R Birtwistle

doi:10.1093/bioadv/vbad039

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

Bioinform Adv. 2023 Mar 23;3(1):vbad039. doi: 10.1093/bioadv/vbad039. eCollection 2023.

Authors

Arnab Mutsuddy¹, Cemal Erdem¹, Jonah R Huggins^{1

2}, Misha Salim³, Daniel Cook³, Nicole Hobbs³, F Alex Feltus⁴, Marc R Birtwistle^{1

5}

Affiliations

¹ Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.
² School of Computing, Clemson University, Clemson, SC, USA.
³ SimBioSys, Inc., Chicago, IL, USA.
⁴ Department of Genetics and Biochemistry, Clemson University, Clemson, SC, USA.
⁵ Department of Bioengineering, Clemson University, Clemson, SC, USA.

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).

Grants and funding

R35 GM141891/GM/NIGMS NIH HHS/United States