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Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format
Umeå University, Faculty of Medicine, Department of Medical Biosciences, Pathology. Department of Chemical and Biomolecular Engineering, Clemson University, Clemson, SC, USA.ORCID iD: 0000-0003-3663-3646
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2023 (English)In: Bioinformatics Advances, ISSN 2635-0041, Vol. 3, no 1, article id vbad039Article in journal (Refereed) Published
Abstract [en]

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).

Place, publisher, year, edition, pages
Oxford University Press, 2023. Vol. 3, no 1, article id vbad039
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Bioinformatics and Computational Biology
Identifiers
URN: urn:nbn:se:umu:diva-218253DOI: 10.1093/bioadv/vbad039OAI: oai:DiVA.org:umu-218253DiVA, id: diva2:1820884
Funder
NIH (National Institutes of Health), R35GM141891Available from: 2023-12-19 Created: 2023-12-19 Last updated: 2025-02-07Bibliographically approved

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Erdem, Cemal

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CiteExportLink to record
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