Oliver Lichtarge, M.D., Ph.D.

Professor
Department of Molecular and Human Genetics

Dr. Lichtarge received this award due to the focus of his computational work on the molecular basis of protein function and interaction; the design of peptides and proteins with new functions and the interpretation of genome variations in health and disease. In a recent paper, he and his colleague showed how high throughput of functional information such as protein expression and interaction can be integrated into a single gene network.

They then invented a compression technique to shrink the size of the network, making it tractable to the prediction of a large number of new functions for genes of many species. When they brought this technique to bear on the malarial gene Exp-1, they were for the first time able to determine its molecular function as a glutathione-s-transferase. In a translational finding, Joel Quiros, a graduate student in the Lichtarge laboratory, determined that artemisinin, the malaria drug of choice, inhibits the activity of this gene –important because the drug is an herbal derivative of unknown mechanism to which malaria in Southeast Asia is becoming resistant.

In a second paper, Lichtarge and his colleagues took on the problem of “big literature” using the protein p53 as an example. They mined the scientific literature to extract facts relating to interactions among biological molecules, complexes and pathways, putting these factors into a network and then using an algorithm to generate novel and testable hypotheses in molecular biology. They discovered multiple predicted new p53 kinases, mining the information from 24 million abstracts with the help of the Donehower Laboratory, the IBM WATSON research team and the U.S. Department of Defense DARPA program. This work is at the cutting edge of cognitive computing. In a third paper, Lichtarge and his compatriots took on the most basic problem in biology – how phenotype relates to genotype.

Applying calculus and perturbation theory to genotype variations and their phenotype response, he established a paradigm-shifting synthesis of the theories of Newton and Leibniz on differential analysis in which the physics rests with Darwin’s ideas on speciation and phylogenies. To be clear, the equation that results quantifies the evolutionary action of mutations on fitness and applies across all biological scales. In practice, it shows how to best interpret exome variations among individual to bring about the personalization of medicine.

Dr. Lichtarge's nomination was based on the following publications:

Lisewski AM, Quiros JP, Ng CL, Adikesavan AK, Miura K, Putluri N, Eastman RT, Scanfeld D, Regenbogen SJ, Altenhofen L, Llinás M, Sreekumar A, Long C, Fidock DA, Lichtarge O. Supergenomic network compression and the discovery of EXP1 as a glutathione transferase inhibited by artesunate. Cell. 2014 Aug 14;158(4):916-28. doi: 10.1016/j.cell.2014.07.011. PubMed PMID: 25126794; PubMed Central PMCID: PMC4167585.

Automated hypothesis generation based on mining scientific literature.