Collaborative Research: Decoding the Self-Organization Mechanism during Myxococcus Xanthus Multicellular Development with Quantitative Experiments and Mathematical Modeling

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One of the long-standing puzzles of developmental biology is how multicellular organisms develop from a single, embryonic cell into an organized 3-dimensional animal with tissues and organs of diverse function. Self-organization processes rely on ordered cell growth, directed motility, and physical and chemical interactions between cells. Albeit a simpler system, these same processes occur when Myxococcus xanthus cells organize into a multicellular fruiting body and differentiate into spores. The underlying dynamics of this collective behavior will be uncovered using genetic and biochemical experiments, cell tracking studies, and computer simulations. Such a multidisciplinary approach is crucial to answering complex biological questions and will train a new generation of interdisciplinary life scientists ready for the challenges of 21st century biology. Moreover, with a focus on a specific model system, this project is expected to elucidate general principles of cellular cooperation and self-organization that have broad practical implications. When faced with nutritional stress, biofilms formed by Myxococcus xanthus cells engage in a collective developmental program to form multicellular fruiting bodies in which some cells differentiate into spores. Despite tremendous progress in understanding how these cells move, signal one-another and differentiate, a mechanistic picture of how aggregation into fruiting bodies occurs is still lacking. Recent results call into question all existing models of the phenomena. The goal of this proposal is to develop a synergistic experimental and modeling approach to explore various mechanisms for aggregation. Quantitative cell-tracking and statistical image analyses will be performed with wild type cells and various developmental mutants or hybrid communities to determine how cells assemble into aggregates and how cell behavior changes in aggregates that disperse. This data will be used to formulate hypotheses about the mechanism of aggregation that would be tested with the help of a computational, agent-based model. Mutant strains in which gene deletions result in aggregation defects as well as mixtures of different strains will be used to refine the model. With each experiment and simulation, the models will be iteratively aligned to reverse-engineer hypotheses about the aggregation mechanism. To compare the aggregation patterns generated by different strains and to quantify the agreement of the simulations with experimental observations, a set of parameters (features) that comprehensively characterize the aggregation patterns will be defined. These parameters will allow use of hierarchical clustering to classify developmental mutants into mechanistic pathways that affect cellular aggregation.