Computational Synthetic Biology Enabled through JAX: A Showcase.

ABSTRACT: Mathematical modeling is indispensable in synthetic biology but remains underutilized. Tackling problems, from optimizing gene networks to simulating intracellular dynamics, can be facilitated by the ever-growing body of modeling approaches, be they mechanistic, stochastic, data-driven, or AI-enabled. Thanks to progress in the AI community, robust frameworks have emerged to enable researchers to access complex computational hardware and compilation. Previously, these frameworks focused solely on deep learning, but they have been developed to the point where running different forms of computation is relatively simple, as made possible, notably, by the JAX library. Running simulations at scale on GPUs speeds up research, which compounds enable larger-scale experiments and greater usability o

SUBMITTER: Gallup O 

PROVIDER: S-EPMC11421211 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature