Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-bas...
Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format
About this item
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Publisher
England: Oxford University Press
Journal title
Language
English
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Publication information
Publisher
England: Oxford University Press
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Scope and Contents
Contents
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)....
Alternative Titles
Full title
Computational speed-up of large-scale, single-cell model simulations via a fully integrated SBML-based format
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_swepub_primary_oai_DiVA_org_umu_218253
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_swepub_primary_oai_DiVA_org_umu_218253
Other Identifiers
ISSN
2635-0041
E-ISSN
2635-0041
DOI
10.1093/bioadv/vbad039
