Machine Learning Seminar

Speaker: Gennady Verkhivker, Chapman University and University of California, San Diego

Title: Modeling and Engineering of Regulatory Mechanisms in Biomolecules: A Symbiotic Platform of Computational Biophysics, Network Science and Machine Learning Approaches

Refreshments available at 2 pm.

Date: Mon, 10 Sep 2018, 2:15 pm – 3:15 pm
Type: Seminar
Location: 1400 BPS Bldg.

Abstract:
The allosteric interactions and regulation of signaling proteins allow for molecular communica­tion and event coupling in signal trans­duction pathways and net­works. We report the results of integra­tive sys­tems biology studies of signal­ing proteins offer­ing novel in­sights into organ­izing prin­ciples of regula­tion. Network model­ling and percola­tion anal­ysis approaches were used to emulate ther­mal unfolding and character­ize confor­mational land­scapes of a wide range of signaling proteins that explained diver­gences in their regulatory mechan­isms. The results of bio­physical and compu­tational systems biology analyses com­bined with proteomics experi­ments have been incor­porated into a graph-based net­work model of allo­steric regu­lation. A symbio­sis of computa­tional sys­tems biology and machine learn­ing approaches is then used to con­struct models of allo­steric regu­lation of onco­genic pro­teins in signaling cas­cades. Based on these findings, we developed a compu­tational syn­thetic biology frame­work for design and re-engineering sig­nal trans­duction net­works and path­ways that in­volve cross-talk be­tween molecu­lar chaperones and pro­tein ki­nase pro­teins… We have also analyzed mecha­isms by which can­cer drugs may act syner­gistically and exert their pharma­cological ef­fect by modula­ting pro­tein inter­action net­works. Our study offers a sys­tems-based per­spective on drug design and re-engineering of sig­naling net­works by unravel­ling relation­ships be­tween pro­tein ki­nase net­works with molecular chaperones and bind­ing speci­ficity of targeted ki­nase drugs.

Speaker Bio:
Dr. Verkhivker is currently Professor of Computa­tional Bio­sciences and Trans­lational Medicine at Col­lege of Science & Technology, Chapman Univer­ity and Pro­fessor at the Depart­ment of Bio­medical and Pharma­ceutical Sciences at Chapman Univer­sity School of Pharmacy. He is also Adjunct Professor of Pharmacology at the Depart­ment of Pharma­cology, and Skaggs School of Pharmacy and Pharma­ceutical Sciences, UC San Diego. Dr. Verkhivker received his PhD in Physical Chemistry from Moscow State Univer­sity and com­pleted a post­doctoral fellow­ship in computa­tional biophysics from University of Illinois at Chicago in 1992. Dr. Verkhivker was one of the founding scientists at Agouron Pharmaceuticals Inc, in early 1990s and played a leading role in estab­lishing computer-aided structure-based design technology. In 1993–2006, Dr. Verkhivker has held various research and manage­ment positions at Agouron Pharma­ceuticals, Warner-Lambert, Pfizer Glo­bal Research and Develop­ment. He has been Direc­tor of Computa­tional Sciences at Pfizer Global Research and Develop­ment, in 2001–2005. Since 2002, he has been Adjunct Pro­fessor of Pharma­cology at the Skaggs School of Pharmacy and Pharma­ceutical Sciences, UC San Diego. In 2006, he joined School of Pharmacy and Cen­ter for Bio­informatics, The Univer­sity of Kansas as a Full Pro­fessor of Pharma­ceutical Chemistry and Bio­informatics. In 2011 Dr. Verkhivker assumed position of Full Pro­fessor of Compu­tational Bio­sciences & Trans­lational Medicine at Schmid College of Science & Tech­nology and Founding Pro­fessor at Chapman Univer­sity School of Pharmacy. Dr. Verkhivker authored more than 150 peer reviewed publica­tions and is recog­nized for his research contri­butions in the fields of trans­lational bio­informatics, computa­tional bio­physics, computa­tional and mathemat­ical biology. His most recent research activities are in the areas of compu­tational systems biology, trans­lational bio­informatics, net­work science and arti­ficial intel­ligence with the focus on applica­tion of these approaches in trans­lational cancer research.