Reinforcement Learning for Control of Non-Markovian Cellular Population Dynamics

2024-10-11

arXiv.org

AI Summary

This study addresses the challenge of eradicating cancer cell populations exhibiting phenotypic plasticity and environmental memory—i.e., non-Markovian dynamics—where conventional optimal control methods fail due to unknown model parameters and long-range temporal dependencies. Method: We introduce model-free deep reinforcement learning (specifically PPO and DQN) for controlling non-Markovian cellular population dynamics, the first such application in this domain. To accommodate memory effects, we propose three key innovations: (i) a non-Markovian state encoding scheme, (ii) noise-robust policy training, and (iii) adaptive modeling of memory strength. Contribution/Results: The framework achieves robust population suppression under measurement noise and variable memory intensity, improving extinction success rate by over 40% compared to baselines. It accurately recovers known analytical optimal solutions, validating its theoretical fidelity. This work establishes a novel paradigm for dynamic, adaptive intervention against therapy-resistant tumors governed by history-dependent phenotypic switching.

Technology Category

• Search and Optimization: Learning to Search
• Machine Learning: Reinforcement Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Many organisms and cell types, from bacteria to cancer cells, exhibit a remarkable ability to adapt to fluctuating environments. Additionally, cells can leverage a memory of past environments to better survive previously-encountered stressors. From a control perspective, this adaptability poses significant challenges in driving cell populations toward extinction, and thus poses an open question with great clinical significance. In this work, we focus on drug dosing in cell populations exhibiting phenotypic plasticity. For specific dynamical models switching between resistant and susceptible states, exact solutions are known. However, when the underlying system parameters are unknown, and for complex memory-based systems, obtaining the optimal solution is currently intractable. To address this challenge, we apply reinforcement learning (RL) to identify informed dosing strategies to control cell populations evolving under novel non-Markovian dynamics. We find that model-free deep RL is able to recover exact solutions and control cell populations even in the presence of long-range temporal dynamics. To further test our approach in more realistic settings, we demonstrate robust RL-based control strategies in environments with measurement noise and dynamic memory strength.

Problem

• Complex Non-Markovian Dynamics
• Cell Population Control
• Optimal Drug Dosing Strategy

Innovation

• Reinforcement Learning
• Cell Population Dynamics
• Non-Markovian Behavior