Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning

TL;DR

This paper introduces a reinforcement learning-based sequential framework for designing genetic circuits that efficiently manages uncertainty from biological stochasticity and experimental variability.

Contribution

It presents an amortized RL approach that enables immediate adaptation without explicit parameter inference, improving design efficiency under uncertainty.

Findings

Successfully applied to gene expression and repressilator models.

Handles both molecular noise and cross-laboratory variability.

Outperforms traditional Bayesian methods in efficiency.

Abstract

The design of biological systems is hindered by uncertainty arising from both intrinsic stochasticity of biomolecular reactions and variability across laboratory or experimental conditions. In this work, we present a sequential framework to optimize genetic circuits under both forms of uncertainty. By employing simulator models based on differential equations or Markov jump processes alongside a reinforcement learning (RL) policy-based approach, our method suggests experiments that adapt to unknown laboratory conditions while accounting for inherent stochasticity. While previous Bayesian methods address uncertainty through iterative experiment-inference-optimization cycles, they typically require computationally expensive inference and optimization steps after each experimental round, leading to delays. To overcome this bottleneck, we propose an amortized approach trained up-front across a distribution of possible uncertain parameters. This strategy sidesteps the need for explicit parameter inference during the design cycle, enabling immediate, observation-based adaptation. We demonstrate our framework on models for heterologous gene expression and a repressilator circuit, showing that it efficiently handles both molecular noise and cross-laboratory variability.