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Advances in bioengineering and biomedicine demand a deep understanding of thedynamic behavior of biological systems, ranging from protein pathways tocomplex cellular processes. Biological networks like gene regulatory networksand protein pathways are key drivers of embryogenesis and physiologicalprocesses. Comprehending their diverse behaviors is essential for tacklingdiseases, including cancer, as well as for engineering novel biologicalconstructs. Despite the availability of extensive mathematical modelsrepresented in Systems Biology Markup Language (SBML), researchers facesignificant challenges in exploring the full spectrum of behaviors andoptimizing interventions to efficiently shape those behaviors. Existing toolsdesigned for simulation of biological network models are not tailored tofacilitate interventions on network dynamics nor to facilitate automateddiscovery. Leveraging recent developments in machine learning (ML), this paperintroduces SBMLtoODEjax, a lightweight library designed to seamlessly integrateSBML models with ML-supported pipelines, powered by JAX. SBMLtoODEjaxfacilitates the reuse and customization of SBML-based models, harnessing JAX'scapabilities for efficient parallel simulations and optimization, with the aimto accelerate research in biological network analysis.

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