title: Analysis of the lag phase to exponential growth transition by incorporating inoculum characteristics.
abstract: During the last decade, individual-based modelling (IbM) has proven to be a valuable tool for modelling and studying microbial dynamics. As each individual is considered as an independent entity with its own characteristics, IbM enables the study of microbial dynamics and the inherent variability and heterogeneity. IbM simulations and (single-cell) experimental research form the basis to unravel individual cell characteristics underlying population dynamics. In this study, the IbM framework MICRODIMS, i.e., MICRObial Dynamics Individual-based Model/Simulator, is used to investigate the system dynamics (with respect to the model and the system modelled). First, the impact of the time resolution on the simulation accuracy is discussed. Second, the effect of the inoculum state and size on emerging individual dynamics, such as individual mass, individual age and individual generation time distribution dynamics, is studied. The distributions of individual characteristics are more informative during the lag phase and the transition to the exponential growth phase than during the exponential phase. The first generation time distributions are strongly influenced by the inoculum state. All inocula with a pronounced heterogeneity, except the inocula starting from a uniform distribution, exhibit commonly observed microbial behaviour, like a more spread first generation time distribution compared to following generations and a fast stabilisation of biomass and age distributions.
authors.affiliation: CPMF2(1)-Flemish Cluster Predictive Microbiology in Foods, Chemical and Biochemical Process Technology and Control (BioTeC), Department of Chemical Engineering, Katholieke Universiteit Leuven, Leuven, Belgium. anke.verhulst@cit.kuleuven.be
authors.forename: A J
authors.initials: AJ
authors.lastname: Verhulst
authors.forename: A M
authors.initials: AM
authors.lastname: Cappuyns
authors.forename: E
authors.initials: E
authors.lastname: Van Derlinden
authors.forename: K
authors.initials: K
authors.lastname: Bernaerts
authors.forename: J F
authors.initials: JF
authors.lastname: Van Impe
mesh_terms: D001419:Bacteria / Q000254:growth & development*; D018533:Biomass; D003198:Computer Simulation; D005516:Food Microbiology / Q000379:methods*; D008954:Models, Biological