Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format.
Journal
Bioinformatics advances
ISSN: 2635-0041
Titre abrégé: Bioinform Adv
Pays: England
ID NLM: 9918282081306676
Informations de publication
Date de publication:
2023
2023
Historique:
received:
25
01
2023
revised:
25
02
2023
accepted:
22
03
2023
medline:
7
4
2023
entrez:
6
4
2023
pubmed:
7
4
2023
Statut:
epublish
Résumé
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).
Identifiants
pubmed: 37020976
doi: 10.1093/bioadv/vbad039
pii: vbad039
pmc: PMC10070034
doi:
Types de publication
Journal Article
Langues
eng
Pagination
vbad039Informations de copyright
© The Author(s) 2023. Published by Oxford University Press.
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