NSF Award
2325171
Our world is dominated in many respects by communities of single celled organisms, e.g., bacteria, archaea and non-filamentous fungi. Such communities are key players in all geochemical cycles; they are present in all multicellular organisms, including humans, where in addition to possibly harmful effects, they also have essential beneficial roles. They are ubiquitous in engineered systems as well where, again, they can be beneficial (e.g., waste water treatment) or harmful (e.g., drinking water distribution). In large part, microbial communities interactions with their environments are metabolic through chemicals they take in, products they make with these inputs, and byproducts they excrete, so understanding these metabolic capabilities is essential for understanding how microbes effect their surroundings. Advances in genetic sequencing are making it easier and easier to determine microbial "machinery" (enzymes); researchers are becoming increasingly adept in predicting how this "machinery" combines into "assemblage lines" (metabolic pathways). Armed with this knowledge, the next step is to understand how these "assembly lines" fit into their environment into a sort of large scale "distribution system" that determines overall microbial community function. At large scale, this becomes a challenging computational problem, and current methods are not adequate. This project aims to accelerate these computations by introducing machine learning tools into key bottlenecks in the algorithms. The project is a collaboration between Temple University, Montana State University, and the University of California, San Diego and offers valuable educational, training, and outreach opportunities. <br/> <br/>Activity funded by this proposal would center on development of computational methods, based on machine learning and artificial intelligence assisted optimization, that are sufficiently efficient so as to make it possible to embed complex cell-scaled models of microbial behavior (metabolic and gene expression models, so-called ME modes) into environmental scale PDE-based models of microbial community activity. In support of this effort, ME models of several specific organisms (S. aureus, S. epidermidis, B. subtilis) will be adapted for use in specific environmental-scale models (basic biofilm communities and built-environment subaerial communities). In complement, environmental-scale continuum-mechanics-based (partial differential equation) models for these systems will be constructed and adapted for use with the new computational methods. AI will also be applied at this macroscale to attempt to identify key metabolic processes at the large scale.<br/><br/>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.
