SBMLTOODEJAX: Efficient Simulation and Optimization of Biological Network Models in JAX - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2023

SBMLTOODEJAX: Efficient Simulation and Optimization of Biological Network Models in JAX

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.
Fichier principal
Vignette du fichier
2307.08452.pdf (504.31 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-04317246 , version 1 (01-12-2023)

Licence

Attribution

Identifiers

Cite

Mayalen Etcheverry, Michael Levin, Clément Moulin-Frier, Pierre-Yves Oudeyer. SBMLTOODEJAX: Efficient Simulation and Optimization of Biological Network Models in JAX. NeurIPS 2023 - AI4Science Workshop, Dec 2023, New Orleans (LA), France. ⟨hal-04317246⟩

Collections

INRIA INRIA2
38 View
26 Download

Altmetric

Share

Gmail Facebook X LinkedIn More