Browsing Publications of Dept. Gene Regulation and Differentiation (RDIF) by Subject (MeSH)
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Linking genes to microbial growth kinetics: an integrated biochemical systems engineering approach.The majority of models describing the kinetic properties of a microorganism for a given substrate are unstructured and empirical. They are formulated in this manner so that the complex mechanism of cell growth is simplified. Herein, a novel approach for modelling microbial growth kinetics is proposed, linking biomass growth and substrate consumption rates to the gene regulatory programmes that control these processes. A dynamic model of the TOL (pWW0) plasmid of Pseudomonas putida mt-2 has been developed, describing the molecular interactions that lead to the transcription of the upper and meta operons, known to produce the enzymes for the oxidative catabolism of m-xylene. The genetic circuit model was combined with a growth kinetic model decoupling biomass growth and substrate consumption rates, which are expressed as independent functions of the rate-limiting enzymes produced by the operons. Estimation of model parameters and validation of the model's predictive capability were successfully performed in batch cultures of mt-2 fed with different concentrations of m-xylene, as confirmed by relative mRNA concentration measurements of the promoters encoded in TOL. The growth formation and substrate utilisation patterns could not be accurately described by traditional Monod-type models for a wide range of conditions, demonstrating the critical importance of gene regulation for the development of advanced models closely predicting complex bioprocesses. In contrast, the proposed strategy, which utilises quantitative information pertaining to upstream molecular events that control the production of rate-limiting enzymes, predicts the catabolism of a substrate and biomass formation and could be of central importance for the design of optimal bioprocesses.
The metabolic response of P. putida KT2442 producing high levels of polyhydroxyalkanoate under single- and multiple-nutrient-limited growth: highlights from a multi-level omics approach.Pseudomonas putida KT2442 is a natural producer of polyhydroxyalkanoates (PHAs), which can substitute petroleum-based non-renewable plastics and form the basis for the production of tailor-made biopolymers. However, despite the substantial body of work on PHA production by P. putida strains, it is not yet clear how the bacterium re-arranges its whole metabolism when it senses the limitation of nitrogen and the excess of fatty acids as carbon source, to result in a large accumulation of PHAs within the cell. In the present study we investigated the metabolic response of KT2442 using a systems biology approach to highlight the differences between single- and multiple-nutrient-limited growth in chemostat cultures.
Reconciliation of genome-scale metabolic reconstructions for comparative systems analysis.In the past decade, over 50 genome-scale metabolic reconstructions have been built for a variety of single- and multi- cellular organisms. These reconstructions have enabled a host of computational methods to be leveraged for systems-analysis of metabolism, leading to greater understanding of observed phenotypes. These methods have been sparsely applied to comparisons between multiple organisms, however, due mainly to the existence of differences between reconstructions that are inherited from the respective reconstruction processes of the organisms to be compared. To circumvent this obstacle, we developed a novel process, termed metabolic network reconciliation, whereby non-biological differences are removed from genome-scale reconstructions while keeping the reconstructions as true as possible to the underlying biological data on which they are based. This process was applied to two organisms of great importance to disease and biotechnological applications, Pseudomonas aeruginosa and Pseudomonas putida, respectively. The result is a pair of revised genome-scale reconstructions for these organisms that can be analyzed at a systems level with confidence that differences are indicative of true biological differences (to the degree that is currently known), rather than artifacts of the reconstruction process. The reconstructions were re-validated with various experimental data after reconciliation. With the reconciled and validated reconstructions, we performed a genome-wide comparison of metabolic flexibility between P. aeruginosa and P. putida that generated significant new insight into the underlying biology of these important organisms. Through this work, we provide a novel methodology for reconciling models, present new genome-scale reconstructions of P. aeruginosa and P. putida that can be directly compared at a network level, and perform a network-wide comparison of the two species. These reconstructions provide fresh insights into the metabolic similarities and differences between these important Pseudomonads, and pave the way towards full comparative analysis of genome-scale metabolic reconstructions of multiple species.