Bayesian Optimization for Design of Multiscale Biological Circuits

Charlotte Merzbacher, Oisin Mac Aodha, Diego a. Oyarzún

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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 allows the joint optimization of both circuit architecture and parameters, and provides a feasible approach to solve a highly nonconvex optimization problem in a mixed-integer input space. We illustrate the applicability of the method on several gene circuits for controlling biosynthetic pathways with strong nonlinearities, multiple interacting scales, and using various performance objectives. The method efficiently handles large multiscale problems and enables parametric sweeps to assess circuit robustness to perturbations, serving as an efficient in silico screening method prior to experimental implementation.
Original languageEnglish
Pages (from-to)2073-2082
Number of pages10
JournalACS Synthetic Biology
Volume12
Issue number7
Early online date20 Jun 2023
DOIs
Publication statusPublished - 21 Jul 2023

Keywords / Materials (for Non-textual outputs)

  • Bayesian optimization
  • dynamic pathway control
  • genetic circuit design
  • machine learning
  • metabolic engineering
  • multiscale biological systems

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