Computation helps evaluate the nature of the NIH research portfolio in ways that were previously very difficult.
Several big-dollar initiatives received NIH funding in late 2010
Advances in visualization changing work flows for understanding molecular dynamics, tracking cell movements, and designing interventional procedures
The complexity and variability of aging itself, along with the fragmented nature of researchers’ current understanding of aging, call for tools that can help scientists dig through mounds of data to find often subtle connections.
Researchers are expanding the types of data that can be used to predict infectious disease spread; developing novel ways to analyze that data; and trying to create systems that can help address public health problems today
HAP-SAMPLE takes real data as the template for simulations
Computing using time steps -- a necessary approximation
Using computational models, researchers are gaining traction toward understanding what makes a stem cell a stem cell; how gene expression drives stem cell differentiation; why studying stem cell heterogeneity is important; and, ultimately, how stem cells control their fate.
Having developed detailed and sophisticated models of both E. Coli and human metabolism, researchers can begin to build toward a whole cell model that will be useful for the study of human health and disease.