SOURCE-LINKED INTELLIGENCE

Mapping vast functional landscapes with single-species resolution: a new approach for precision engineering of microbial consortia

CORDIS · observation · Publication date unknown

an 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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recordType

award

status

SIGNED

region

EU

value

1991470

unit

EUR