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.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.06552 [cs.LG] |
| (or arXiv:2605.06552v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2605.06552 arXiv-issued DOI via DataCite (pending registration) |
From: Michal Kobiela [view email]
[v1]
Thu, 7 May 2026 16:49:07 UTC (1,478 KB)
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