MODELING EMERGENT BEHAVIORS IN SYSTEMS BIOLOGY: A BIOLOGICAL PHYSICS APPROACH

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Project Summary Biology is full of stunning examples of emergent behaviors – behaviors that arise from, but cannot be reduced to, the interactions of the constituent parts that make up the system under consideration. These behaviors span the full spectrum of length scales, from the emergence of distinct cell fates (e.g. neurons, muscle, etc.) due to the interactions of genes within cells, to the formation of complex ecological communities arising from the interactions of thousands of species. The overarching goal of my research is to develop new conceptual, theoretical, and computational tools to model such emergent, system-level behaviors in biology. To do so, we utilize an interdisciplinary approach that is grounded in Biological and Statistical Physics, but that draws heavily from Machine Learning, Information Theory, and Theoretical Ecology. Our work is unified and distinguished by our deep commitment to integrating theory with the vast amount of biological data now being generated by experiment. An important goal of the proposed research is to find common concepts and tools that transcend traditional biological sub-disciplines and model systems. The proposed research pursues three distinct but conceptually interrelated research directions: (1) identifying the ecological principles governing community assembly in microbial communities and developing techniques for understanding function, diversity, and stability in microbiomes; (2) developing new mathematical and computational tools for modeling information processing in biochemical networks, especially the gene networks underlying cellular identity and the signaling networks that control collective behavior in the NIH model organism Dictyostelium discoideum; and (3) understanding and developing new interpretable machine learning techniques for systems and synthetic biology, with special attention paid to the unique challenges posed by living systems with regards to data heterogeneity, biological interpretability, and potential sources of bias. In addition to developing physics-based and machine learning-inspired models for diverse biological phenomena, the proposed research will yield a series of practical and important computational tools and algorithms which we will make publically available including: (1) our “Community Simulator” Python package for simulating microbial communities based on the novel microbial consumer resource model framework we have developed; (2) new machine learning and statistical algorithms for analyzing microbial communities and gene networks; and (3) new computational tools for predicting the behavior of synthetic biological parts and circuits in diverse contexts. These computational tools will allow scientists to leverage the power of modern theory, computation, and advances in Deep Learning to tackle fundamental problems relevant to human disease.