CAREER: Signal Crosstalk Within Complex Microbial Ecosystems

University Of Southern California, Los Angeles CA

Investigators

Abstract

Microbial ecosystems in nature are typically composed of hundreds or thousands of microbial species, heterogeneously distributed in space and time. New experimental and theoretical tools are needed to develop a predictive understanding of the regulatory processes that control the outputs of these complex cellular networks. Current approaches focus on correlations between the components of the system (such as genetic composition, expression levels, metabolite concentrations), however such correlations alone are usually insufficient to develop strategies for robust control of the state and function of diverse microbial communities. In this project the PI will use multiscale approach that combines theoretical predictions with precision experimental tests to quantitatively understand how individual and groups of cells reach decisions to coordinate global behavior within microbial ecosystems. Tackling modern societal challenges related to health, agriculture, and the environment will benefit from a predictive understanding of how multiple components of a network work together, particularly for complex networks involving many densely connected components. To inspire the next generation of scientists to take up problems on the physics of systems and complexity, the PI has partnered with a local science non-profit to design and implement hands-on learning activities related to the biophysics of signal exchange. Students will gain an intuitive understanding of the physical principles that govern cellular networks, including insight into how network properties emerge from the behavior of individuals within a population. Themes of this research will also appear in an undergraduate general education course focused on training undergraduates from diverse background, including non-science majors, in essential quantitative skills intrinsic to the physical sciences. The PI will examine the exchange of variant quorum sensing signals within diverse microbial networks to understand how signaling interactions between individual strains give rise to emergent properties of the network. An artificial neural network model predicts the impact of crosstalk between cell types on system-level signaling states. The stability of these signal-driven regulatory states will be examined in both models and experiments to gauge the potential to direct the outputs of multispecies networks through perturbations of species composition or signal exchange pathways. This combination of theory and experiment illuminates how systems-level behaviors such as robustness emerge from the collective action of multiple cell types working together. 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.