Bayesian Optimization for Design of Multiscale Biological Circuits.

ABSTRACT: Recent advances in synthetic biology have enabled the construction of molecular circuits that operate across multiple scales of cellular organization, such as gene regulation, signaling pathways, and cellular metabolism. Computational optimization can effectively aid the design process, but current methods are generally unsuited for systems with multiple temporal or concentration scales, as these are slow to simulate due to their numerical stiffness. Here, we present a machine learning method for the efficient optimization of biological circuits across scales. The method relies on Bayesian optimization, a technique commonly used to fine-tune deep neural networks, to learn the shape of a performance landscape and iteratively navigate the design space toward an optimal circuit. This strategy

SUBMITTER: Merzbacher C 

PROVIDER: S-EPMC10367132 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature