More than a decade after the completion of the Human Genome Project, our ability to predict important high-level phenotypes from molecular information at the cellular level remains woefully inadequate. Statistical mapping between variants identified by genome -wide association studies and complex traits such as hypertension do not effectively explain the range of phenotypes in the population, nor do they provide useful predictions of disease risk. In short, the standard machinery of statistical genetics has fallen short as a tool to understand complex disease. This provides the opportunity and motivation for a more comprehensive approach to the grand challenge of understanding the mechanistic relationships between high-level phenotypes and molecular information.
Multi-scale simulation of physiological systems represents a powerful vehicle for linking multiple levels of causality. Mathematical modeling in combination with high-performance computing and high-resolution data has led to tremendously sophisticated and reliable multi-scale multi-physics based simulations of certain physiological systems. In particular, system dynamics from the cellular to the system levels have long been studied using mathematical modeling, for example, computer models of the heart. Yet such dynamics models rarely make any use of data gathered at the molecular level, and therefore cannot capitalize on the emerging availability of patient data collected at multiple scales (e.g. genome information). This workshop will discuss the state-of-the-art mathematical techniques (and outstanding needs) for effectively synthesizing data ranging from genomic through molecular and organ up to the system level with multi-scale computational techniques. Efforts will be focused on addressing how models can be adapted to couple data measured at different scales and from different species, yet belong to the same physiological system. This question will be studied within the respiratory, cardiovascular, and renal systems. We expect that it is possible to extract common features from these systems, and that techniques applied will have applicability outside the systems studied.
This workshop will bring together domain experts from physiology, mathematics, and statistics. Physiologists and statisticians will help identify key data sets of interest and address questions related to uncertainty in data sampling, including discussion of known variation within species, and between in-vivo and in-vitro sampling. Mathematicians will bring expertise in modeling, model reduction, and solving inverse problems. The aim will be to discuss ways to combine data from multiple sources and scales with relevant models to predict patient specific responses. New techniques that have shown promise for solving these types of problems include reformulation of models using techniques from algebra, uncertainty quantification, parameter estimation, and networks. This diverse group of researchers will have potential to generate new projects and ideas for linking statistical and physics-based techniques for building multi-scale mathematical models that incorporate physiological data from multiple sources and scales, which may eventually elucidate relationships between phenotypes and the underlying physiology.