Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning

• URL: http://arxiv.org/abs/2605.06552v1
• Date: Thu, 07 May 2026 16:49:07 GMT
• Title: Sequential Design of Genetic Circuits Under Uncertainty With Reinforcement Learning
• Abstract summary: We present a sequential framework to optimize genetic circuits under both forms of uncertainty.<n>Our method suggests experiments that adapt to unknown laboratory conditions while accounting for inherentity.<n>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.
• Score: 3.5429774642987915
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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