ArticleNPJ systems biology and applications2025
Leveraging agent-based models and deep reinforcement learning to predict taxis in cell migration.
Article in NPJ systems biology and applications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Organ-Specific Migration License (OSML) theory: a novel paradigm for spatiotemporal regulation and intervention of cross-organ immune cell migration in tumor immune responses.Cell communication and signaling : CCS · 2026Review
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Authors and funding
4 authors.
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Abstract
We present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this approach, the model captures the transduction of environmental cues into biological responses directly from experimental observations, without explicitly predefining cell behavior. This enables the prediction of dynamic, environment-dependent cell behavior and offers a scalable and flexible alternative to traditional rule-based ABM. To illustrate its potential, we present an application to barotactic cell migration data from microfluidic device experiments, where cells adapt their migration behavior based on pressure gradients, demonstrating the model's ability to generalize across varying geometries and pressure configurations. Thus, this approach introduces a novel direction for modeling how cells sense and transduce environmental cues into biological behaviors.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.