In recent years the ubiquity of microbial communities in nature has become apparent. For example, bacterial surface-associated communities, biofilms, are the most common mode of bacterial growth and are oftentimes resistant to environmental stress or antimicrobial treatment. However, it is still not clear how individual cells self-organize into these communities and how a community as a whole responds to environmental cues. This project aims to discover the mechanisms of self-organization in dynamic single-species biofilms (swarms). In these swarms, bacteria display collective surface motility, cooperatively sense the environment, execute collective developmental programs, and often differentiate into distinct cell types that perform specialized functions. Research on mechanisms of biofilm formation addresses questions similar to those in developmental biology; connecting macroscopic phenotypes and biochemical pathways in individual cells. Even when the genome composition of the cells is known, relating mutations affecting emergent spatio-temporal patterns to mechanisms of intercellular signaling and motility remains a challenge. Decoding these mechanisms from phenotypic observations is a complex reverse-engineering problem that cannot be solved solely by traditional experimental research. A complementary approach combining agent-based modeling and biostatistical image quantification with experimentation will address this problem. This research focuses on self-organization in spreading or aggregating biofilms formed by Myxococcus xanthus, an important model organism for studying microbial cooperation, development, and collective motility. This bacterium uses two motility systems and multiple sensory and signaling pathways to move over surfaces, forming a variety of population patterns. These patterns reveal motility coordination of individual cells as well as their ability to collectively sense and respond to environmental cues. This project will develop an approach to decode diverse phenotypes and uncover intercellular interactions using mathematical models that mimic experimentally observed patterns.
Broader Impact Broader impacts of the project include the development of new methods that bridge gaps between subcellular, cellular, and multicellular scales in spreading bacterial biofilms. Despite the focus on a specific model system (M. xanthus), the project will elucidate general mechanisms behind collective motility behavior. More than 50 bacterial genera use surface motility to form various types of dynamic biofilms, and many of these are important in industry. The methods and software developed for this project will be made available to the Myxobacteria research community worldwide. Developed tools and results of the research will be incorporated into a M. xanthus model organism database (xanthusBase) based on Wikipedia principles of community participation. Answering complex biological questions in the post-genomic era will require a new generation of life scientists with cross-disciplinary training in combining experimental and computational methods. To broaden the impact of this project, the PI seeks to improve and expand Systems Biology education on various levels, attract a diverse pool of talented students to the field of computational and systems biosciences, and contribute significantly to their training. The cornerstone of the educational component of this project is training for postdoctoral scholars, graduate students, undergraduate students, and high school teachers and their students. In addition the training includes a plan for outreach to include students from members of underrepresented groups.
