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.

翻译：生物工程与生物医学的进步要求深入理解生物系统的动态行为，涵盖从蛋白质通路到复杂细胞过程等多个层面。基因调控网络和蛋白质通路等生物学网络是胚胎发生和生理过程的关键驱动因素。理解其多样化行为对于应对包括癌症在内的疾病以及设计新型生物构建体至关重要。尽管系统生物学标记语言（SBML）已提供大量数学模型，研究人员在探索行为的完整谱系并优化干预手段以高效塑造这些行为时仍面临重大挑战。现有针对生物学网络模型模拟的工具并非为促进网络动态干预或自动化发现而设计。本文利用机器学习（ML）的最新进展，推出SBMLtoODEjax——一个轻量级库，旨在通过JAX驱动的计算能力，将SBML模型与ML支持的工作流程无缝集成。SBMLtoODEjax促进基于SBML模型的复用与定制，借助JAX高效并行模拟与优化的能力，加速生物学网络分析研究。