用时序逻辑指导生物神经网络训练，实现精准回归与调控
STL-based Optimization of Biomolecular Neural Networks for Regression and Control
- 以时序逻辑规范作为训练目标，支持梯度优化
- 在慢性病模型中成功实现炎症抑制与感染响应规避
- 适用于需精确动态控制的生物系统设计
生物分子神经网络（BNN）是具有可生物合成结构的人工神经网络，具备超越简单生物电路的通用函数逼近能力。然而，由于缺乏目标数据，其训练仍具挑战。为此，本文提出利用信号时序逻辑（STL）规范定义BNN的训练目标。基于STL的定量语义，实现了BNN权重的梯度优化，并提出一种学习算法，使BNN能在生物系统中完成回归与控制任务。具体而言，研究了两个回归问题：训练BNN作为失调状态的报告器；以及一个闭环反馈控制问题：在慢性病模型中训练BNN，学习在抑制炎症的同时避免对外部感染的过度反应。数值实验表明，基于STL的学习方法能高效解决所研究的回归与控制任务。
原文摘要 · Abstract (English)
Biomolecular Neural Networks (BNNs), artificial neural networks with biologically synthesizable architectures, achieve universal function approximation capabilities beyond simple biological circuits. However, training BNNs remains challenging due to the lack of target data. To address this, we propose leveraging Signal Temporal Logic (STL) specifications to define training objectives for BNNs. We build on the quantitative semantics of STL, enabling gradient-based optimization of the BNN weights, and introduce a learning algorithm that enables BNNs to perform regression and control tasks in biological systems. Specifically, we investigate two regression problems in which we train BNNs to act as reporters of dysregulated states, and a feedback control problem in which we train the BNN in closed-loop with a chronic disease model, learning to reduce inflammation while avoiding adverse responses to external infections. Our numerical experiments demonstrate that STL-based learning can solve the investigated regression and control tasks efficiently.
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