(330d) Advancing Microbial Growth Kinetic Models with the Use of Genetic Modelling

Microbial growth kinetics is an essential tool for the design of optimal bioprocesses. Despite more than half a century of research, many fundamental questions about the validity and application of growth kinetics are still unanswered [1]. Various cases in biotechnology demonstrate that understanding of the kinetics of microbial growth is limited and a multitude of substrate utilisation patterns may occur depending on the number of substrates present, their metabolic effects and their concentration level [2]. Thus, although usually an analogy to enzyme kinetics is made when modelling a bioprocess, in some cases ?unusual? growth patterns have been reported but not modelled [3]. Therefore, for a certain combination of substrates none of the so far developed models may accurately fit the experimental data or may not be valid for a wide range of conditions. The failure of models to predict the growth kinetics in some cases underlines the need for inclusion of the exact mechanism for the production of enzymes [4]. The enzymes used for the metabolism of substrates in a certain process, are synthesized by genes existing in specific genetic circuits of the cells. Thus, in cases where the use of quantitative genetic information is imperative, the construction of mathematical models describing the molecular interactions regulating the transcription of these genes might provide the exact mechanism for the prediction of biomass growth.

Genetic circuits are groups of interacting genes and proteins that produce certain behaviour [5]. Based on our capability to engineer naturally occurring genetic circuits, synthetic circuits can be constructed and fundamental biological processes can be studied systematically. However, the extensive experimentation required to understand the function of genetic circuits, is often limited by the time or cost required. Therefore, analysis of genetic circuits with mathematical models can answer different compelling biological questions that centre on how interactions between genes and proteins lead to distinct responses of various cellular functions to changes in the cells environment and help us reduce substantially the trial-and-error experimentation. In line with this, dynamic modelling can be used for characterisation of the physiological behaviour of cells integrating biological information into predictive models [6].

This is the first study, to our knowledge, attempting to combine a mathematical model of a key genetic circuit with the growth kinetics of the host microorganism. To this end we have previously paved the way with the development of a mathematical model of the Ps/Pr node of the TOL plasmid encoded by P. putida mt-2, involved in the metabolism of m-xylene [7]. Herein, we present a growth kinetic model of the strain and its novel coupling with the genetic circuit model, which is extended to include the function of Pu and Pm promoters of the network that control the transcription from its catabolic operons. The structure of the combined model was validated with batch cultures of mt-2 fed with different concentrations of m-xylene ranging between 0.4-1.3 mM and its predictive capability was confirmed by successful predicting independent sets of experimental data, including measurements of the promoters' activity via Real Time RT-PCR. The combined model demonstrates a new approach for the improvement of growth kinetic models in cases where the regulatory mechanisms of key genetic circuits are very important when predicting cellular growth.

The genetic circuit-growth kinetic model successfully combines the prediction of a key genetic circuit for the bioprocess to the growth kinetics of the microorganism, producing a reliable description of the system's performance. The results exemplify the critical importance of genetic information providing a systemic understanding of the behaviour of bioprocesses with increased modelling complexity that currently used models fail to predict. Consequently, in bioprocesses where the cellular concentration of catabolic enzymes is subject to regulation, linking residual concentrations of substrates to the regulatory patterns of genetic circuits should emerge as a viable direction in biological modelling.

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