mlsanityData to 14 Oct 2026

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

arXiv 2505.02712 ↗First posted 5 May 2025cs.LG cs.AI q-bio.MNcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 42 references that are in the map. First posted on arXiv 5 May 2025.

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10 of 42 references.

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  1. 2024Deep Reinforcement Learning for Controlled Traversing of the Attractor Landscape of Boolean Models in the Context of Cellular Reprogramming13 Feb

Other references · 9

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,417
  2. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  3. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,745
  4. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  6. 2017Action Branching Architectures for Deep Reinforcement Learningcited by 92
  7. 2019Deep Reinforcement Learning for Control of Probabilistic Boolean Networkscited by 3
  8. 2015Control of complex networks requires both structure and dynamicscited by 2
  9. 2022Deep Reinforcement Learning for Stabilization of Large-scale Probabilistic Boolean Networkscited by 2

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  3. 2026CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion
  4. 2025Neural cellular automata: applications to biology and beyond classical AI
  5. 2024Generalisation to unseen topologies: Towards control of biological neural network activity
  6. 2024Growing Artificial Neural Networks for Control: the Role of Neuronal Diversity
  7. 2024Biological Neurons Compete with Deep Reinforcement Learning in Sample Efficiency in a Simulated Gameworld
  8. 2022RLFlow: Optimising Neural Network Subgraph Transformation with World Models