Artificial cells can exhibit survival behavior similar to living organisms, responding to environmental stimuli and undertaking specific displacements. However, a clear definition and characterization of these survival strategies from a statistical physics perspective is still lacking. This study applies Transition Path Theory to active systems and demonstrates its usefulness in identifying survival strategies, improving modeling, and making experimentally testable predictions. The definition of survival in artificial systems allows for a formal characterization and prediction of complex life-like behaviors.
Artificial cells can be engineered to display dynamics sharing remarkable features in common with the survival behavior of living organisms. In particular, such active systems can respond to stimuli provided by the environment and undertake specific displacements to remain out of equilibrium, e.g. by moving towards regions with higher fuel concentration. In spite of the intense experimental activity aiming at investigating this fascinating behavior, a rigorous definition and characterization of such survival strategies from a statistical physics perspective is still missing. In this work, we take a first step in this direction by adapting and applying to active systems the theoretical framework of Transition Path Theory, which was originally introduced to investigate rare thermally activated transitions in passive systems. We perform experiments on camphor disks navigating Petri dishes and perform simulations in the paradigmatic active Brownian particle model to show how the notions of transition probability density and committor function provide the pivotal concepts to identify survival strategies, improve modeling, and obtain and validate experimentally testable predictions. The definition of survival in these artificial systems paves the way to move beyond simple observation and to formally characterize, design and predict complex life-like behaviors.
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