Modelling the work to be done by Escherichia coli to adapt to sudden temperature upshifts.

AIMS: This paper studies and models the effect of the amplitude of a sudden temperature upshift DeltaT on the adaptation period of Escherichia coli, in terms of the work to be done by the cells during the subsequent lag phase (i.e., the product of growth rate mumax and lag phase duration lambda). METHODS AND RESULTS: Experimental data are obtained from bioreactor experiments with E. coli K12 MG1655. At a predetermined time instant during the exponential growth phase, a sudden temperature upshift is applied (no other environmental changes take place). The length of the (possibly) induced lag phase and the specific growth rate after the shift are quantified with the growth model of Baranyi and Roberts (Int J Food Microbiol 23, 1994, 277). Different models to describe the evolution of the product lambda x mumax as a function of the amplitude of the temperature shift are statistically compared. CONCLUSIONS: The evolution of lambda x mumax is influenced by the amplitude of the temperature shift DeltaT and by the normal physiological temperature range. As some cut-off is observed, the linear model with translation is preferred to describe this evolution. SIGNIFICANCE AND IMPACT OF THE STUDY: This work contributes to the characterization of microbial lag phenomena, in this case for E. coli K12 MG1655, in view of accurate predictive model building.

Quantifying microbial lag phenomena due to a sudden rise in temperature: a systematic macroscopic study.

The microbial lag phase is a complex and yet not completely understood phenomenon. Many studies on the microbial lag phase have been published but few report a systematic study; moreover, previous lag studies have involved the effect of multiple confounded factors. Here, the effect of sudden temperature rises on an exponentially growing Escherichia coli culture is systematically investigated. Experiments are performed in a computer-controlled bioreactor where E. coli K12 MG1655 is grown under aerobic conditions in Brain Heart Infusion (BHI) broth. This experimental set-up is used to characterise the effect of (i) the amplitude of the temperature shift, (ii) the pre-shift temperature level and (iii) the post-shift temperature level on the occurrence and length of a lag phase. Besides temperature, no other environmental changes take place at the moment of the temperature shift. To quantify the length of the induced lag phase, the experimental data are described with a common growth model. Depending on the three factors tested, a lag phase of more or less 1 h is induced or not. This lag/no lag behaviour can largely be explained by the existence of a normal physiological temperature range but also the amplitude of the temperature rise plays a role. It can be concluded that for the microorganism under study the lower boundary of the normal range lies approximately between 22.78 and 23.86 degrees C. It is shown that this boundary is no cut-off point, but rather a transition zone. Even more, repeated experiments at this boundary have yielded different results (lag or no lag). This observation points out that the mechanism triggering this lag phase is not absolute but may be subject to biological variability.

Predictive modelling of the microbial lag phase: a review.

This paper summarises recent trends in predictive modelling of microbial lag phenomena. The lag phase is approached from both a qualitative and a quantitative point of view. First, a definition of lag and an analysis of the prevailing measuring techniques for the determination of lag time is presented. Furthermore, based on experimental results presented in literature, factors influencing the lag phase are discussed. Major modelling approaches concerning lag phase estimation are critically assessed. In predictive microbiology, a two-step modelling approach is used. Primary models describe the evolution of microbial numbers with time and can be subdivided into deterministic and stochastic models. Primary deterministic models, e.g., Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277], Hills and Wright [J. Theor. Biol. 168 (1994) 31] and McKellar [Int. J. Food Microbiol. 36 (1997) 179], describe the evolution of microorganisms, using one single (deterministic) set of model parameters. In stochastic models, e.g., Buchanan et al. [Food Microbiol. 14 (1997) 313], Baranyi [J. Theor. Biol. 192 (1998) 403] and McKellar [J. Appl. Microbiol. 90 (2001) 407], the model parameters are distributed or random variables. Secondary models describe the relation between primary model parameters and influencing factors (e.g., environmental conditions). This survey mainly focuses on the influence of temperature and culture history on the lag phase during growth of bacteria.