Minimal Generative Structure of Dynamically Closed Modules of a Reaction Network

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Chemical Organization Theory (COT) is an abstract framework to identify self-producing structures within reaction networks. Despite its usefulness to explain complex adaptive phenomena in biochemistry and other domains, there is no understanding of how organizations, being a mininmal model of purposeful agency, could evolve through their interaction with a complex environment.
Here we develop a framework and a systematic analysis to study the emergence and evolution of organizations in random reaction networks. We first study how likely is that a random reaction network produces an non-trivial organizational structure, i.e. a hierarchical set of organizations. We find that there is a particular rate between reactions and species that maximizes the size of the organizational structure, and that by fine tuninng parameters relations between different types of reactions we can obtain organizational structures of significantly larger complexity, i.e. height and wdth of the organizational structure. Next, we formulate the evolution of organizations as a Markov process, where nodes of the process graph are organizations and transitions are triggered by perturbations adding or eliminating species from the system. We have found that in all scenarios organizations exhibit larger adaptive capacity (resilience) as their structure complexifies, and that groups of organizations form resilient Markov blankets, suggesting that over the evolution of self-producting structures there is a natural tendency to evolve second-order more resilient structures. We discuss our results in relation to foundational questions such as the emergence and evolution of life, agency and goal-directedness.
Original languageEnglish
JournalProc Natl Acad Sci USA
Publication statusIn preparation - 2023


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