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Study on dynamics of microbial collective behaviour using an original machine-ecosystem interaction

Abstract : No matter the scale of observation, biological systems ranging from molecules to cells, and multicellular organisms to communities, manifest collective behaviours. Many explanatory ideas have been put forward based on local perception. In each of these cases progress has been possible because the collective-level phenotype is obvious and observable to the naked eye, but also because of the objective function targeted in the understanding of these behaviours. Here I constructed a machine-ecosystem hybrid that involve an observation device, coupled to a light landscape generator in an automated loop that contain a learning process at every step based on data captured from the ecosystem being observed. In order to break the reality gap between modelisation and reality, taking into account the deep complexity of the studied system as the modelisation can then co evolve with the observed matter, that is reactive toward the adaptive landscape that interact with it, and then reach unexplored and not experimentally implementable fields of heuristic. My project involves several stages. Construction of the hybrid. Application and development interaction algorithms. Establishment of simple microbial populations that can be sustained and whose behaviour can be manipulated via the machine. Experiments to demonstrate proof of principle.
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Charles Fosseprez. Study on dynamics of microbial collective behaviour using an original machine-ecosystem interaction. Human health and pathology. Université Paris sciences et lettres, 2019. English. ⟨NNT : 2019PSLET050⟩. ⟨tel-02982237⟩

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