URoL:EN: A non-parametric framework to understand emergent behaviors of microbial consortia

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The goal of this project is to develop a new framework to better understand the emergent behaviors of microbial consortia. Emergent behavior is defined as a system’s behavior that arises from the interactions between its individual parts and that cannot easily be predicted by the behavior of these individual parts. For example, researchers found that when subjected to antibiotics, the overall population consisting of both resistant and non-resistant cells grows faster than the individual resistant mutants. Because such behaviors cannot easily be extrapolated from that of individual parts of a system, their study has been based on observation rather than prediction. This project will provide a novel framework to predict emergent survival properties of a synthetic community consisting of individual microbial specialists or generalists that are designed to degrade specific toxic aromatic compounds. This interdisciplinary research is innovative and will advance our knowledge on emergent behaviors of complex living communities. This innovative project will also bring broader impacts to the global synthetic biology community, microbiome scientists, mathematics experts, relevant industry, and future generations. In addition, the team will engage the research community through Synthetic Biology Young Speaker Series, a weekly, virtual, and multi-year forum where a global thought leader gives an opening 5 min talk, followed by a 45 min, rising star’s talk, for 1,000 global audiences. Furthermore, the project will increase K-12 students’ participation in STEM education, focusing on the St. Louis area where most of the K-12 students are underrepresented students. Through these activities, this project will nurture our future leaders in the converging field of research and industry to sustainably meet global chemical and energy demands and address climate crisis issues.This project will use diverse interdisciplinary approaches to develop a non-parametric estimation framework as follows. First, the team will rationally design and create microbial strains that can degrade a specific set of model lignin breakdown products. Second, the team will develop generalizable tools for statistical inferences, including tools for mean function estimation, simultaneous confidence band construction, estimation of joint effects, and breakpoint estimation. Third, the team will perform carefully-designed microbial community assembly and adaptive evolution. From these efforts, the project will generate new quantitative insights into emergent survival properties in synthetic microbial consortia and provide reusable tools for rigorous inferences of emergent properties in various microbial consortia. This project has huge impacts on the scientific community and society thanks to the following reasons. First, the developed framework is generalizable because it does not rely on specific function forms and parameters. Second, the rational and iterative approach of strain design, building, and testing will validate the putative gene functions, filling the critical knowledge gaps. Notably, this work will expand our understanding of the regulatory network controlling aromatic compound catabolism in Rhodococcus opacus, with broad applicability to related organisms. Third, the integrated approach will generate quantitative insights into community assembly and functional redundancy in consortia of rationally constructed organisms. Last, the parallel evolution experiments will also generate insights into evolutionary processes of collective systems and their two forces: robustness and adaptability. Using diverse microbial consortia as model systems, the team will provide the insights into the complex evolution process, enabling quantitative analysis of robustness and adaptability of collective systems. This project will also train graduate students and postdoctoral researchers in an interdisciplinary manner, enabling unique training of the future workforce and educators in chemical engineering, synthetic biology, microbial ecology, and mathematics.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.