Reinforcement learning framework models cellular navigation via chemical reactions

Researchers have developed a novel framework that integrates reinforcement learning with chemical reaction networks to model cellular navigation. This approach treats phototaxis, the movement of organisms towards light, as an information-driven sensorimotor process rather than a simple stimulus-response mechanism. By framing the problem as a Partially Observable Markov Decision Process (POMDP), the model accounts for hidden environmental variables and uses Bayesian updates to maintain an internal state, balancing directed movement with exploratory sampling. The framework is implemented using chemical-reaction-network ordinary differential equations and has been validated against experimental data of Chlamydomonas trajectories, demonstrating that run-tumble behavior emerges as a strategy for information acquisition. AI

IMPACT This research offers a new computational approach for understanding biological navigation, potentially informing the design of bio-inspired AI systems.

RANK_REASON The cluster contains an academic paper detailing a new computational framework for modeling biological processes using AI techniques. [lever_c_demoted from research: ic=1 ai=1.0]

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• Chlamydomonas
• Gregoire Sergeant-Perthuis

• paper
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