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

The paper introduces GATTACA, a scalable framework that applies deep reinforcement learning to control Boolean network models of biological systems, particularly for cellular reprogramming. It formulates a novel control problem under asynchronous updates and enhances scalability by using pseudo‑attractor states and graph neural networks to encode network structure into the DRL agent’s action‑value function. Experiments on large real‑world biological networks demonstrate the approach’s scalability and effectiveness.

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• agents
• reinforcement-learning