SBMLtoODEjax: Efficient Simulation and Optimization of Biological
Network Models in JAX
- URL: http://arxiv.org/abs/2307.08452v2
- Date: Sun, 29 Oct 2023 06:29:33 GMT
- Title: SBMLtoODEjax: Efficient Simulation and Optimization of Biological
Network Models in JAX
- Authors: Mayalen Etcheverry, Michael Levin, Cl\'ement Moulin-Frier, Pierre-Yves
Oudeyer
- Abstract summary: This paper introduces SBMLtoODEjax, a lightweight library designed to seamlessly integrate SBML models with ML-supported pipelines, powered by JAX.
It harnesses JAX's capabilities for efficient parallel simulations and optimization, with the aim to accelerate research in biological network analysis.
- Score: 19.55237447763145
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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