Speaker: Yang Shen
Time: 2017-12-28 14:00-2017-12-28 15:00
Venue: FIT 1-222

Abstract:

Living cells can be viewed as systems of interacting biomolecules whereas therapeutics can be viewed as controls to perturb diseased systems.  A major barrier to effective therapeutics, especially for cancers and infectious diseases, is that these systems are highly adaptive — under selective pressures of therapeutics, they will inevitably evolve therapeutic resistance often through mutating therapeutic targets.  Current drug discovery and development processes, although extremely costly and lengthy with a rather low success rate, are often reacting to therapeutic resistance post hoc. 

In this talk, I will present a computational scheme to address this challenge a priori.  Specifically, we adopt an inverse approach to formulate the problem as combinatorial protein design, treat the formulation as correlated constraint satisfaction problems, and develop an exact algorithm iCFN (interconnected cost function networks) that guarantees the optimal and sub−optimal solutions. Numerical results indicate that iCFN significantly outperforms the only alternative exact algorithm and enables solving real-world large-scale problems. When applied to model oncogenic mutations in metastatic breast cancer, iCFN correctly predicts clinically-validated resistance mutations and further provides mechanistic rationales. 

If time allows, I will also introduce our other research projects on developing optimization, machine learning, and other algorithms for modeling biological molecules, systems, and data. 

Short Bio:

Dr. Yang Shen received his B.E. from the University of Science and Technology of China, Ph.D. in Systems Engineering from Boston University, and postdoctoral training in Biological Engineering and Electrical Engineering & Computer Science at the Massachusetts Institute of Technology.  He is currently an Assistant Professor in the Department of Electrical and Computer Engineering and the Center for Bioinformatics and Genomic Systems Engineering at the Texas A&M University.  His research interests are in optimization and learning algorithms for modeling biological molecules, systems, and data.  Applications include protein docking, protein and drug design, systems and synthetic biology, and omics. He is an awardee of Maximizing Investigators’ Research Award (MIRA) from the National Institute of General Medical Sciences of the National Institutes of Health.  More information about Dr. Shen’s research can be found at https://shen-lab.github.io/

Postdoc and Ph.D. openings are available in Dr. Shen’s lab on sequence analysis, protein modeling, systems biology, omics, data mining, and machine learning.