LAUSR

Abstract In predictive microbiology, the (induced) lag-phase is a phenomenon of specific interest, as it has a large impact on the assessment of safety and quality of food products. This lag phase has been studied mostly on a macroscopic level. However, a quest for more mechanistically-based predictive models has started, for example, through the integration of a metabolic reaction network into widely used macroscopic model structures. This multi-scale modeling approach is called dynamic metabolic flux analysis (dMFA). In this contribution, a recently developed algorithm for dMFA is used to estimate the metabolic fluxes in Escherichia coli K12 during an experimentally induced lag phase through a sudden shift in temperature. To study this phenomenon, controlled bioreactor experiments were performed: on the one hand at a fixed and optimal temperature for growth (37 °C), and on the other hand starting at 20 °C, with a sudden temperature shift to 37 °C during the exponential growth, inducing an intermediate lag phase. The evolution of biomass and metabolite concentrations was monitored during these experiments. After dMFA analysis of the gathered measurements, some interesting patterns in metabolic activity during the different growth phases are revealed. The described case study is a first practical test case to assess the capabilities of the recently developed dMFA methodology in an experimental predictive microbiology setting.