Induction kinetics of aerobic toluene degradation as a function of carbon starvation history

Improving the prediction of Pseudomonas putida mt-2 growth kinetics with the use of a gene expression regulation model of the TOL plasmid

“…Furthermore, in continuous applications with unstable substrates feed (e.g. fluctuating substrates concentration, starvation periods, sequentially alternating substrates), which is commonplace at remediation sites [38], the duration of the lag can be even harder to predict due to excessive biomass inhibition or inactivation and biomass might require longer time periods to re-establish its full biodegradation capacity [39]. The failure of models to predict the lag-phase in some cases highlights the importance of including in our models the exact mechanism for the production of enzymes with the incorporation of the function of specific genetic circuits producing these enzymes.…”

Kinetic modeling of microbial growth, enzyme activity, and gene deletions: An integrated model of β‐glucosidase function in Cellvibrio japonicus

“…Furthermore, extracting mechanistic knowledge from these datasets towards quantitative predictions in terms of growth dynamics under different conditions requires system‐level kinetic modeling able to account for the degradation of multiple metabolites by different enzymes and under various growth conditions (Kurata & Sugimoto, 2018; Steuer, Gross, Selbig, & Blasius, 2006). However, traditional kinetic models lack genetic and enzymatic dynamics and are usually calibrated for predicting growth using only a single substrate (Park, Lang, Thamaraiselvi, Kukor, & Abriola, 2008). Consequently, more sophisticated kinetic models are needed for gaining a comprehensive understanding of specific microbial degradation pathways that can predict multiple enzyme functions and growth dynamics under complex substrate conditions and genetic perturbations (Smallbone et al, 2013).…”

Linking genes to microbial growth kinetics—An integrated biochemical systems engineering approach

“…However, in many cases biomass utilization and substrate consumption patterns cannot be accurately predicted by models developed merely based on bulk measurements, due to regulation at both the enzyme and the genetic level (Rogers and Reardon, 2000). Especially in bioprocesses with mixed microbial populations, multiple substrates and fluctuating substrate concentrations, traditional Michaelis-Menten and Monod models do not capture the description of substrate degradation (Park et al, 2008). Previous studies have demonstrated that enzymatic measurements can be successfully used to construct mechanistic models with improved predictive capabilities (Melchiorsen et al, 2001).…”