Application of a dynamic metabolic flux algorithm during a temperature-induced lag phase

• Published in: Food and bioproducts processing Vol. 102; pp. 1 - 19
• Main Authors: Vercammen, D., Telen, D., Nimmegeers, P., Janssens, A., Akkermans, S., Noriega Fernandez, E., Logist, F., Van Impe, J.
• Format: Journal Article
• Language: English
• Published: Rugby Elsevier B.V 01-03-2017; Elsevier Science Ltd
• Subjects: Algorithms; B-spline parameterization; Bioreactors; Dynamic metabolic flux analysis; Dynamic optimization; E coli; Experimental validation; Fluxes; Food production; Food quality; Food safety; Growth; Lag phase; Macroscopic models; Mathematical models; Metabolic flux; Metabolism; Metabolites; Microbiology; Multiscale analysis; Prediction models; Predictions; Predictive microbiology; Product safety; Reactors; Temperature; Temperature effects; Dynamic metabolic flux analysis; B-spline parameterization; Experimental validation; Predictive microbiology; Dynamic optimization; Lag phase
• ISSN: 0960-3085; 1744-3571
• DOI: 10.1016/j.fbp.2016.10.003

•One of the first applications of dMFA to experimental data in predictive microbiology is presented.•Two batch bioreactor experiments are performed and the measurements are described.•Specific metabolic fluxes during a temperature-induced lag phase are estimated.•Characteristic metabolic patterns during initial and induced lag phase are discerned.•The recent dMFA methodology is shown to be performant in a real-life case study.
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.