Building Predictive Coarse-Graining Schemes for Complex Microbial Ecosystems

Microbial communities play a defining role in global climate, agriculture, food safety, and environmental health. These systems are highly complex, often composed of hundreds of interacting species, which makes it very difficult to predict their behavior in detail. However, it is known that important ecosystem properties (e.g., the overall production of a nutrient) can sometimes be predicted without tracking every species, instead using simpler, coarse representations. Leveraging such "coarse-grainability" could transform our ability to predict and control these systems, but the phenomenon remains poorly understood: which properties are expected to be coarse-grainable, and under what conditions, is not known. This project will draw on methods of theoretical physics, statistics and learning theory to develop a systematic theory of ecosystem coarse-grainability, validate it experimentally, and translate it into practical software tools disseminated to the broader research community. This work will significantly advance our ability to model and predict the behavior of microbial ecosystems of complexity as found in nature, such as those responsible for global nutrient cycling, nitrogen fixation or other key environmental functions. On a more technical level, the objectives of the project include: (1) Establishing the theoretical and computational methodology for quantifying ecosystem coarse-grainability and identifying the Pareto front of the tradeoff between description complexity and its prediction error for a given community-level observable; (2) Validating this approach in the laboratory, applying it to communities of marine bacteria assembled in hundreds of environments spanning the axes of variation relevant for ocean ecosystems; and finally, (3) Translating the methodology into shareable software for identifying observable-specific predictive coarsening of compositional microbial data. Additionally, this award will support outreach activities targeting students of grades 6-12 in the St Louis area, emphasizing how the pursuit of predictable models of complex systems unites diverse areas of science (math, physics, biology and medicine, including this research), and increase the participation of underrepresented groups in research through the MIT Summer Research Program initiative. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.