Computational analysis of Pseudomonas Putida metabolism Using Large-scale Kinetic Models

P. putida is a highly adaptive, non-pathogenic, soil bacterium that can grow on a wide range of substrates, and it is tolerant to high toxicity compounds. For these reasons, it emerged recently as one of the most promising production hosts for a wide range of chemicals. In this work, we performed a computational analysis of this organism to evaluate its metabolic capacities and design metabolic engineering strategies to improve its robustness to stress conditions. To this end, we first performed a thermodynamic curation of the genome-scale iJN1411 model of P. putida KT2440, and we then used redGEM and lumpGEM algorithms to derive a consistently reduced large-scale stoichiometric model of P. putida. We integrated experimental data into the resulting core stoichiometric model, and we computed the thermodynamically-consistent steady state of metabolic fluxes. We then used the ORACLE framework to generate a population of large-scale kinetic models around the computed steady state, and we employed these models in two studies. In the first study, for wild-type strain of P. putida KT2440 grown under aerobic conditions using glucose as a carbon source, we evaluated and validated the predictions of the generated kinetic models against a collection of experimental single-gene knockouts. In the second study, we analyzed the capacity of P. putida to adapt to increased energy demand and we identified potential metabolic engineering targets for improved resistance of this organism to stress conditions. This work demonstrates the potential and usefulness of kinetic models in rational metabolic engineering strategies for (i) understanding the physiology of production hosts, (ii) optimizing production pathways, and (iii) improving the metabolic responses of organisms to environmental stresses.


Advisor(s):
Miskovic, Ljubisa
Hatzimanikatis, Vassily
Presented at:
Metabolic Engineering 12, Munich, Germany, June 24-28, 2018
Year:
2018
Laboratories:




 Record created 2018-08-15, last modified 2019-04-15


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