Graph Neural Network-Based Reinforcement Learning for Controlling Biological Networks - the GATTACA Framework
This work addresses the challenge of costly and time-consuming wet-lab experiments in cellular reprogramming for therapeutic applications, representing an incremental improvement with a novel method for a known bottleneck.
The study tackled the problem of identifying effective cellular reprogramming strategies by using deep reinforcement learning to control Boolean network models of biological systems, resulting in a scalable framework called GATTACA that demonstrated effectiveness on large-scale real-world networks.
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, identifying effective 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 and signalling pathway networks. We formulate a novel control problem for Boolean network models under the asynchronous update mode, specifically in the context of cellular reprogramming. To solve it, we devise GATTACA, a scalable computational framework. To facilitate scalability of our framework, we consider previously introduced concept of a pseudo-attractor and improve the procedure for effective identification of pseudo-attractor states. We then incorporate graph neural networks with graph convolution operations into the artificial neural network approximator of the DRL agent's action-value function. This allows us to leverage the available knowledge on the structure of a biological system and to indirectly, yet effectively, encode the system's modelled dynamics into a latent representation. Experiments on several large-scale, real-world biological networks from the literature demonstrate the scalability and effectiveness of our approach.