使用强化学习切换遗传开关
Toggling a Genetic Switch Using Reinforcement Learning
AI总结：
本文采用拟合Q迭代强化学习算法，无需系统数学模型，直接利用测量数据实现基因调控网络的最优外源控制，并以切换开关系统为例驱动两种蛋白质浓度到达目标状态区域。
摘要：
在本文中，我们考虑了基因调控网络的最优外源控制问题。我们的方法在于采用一种称为拟合Q迭代的成熟强化学习算法。该算法直接根据系统对外部控制输入的响应测量值推断控制律，而无需使用系统的数学模型。测量数据集既可以从湿实验室实验中收集，也可以通过系统动力学模型的计算机模拟人工创建。由于该算法能够处理非线性和随机系统动力学，因此适用于广泛的生物系统。为了说明该算法在基因调控网络中的应用，考虑了切换开关系统的调控。该问题的控制目标是驱动两种特定蛋白质的浓度到达状态空间中的目标区域。
英文摘要：
In this paper, we consider the problem of optimal exogenous control of gene regulatory networks. Our approach consists in adapting an established reinforcement learning algorithm called the fitted Q iteration. This algorithm infers the control law directly from the measurements of the system's response to external control inputs without the use of a mathematical model of the system. The measurement data set can either be collected from wet-lab experiments or artificially created by computer simulations of dynamical models of the system. The algorithm is applicable to a wide range of biological systems due to its ability to deal with nonlinear and stochastic system dynamics. To illustrate the application of the algorithm to a gene regulatory network, the regulation of the toggle switch system is considered. The control objective of this problem is to drive the concentrations of two specific proteins to a target region in the state space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
