【报告摘要】 Biomolecular networks underpin emerging technologies in synthetic biology-from robust biomanufacturing and metabolic engineering to smart therapeutics and cell-based diagnostics-and also provide a mechanistic language for understanding complex dynamics in natural and ecological systems. Yet designing chemical reaction networks (CRNs) that implement a desired dynamical function remains largely manual: while a proposed network can be checked by simulation, the reverse problem of discovering a network from a behavioral specification is difficult, requiring substantial human insight to navigate a vast space of topologies and kinetic parameters with nonlinear and possibly stochastic dynamics. Here we introduce GenAI-Net, a generative AI framework that automates CRN design by coupling an agent that proposes reactions to simulation-based evaluation defined by a user-specified objective. GenAI-Net efficiently produces novel, topologically diverse solutions across multiple design tasks, including dose responses, complex logic gates, classifiers, oscillators, and robust perfect adaptation in deterministic and stochastic settings (including noise reduction). By turning specifications into families of circuit candidates and reusable motifs, GenAI-Net provides a general route to programmable biomolecular circuit design and accelerates the translation from desired function to implementable mechanisms.

【报告人简介】方洲，中国科学院数学与系统科学研究院副研究员。主要研究方向为生物系统控制与计算。其研究旨在通过数学与系统控制理论的方法，探究生命系统运行的深层机制，并为其认知、调控、改造提供理论支持与创新性解决方案。方洲于2014年和2019年分别获得浙江大学本科与博士学位，2019年至2024年于苏黎世联邦理工学院（ETH Zurich）从事博士后研究工作。已在本领域重要期刊发表论文十余篇，包括Nature Communications、SIAM系列期刊、IEEE Transactions on Automatic Control、Automatica等。