This project (ECOPROSPECTOR) focuses on addressing a key open question in microbial biotechnology: Given a library of candidate species, which should we inoculate together in a bioreactor to form a community that optimizes a biotechnological process of interest, such as the production of high-value molecules? Traditional approaches to answer this question have struggled due to the complexity and astronomical number of potential interactions among multiple species. For example, choosing optimal communities from a pool of just 100 microbial species involves navigating approximately 10³⁰ possible assemblages. As a result of these interactions, it has been difficult to predict how adding or removing a single species might boost or hinder the overall performance of the communities.
ECOPROSPECTOR addresses this bottleneck by leveraging conceptual and methodological tools of Quantitative Genetics, a field that has tackled the similar problem of similarly vast combinatorial spaces (of mutations, in that case) and similarly complex potential interactions (which are known in genetics as “epistasis”). The objective of ECOPROSPECTOR is to extend the theory of epistasis in genetics to ecology, using it to develop a new theoretical and computational paradigm for predicting the quantitative properties (or functions) of microbial communities based on their composition at the single-species level. This approach is supported by preliminary findings suggesting that the effect of adding a species to a community can often be forecasted by a simple, yet powerful, mathematical relationship that mirrors similar findings in genetics.
The project begins with an empirical focus on a tractable library of soil bacteria, aiming to identify and model these predictive relationships between community composition and function. Machine learning tools will then be used to reconstruct the high-dimensional landscape connecting community composition to ecosystem function (the production and secretion of biotechnologically relevant molecules, such as enzymes or peptides). This theoretical map will guide the search for optimal communities. Genetic, environmental, and modeling techniques will be employed to mechanistically explain the discovered patterns and link them to specific species traits.
The anticipated impact will be large, both practically (by enabling rational design of high-functioning microbial consortia for industrial or environmental use) and theoretical—laying the groundwork for a unifying framework that bridges evolutionary and ecological theory, with wide-reaching implications for both fields. We envision three important axis of impact
Expected Impact
• Fundamental scientific knowledge: By determining how species interactions shape community function, we’ll help push microbial ecology to a new level of quantitative understanding.
• Industrial and clinical applications: Our methods can benefit areas like biotechnology, where microbial consortia are key (e.g. fermentation, food production, antibiotic resistance research).
• Innovation: If we can reliably predict community behavior, this will pave the way to new collaborations, industrial partnerships, and potentially spin-off applications (like improved microbial production platforms).
