Statistical computation for deciphering biological outcomes in proteins and cells

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We are witnessing advances in experimental techniques that have enabled the rapid collection of biological data at the levels of proteins and cells. The Protein Data Bank of known 3-D protein structures has been growing quickly. Improved imaging techniques permit cellular motions to be tracked with greater detail. The components of many dynamical systems can now be measured more precisely and closer to real-time. As a result, we have access to datasets that are not only large, but with a high level of complexity and structure. These scientific developments, in turn, provide fertile ground for the development of novel approaches that leverage these data to answer important biological questions.  Statisticians have a key role in quantifying uncertainty that arises in the scientific process, by building models and methods that yield new insights and improve our capacity to make predictions of biological outcomes. This proposal advances a research program for developing the necessary analytical and computational methods to advance our scientific understanding of the complex biological mechanisms governing proteins, genes, and cells, and the interplay between them. Some specific questions to be addressed, in light of data currently available to us, are as follows.  How can sampling methods in statistics be adapted and combined with data-driven learning to further improve our ability to predict 3-D protein structure from amino acid sequence?  How can we better understand the links between molecular mechanisms in cells with cellular-level outcomes such as motility and migration?  How can we better fit models to noisy data collected from monitoring biological systems?  Together, we anticipate integrating knowledge from these interconnected biological facets - proteins, genes, and cells - and providing solid statistical foundations for their study. The methods to be developed will incorporate techniques of modern statistical computation, especially from the realms of Bayesian inference and Monte Carlo methods.  We will also fully utilize modern hardware for computation, leveraging mass parallelization as well as both CPU and GPU computing.  In particular, handling these biological "big data" requires efficient and carefully-tailored algorithms that also fit within practical constraints of time and computing. An important long-term goal of the proposed research is its practical biomedical impacts. In drug discovery, there is increasing interest in designing amino acid sequences to achieve specific 3-D structure and function.  In pathology, abnormal migratory signals may cause erroneous cell migrations; a thorough understanding of cell migration mechanisms is therefore essential in medical and therapeutic domains, including cancer research.