The Computational Life Science Colloquium presents:
Ruben van Heck, Wageningen University, Wageningen, The Netherlands
with a talk titled:
Exploring and re-designing Pseudomonas putida metabolism using genome-scale metabolic models
Genome-Scale constraint-based Metabolic models (GSMs) comprehensively represent the current knowledge on the metabolism of a microbe. They detail both what the microbe can do - its repertoire of biochemical reactions – and what it needs to do in order to survive and grow. Thereby, GSMs enable us to predict the possible metabolic phenotypes the microbe can have in a particular environment, as well as what would be its best strategy to thrive. These predictions enable us (i) to identify knowledge gaps through inconsistencies with experimental data, (ii) to explore experimental conditions for which no data is available, and (iii) to design genetically modified strains with a desired metabolic phenotype. First, to consolidate and expand the knowledge on Pseudomonas putida metabolism and the representation thereof in a GSM. Second, to use this GSM to design P. putida strains that either efficiently produce compounds of interest, or that have distinct lifestyles from the wildtype. To consolidate current knowledge on P. putida metabolism we created a computation tool to compare and combine independently generated GSMs for the same organism1. To expand the knowledge on P. putida metabolism we pinpointed knowledge gaps using P. putida GSMs and used these as a basis to functionally re-annotate its genome2. Currently, we’re using established methods such as Flux Balance Analysis and OptKnock to explore the current potential for model-driven strain design in P. putida.
 van Heck et al. PLoS Computational Biology 2016
2] Belda et al. Environmental Microbiology 2016
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