Contributed Talk 4: Metabolic Modelling in an Evolutionary Framework Predicts Phenotypic Diversification of E.coli growing on Glucose as the Single Carbon Source
Duration: 16 mins 58 secs
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| Description: |
Grosskopf, T (University of Warwick)
Thursday 11 September 2014, 17:05-17:20 |
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| Created: | 2014-09-15 14:24 |
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| Collection: | Understanding Microbial Communities; Function, Structure and Dynamics |
| Publisher: | Isaac Newton Institute |
| Copyright: | Grosskopf, T |
| Language: | eng (English) |
| Distribution: |
World
(downloadable)
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| Explicit content: | No |
| Aspect Ratio: | 16:9 |
| Screencast: | No |
| Bumper: | UCS Default |
| Trailer: | UCS Default |
| Abstract: | Co-author: Orkun S. Soyer (University of Warwick)
Understanding microbial communities is of great importance to monitor and manipulate complex ecosystems like anaerobic digesters, wastewater removal systems or the human gut. A variety of modelling approaches have been presented to simulate microbial communities, however many imply an optimization acting at the community level, an assumption which is not well grounded in evolutionary theory. We here present an evolutionary algorithm that uses multiple instances of flux balance analysis (FBA) models and density dependant selection as the fitness function, leading to the coexistence of different phenotypes of an Escherichia coli core FBA model after 500 rounds of simulated batch-transfers in a minimal medium with Glucose as the only carbon source. The solutions are “selfish” in the way that the only optimization is to maximize individual growth rate, yet they are affected by the metabolic layout of all other members in the community through a shared culture medium. We suggest that such self-optimizing models could be used to study complex microbial communities, where competition, cross-feeding, syntrophy, symbiosis and all forms of microbial interaction arise as emergent properties of the individual optimization of the member organisms. |
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