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Quantitative Biology > Biomolecules

arXiv:2307.08452 (q-bio)
[Submitted on 17 Jul 2023 (v1), last revised 29 Oct 2023 (this version, v2)]

Title:SBMLtoODEjax: Efficient Simulation and Optimization of Biological Network Models in JAX

Authors:Mayalen Etcheverry, Michael Levin, Clément Moulin-Frier, Pierre-Yves Oudeyer
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Abstract:Advances in bioengineering and biomedicine demand a deep understanding of the dynamic behavior of biological systems, ranging from protein pathways to complex cellular processes. Biological networks like gene regulatory networks and protein pathways are key drivers of embryogenesis and physiological processes. Comprehending their diverse behaviors is essential for tackling diseases, including cancer, as well as for engineering novel biological constructs. Despite the availability of extensive mathematical models represented in Systems Biology Markup Language (SBML), researchers face significant challenges in exploring the full spectrum of behaviors and optimizing interventions to efficiently shape those behaviors. Existing tools designed for simulation of biological network models are not tailored to facilitate interventions on network dynamics nor to facilitate automated discovery. Leveraging recent developments in machine learning (ML), this paper introduces SBMLtoODEjax, a lightweight library designed to seamlessly integrate SBML models with ML-supported pipelines, powered by JAX. SBMLtoODEjax facilitates the reuse and customization of SBML-based models, harnessing JAX's capabilities for efficient parallel simulations and optimization, with the aim to accelerate research in biological network analysis.
Subjects: Biomolecules (q-bio.BM); Machine Learning (cs.LG); Programming Languages (cs.PL)
Cite as: arXiv:2307.08452 [q-bio.BM]
  (or arXiv:2307.08452v2 [q-bio.BM] for this version)
  https://doi.org/10.48550/arXiv.2307.08452

Submission history

From: Mayalen Etcheverry [view email]
[v1] Mon, 17 Jul 2023 12:47:33 UTC (189 KB)
[v2] Sun, 29 Oct 2023 06:29:33 UTC (367 KB)
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