Our approach to mathematically describing a cell population behaviour has evolved in the past decades. From the use of Monod kinetics, when a limiting nutrient shows to control cell behaviour, we now see modelling approaches based on a genome-wide description of a biosystem, integrating the various “omics” datasets available. However, do we always need that level of complexity to face a problem of bioprocess optimization or to elucidate the mechanisms of a metabolic disease?
Moving from steady state approaches, the use of dynamic metabolic models is gaining in interest. Besides their capacity to “animate” a biosystem metabolic network, such models also allow following the time-evolution of metabolic fluxes performing a dynamic flux analysis. Although in silico, such simulation tools enable observations that are tedious/impossible to perform experimentally. The inherent cost for developing a model with each flux kinetics described relies, however, on acquiring experimental concentration data of extracellular and intracellular volumes, as well as literature data on kinetic parameters, when available.
Various optimization algorithms can be applied to estimate parameters value, based on a sensitivity analysis on model behaviour. This process is highly instructive, testing hypotheses on the network map and on flux regulation mechanisms while refining model structure. Starting with a minimal metabolic network can be a wise idea to accelerate the model development process. Therefore, once anchored on an experimental reality, model simulations, which require only initial conditions and kinetic parameters values, allow questioning the cell metabolic behaviour and thus visualise flux dynamics with time as well as the specific contribution of each regulation mechanisms, and thus lead to the identification of biomarkers of specific cell phenotypes. Examples for biomedical and bioprocess development will be shown, and current limitations and future trends will be discussed.
Bio
Dr. Mario Jolicoeur is a full professor in the Department of Chemical Engineering at the École Polytechnique de Montréal. Prof. Jolicoeur’s research program aims at developing models that can help understand cell behaviour. From his research activities in Applied Metabolic Engineering, he has contributed to various fields, including biomedicine, biopharmaceutics and biofuels production.