Summary
Given a list of candidate species, which community should one form to optimize a target function? Answering this fundamental ecological question is particularly urgent in the emerging field of synthetic microbial ecology, where it would dramatically improve our ability to design multi-species consortia for biotechnological applications. An empirical solution is out of reach, due to the astronomical number of possibilities: one could form ~1E30 possible assemblages from just 100 species. To provide a solution, ECOPROSPECTOR will borrow state-of-the-art ideas from Evolutionary Systems Biology and the theory of Fitness Landscapes, where similarly vast combinatorial spaces must be explored to find optimal sequences (peaks) in a genotype-phenotype map. This will result in a new and groundbreaking theoretical paradigm to map community composition and function in large communities at the single-species level.
My proposal is motivated by exciting preliminary results revealing an ecological parallel to the emerging evolutionary concept of global epistasis: the functional effect of adding a species to a community can be predicted by a simple mathematical relationship. I will start by characterizing these relationships in a model empirical system consisting of 100 starch-degrading soil bacteria. I will then use machine learning to reconstruct and navigate the full combinatorial landscape between community composition and function, in search of communities that optimize the rate of starch hydrolysis. Through genetic and environmental manipulations and mathematical modeling, I will then mechanistically explain the emergence of those predictive equations and causally link them with species traits. Besides solving a problem of critical practical importance, the theoretical paradigm emerging from this work will unify quantitative research in ecology and evolution, providing unique opportunities for cross-pollination across fields.
My proposal is motivated by exciting preliminary results revealing an ecological parallel to the emerging evolutionary concept of global epistasis: the functional effect of adding a species to a community can be predicted by a simple mathematical relationship. I will start by characterizing these relationships in a model empirical system consisting of 100 starch-degrading soil bacteria. I will then use machine learning to reconstruct and navigate the full combinatorial landscape between community composition and function, in search of communities that optimize the rate of starch hydrolysis. Through genetic and environmental manipulations and mathematical modeling, I will then mechanistically explain the emergence of those predictive equations and causally link them with species traits. Besides solving a problem of critical practical importance, the theoretical paradigm emerging from this work will unify quantitative research in ecology and evolution, providing unique opportunities for cross-pollination across fields.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101088469 |
| Start date: | 01-03-2023 |
| End date: | 29-02-2028 |
| Total budget - Public funding: | 1 991 470,00 Euro - 1 991 470,00 Euro |
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Original description
Given a list of candidate species, which community should one form to optimize a target function? Answering this fundamental ecological question is particularly urgent in the emerging field of synthetic microbial ecology, where it would dramatically improve our ability to design multi-species consortia for biotechnological applications. An empirical solution is out of reach, due to the astronomical number of possibilities: one could form ~1E30 possible assemblages from just 100 species. To provide a solution, ECOPROSPECTOR will borrow state-of-the-art ideas from Evolutionary Systems Biology and the theory of Fitness Landscapes, where similarly vast combinatorial spaces must be explored to find optimal sequences (peaks) in a genotype-phenotype map. This will result in a new and groundbreaking theoretical paradigm to map community composition and function in large communities at the single-species level.My proposal is motivated by exciting preliminary results revealing an ecological parallel to the emerging evolutionary concept of global epistasis: the functional effect of adding a species to a community can be predicted by a simple mathematical relationship. I will start by characterizing these relationships in a model empirical system consisting of 100 starch-degrading soil bacteria. I will then use machine learning to reconstruct and navigate the full combinatorial landscape between community composition and function, in search of communities that optimize the rate of starch hydrolysis. Through genetic and environmental manipulations and mathematical modeling, I will then mechanistically explain the emergence of those predictive equations and causally link them with species traits. Besides solving a problem of critical practical importance, the theoretical paradigm emerging from this work will unify quantitative research in ecology and evolution, providing unique opportunities for cross-pollination across fields.
Status
SIGNEDCall topic
ERC-2022-COGUpdate Date
31-07-2023
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