Twelve quick tips for designing sound dynamical models for bioprocesses

Abstract

Because of the inherent complexity of bioprocesses, mathematical models are more and more used for process design, control, optimization, etc. These models are generally based on a set of biochemical reactions. Model equations are then derived from mass balance, coupled with empirical kinetics. Biological models are nonlinear and represent processes, which by essence are dynamic and adaptive. The temptation to embed most of the biology is high, with the risk that calibration would not be significant anymore. The most important task for a modeler is thus to ensure a balance between model complexity and ease of use. Since a model should be tailored to the objectives, which will depend on applications and environment, a universal model representing any possible situation is probably not the best option.

ALBA: A comprehensive growth model to optimize algae-bacteria wastewater treatment in raceway ponds

“…Designing a model, especially for a complex outdoor biological process is the sum of many subtle and strategic choices (Mairet and Bernard, 2019). We detail hereafter the most determinant modelling choices, highlighting the main differences between ALBA and pre-existing algae-bacteria models.…”

A framework based on fundamental biochemical principles to engineer microbial community dynamics

“…We propose that equipped with this understanding, there is potential to go beyond models that are constrained by the availability of empirical measurements of kinetic parameters, to predict microbial dependencies and community assembly. By adapting this conceptual framework to specific cases [5], it can be potentially integrated with molecular approaches to explain and/or support their findings, overall increasing our predictive capacity [6]. We, of course, realise that dependencies within a microbial community are far more complex and nuanced than presented here, with predator-prey or/and parasite-host relationships, dependencies on metals, vitamins, light levels, and others not accounted for, at play [7].…”

Reducing a model of sugar metabolism in peach to catch different patterns among genotypes

“…The question recalls the one of "model validation domain" i.e. the ability for a given model to describe data obtained in conditions dierent from those in which the model itself was calibrated [41]. Here it is about selecting for a reduced model having a large validation domain and able to cope with changes in model's inputs, parameter values, and initial conditions.…”