Physiological, biochemical, and mathematical studies of micro‐aerobic continuous ethanol fermentation by Saccharomyces cerevisiae. III: Mathematical model of cellular energetics and catabolism

Abstract

Mathematical models of the catabolic pathways, the utilization and waste of ATP, and the factors affecting yeast growth in a micro-aerobic chemostat are presented. The models incorporate the intracellular metabolite and enzyme activity assays performed in Part II to explain the unusual macroscopic chemostat behaviors reported in Part I. The catabolic model successfully predicts a maximum in the specific ethanol productivity as a function of the intracellular ATP concentration. The ATP balance model enables the prediction of the intracellular ATP concentration and the ATP yield for given dissolved oxygen concentrations. Finally, in the context of a growth model, singularity theory provides a framework to explain the transition observed in Part I between hysteresis and the monotonic biomass versus oxygenation profiles in response to changes in the nutrient composition. The models serve to organize data and to concretely express proposed metabolic mechanisms and cause-effect hypotheses. The model is only applicable to the micro-aerobic and excess glucose conditions encountered in this study.

An integrated metabolic modeling approach to describe the energy efficiency of Escherichia coli fermentations under oxygen‐limited conditions: Cellular energetics, carbon flux, and acetate production

“…PK2CT p c = Kl(6 -0.007)VX This Eq. (19) and the criterion for acetate production, (5.0562 + 1.141)n > CT, are consistent with phenomena we have observed during aerobic E. coli fermentation processes. These include acetate production accompanying high specific growth rate (p), high glucose concentration ( n ) , and low oxygen concentration (7).…”

In vivo analysis of metabolic dynamics inSaccharomyces cerevisiae : I. Experimental observations

“…In recent years, structured models for metabolic modeling have received increasing attention (Boiteux and Busse, 1989;Delgado et al, 1993;Galazzo and Bailey, 1990;Grosz and Stephanopoulos, 1990;Joshi and Palsson, 1989;Liao et al, 1988;Richter et al, 1975;Rizzi et al, 1991Rizzi et al, , 1995Rizzi et al, , 1996Rizzi et al, , 1997Schlosser et al, 1994;Steinmeyer and Shuler, 1989). Their success depends on the reliability of the enzyme kinetics used to model the network of intracellular reactions.…”

“…1) have used resting cells (Betz and Moore, 1967;Boiteux and Hess, 1981), cell extracts (Boiteux and Busse, 1989;Delgado et al, 1993;Richter et al, 1975), or anaerobic conditions (Galazzo and Bailey, 1990;Grosz and Stephanopoulos, 1990;Liao et al, 1988;Schlosser et al, 1994). The primary goal of these studies was a model of the steady state or a transient state in the time window of minutes.…”

Mathematical modeling of microbial growth and production kinetics for @-amylase production using mustard oil cake as solid substrate

“…There are consequential diffi culties that have been observed with the direct measurement of cell biomass in SSF systems therefore a weighing method could provide a signifi cant method in kinetic studies of such processes. Bioprocess modeling including cells and cultures can be of signifi cant importance in optimizing and controlling actual production process of biomass and product (Curien et al 2003;Grosz and Stephanopoulos 1999). Several challenges have been observed at each stage in the development of models of enzyme production kinetics (Copella and Dhurjati 1989;Thilakavathi et al 2007).…”