|国家预印本平台
首页|Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks: The GATTACA Framework

Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks: The GATTACA Framework

Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks: The GATTACA Framework

来源:Arxiv_logoArxiv
英文摘要

Cellular reprogramming, the artificial transformation of one cell type into another, has been attracting increasing research attention due to its therapeutic potential for complex diseases. However, discovering reprogramming strategies through classical wet-lab experiments is hindered by lengthy time commitments and high costs. In this study, we explore the use of deep reinforcement learning (DRL) to control Boolean network models of complex biological systems, such as gene regulatory networks and signalling pathway networks. We formulate a novel control problem for Boolean network models under the asynchronous update mode in the context of cellular reprogramming. To facilitate scalability, we consider our previously introduced concept of a pseudo-attractor and we improve our procedure for effective identification of pseudo-attractor states. Finally, we devise a computational framework to solve the control problem. To leverage the structure of biological systems, we incorporate graph neural networks with graph convolutions into the artificial neural network approximator for the action-value function learned by the DRL agent. Experiments on a number of large real-world biological networks from literature demonstrate the scalability and effectiveness of our approach.

生物科学研究方法、生物科学研究技术计算技术、计算机技术

.Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks: The GATTACA Framework[EB/OL].(2025-05-05)[2025-05-17].https://arxiv.org/abs/2505.02712.点此复制

评论