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Twelve quick tips for designing sound dynamical models for bioprocesses

Francis Mairet 1 Olivier Bernard 2
2 BIOCORE - Biological control of artificial ecosystems
CRISAM - Inria Sophia Antipolis - Méditerranée , INRA - Institut National de la Recherche Agronomique, LOV - Laboratoire d'océanographie de Villefranche
Abstract : Because of the inherent complexity of bioprocesses, mathematical models are more and 1 more used for process design, control and optimization etc... These models are generally 2 based on a set of biochemical reactions. Model equations are then derived from mass balance, 3 coupled to empirical kinetics. Biological models are nonlinear and represent processes, 4 which by essence are dynamic and adaptive. The temptation to embed most of the biology 5 is high, with the risk that calibration would not be significant anymore. The most important 6 task for a modeler is thus to ensure a balance between model complexity and ease of use. 7 Since a model should be tailored to the objectives which will depend on applications and 8 environment, a universal model representing any possible situation is probably not the best 9 option. 10 Here are twelve tips to develop your own bioprocess model. For more details on bioprocess 11 modelling, the readers could refer to [1]. More tips concerning computational aspects can 12 be found in [2, 3].
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Francis Mairet, Olivier Bernard. Twelve quick tips for designing sound dynamical models for bioprocesses. PLoS Computational Biology, Public Library of Science, 2019, 15 (8), pp.e1007222. ⟨10.1371/journal.pcbi.1007222⟩. ⟨hal-02421763⟩

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