The analysis of human disease has profited enormously from systematic application of reductionist methods. But as we learn more about biological systems, we realize that health and disease are due to complex networks of interactions across multiple levels. Simple systems often do not provide robust results or reliable predictions. Systems genetics is a new hybrid of genetics and systems biology that models the consequences of genetic variation and the impact of environmental perturbations on biological processes and disease risk.
In this talk I describe key experimental and computational systems needed for systems genetics. Experimental and replicable populations of isogenic lines of mice that incorporate the same level of genetic complexity as humans are a key requirement. These new murine Reference Populations or families promise to revolutionize our ability to deliver personalized and predictive health care to humans over the next 100 years. Constructing and testing complex biological models is a computational and sociological challenge. I will review some of the latest progress we and other groups are making on building open on-line computational services for systems genetics.