ABI Innovation: Towards Recovery of Biological Information

Baylor College Of Medicine, Houston TX

Investigators

Abstract

Baylor College of Medicine is awarded a grant to develop novel techniques of network analysis, rooted on the evolutionary principle of continuity, to tackle the fundamental problems of biological data integration and analysis. A problem in biology is that experimental information accumulates far faster than it can be analyzed. These data are complicated, incomplete, and noisy, slowing down their interpretation, but a major difficulty is that because they arise from different experiments they are analyzed separately, or in small groups. This project aims instead to tie all biological data into one network so that each bit of information is understood in light of all other data. To make this approach feasible we propose a new, efficient use of evolutionary relationships to coalesce experimental results arising from hundreds of species into a single network that, although massive, is still easy to compute over. Another insight is that neighboring regions of the network are likely to carry out related biological functions. This hypothesis translates into precise mathematical rules to spread information across the entire network and, as much as possible, resolve contradictions. In practice, these network and computational tools will predict various aspects of protein function that will then be tested experimentally across a broad range of applications (p53, a central gene in animal biology; in proteins that regulate stress adaptation in bacteria; and in malarial proteins). Together computation and experiments will assess the value and limitations of this novel and versatile network approach to discover the molecular origin of function in any organism. The project will fulfill many goals of broader societal significance. First, it will validate novel techniques of biological network analysis that can be used in any area touched by research in molecular biology; this includes biotechnology, bioengineering, nanotechnology, agriculture, renewable energy production, and synthetic biology applications for industrial processes. Second, it is also important to note that the scientific results fall in the area of BIG DATA analytics, which cuts across science, finance, social and national defense areas of interest. In that light, third, we note that the projects will train both postdoctoral scientists, graduate students, and offer summer internships to undergraduate students, including some from programs supporting minority education and research, who will therefore be able later to contribute their skills in Big Data analytics to many fields of national interest.