000162728 001__ 162728
000162728 005__ 20251017144613.0
000162728 0247_ $$2doi$$a10.1038/s41540-025-00576-0
000162728 0248_ $$2sideral$$a145314
000162728 037__ $$aART-2025-145314
000162728 041__ $$aeng
000162728 100__ $$aCamacho-Gomez, Daniel$$uUniversidad de Zaragoza
000162728 245__ $$aLeveraging agent-based models and deep reinforcement learning to predict taxis in cell migration
000162728 260__ $$c2025
000162728 5060_ $$aAccess copy available to the general public$$fUnrestricted
000162728 5203_ $$aWe present a novel computational framework that combines Agent-Based Modeling (ABM) with Reinforcement Learning (RL) using the Double Deep Q-Network (DDQN) algorithm to determine cellular behavior in response to environmental signals. With this approach, the model captures the transduction of environmental cues into biological responses directly from experimental observations, without explicitly predefining cell behavior. This enables the prediction of dynamic, environment-dependent cell behavior and offers a scalable and flexible alternative to traditional rule-based ABM. To illustrate its potential, we present an application to barotactic cell migration data from microfluidic device experiments, where cells adapt their migration behavior based on pressure gradients, demonstrating the model’s ability to generalize across varying geometries and pressure configurations. Thus, this approach introduces a novel direction for modeling how cells sense and transduce environmental cues into biological behaviors
000162728 536__ $$9info:eu-repo/grantAgreement/EC/H2020/101018587/EU/Individual and Collective Migration of the Immune Cellular System/ICoMICS$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 101018587-ICoMICS
000162728 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000162728 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000162728 700__ $$aSentiero, Raffaele
000162728 700__ $$aVentre, Maurizio
000162728 700__ $$0(orcid)0000-0002-9864-7683$$aGarcia-Aznar, Jose Manuel$$uUniversidad de Zaragoza
000162728 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000162728 773__ $$g11, 1 (2025), 8 pp.$$pnpj syst. biol. appl.$$tnpj Systems Biology and Applications$$x2056-7189
000162728 8564_ $$s3117854$$uhttps://zaguan.unizar.es/record/162728/files/texto_completo.pdf$$yVersión publicada
000162728 8564_ $$s2972188$$uhttps://zaguan.unizar.es/record/162728/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000162728 909CO $$ooai:zaguan.unizar.es:162728$$particulos$$pdriver
000162728 951__ $$a2025-10-17-14:18:23
000162728 980__ $$aARTICLE