Mathukumalli Vidyasagar is currently Cecil & Ida Green Chair in Systems Biology at the University of Texas at Dallas. His current research interests are in stochastic processes and their applications to computational biology with special emphasis on cancer biology, and to mathematical finance. He was born in Guntur, India on September 29, 1947. He received the B.S., M.S. and Ph.D. degrees in electrical engineering from the University of Wisconsin in Madison, in 1965, 1967 and 1969 respectively. Between 1969 and 1989, he was a Professor of Electrical Engineering at various universities in the USA and Canada. His last overseas job was with the University of Waterloo, Waterloo, ON, Canada, where he served between 1980 and 1989. In 1989 he returned to India as the Director of the newly created Centre for Artificial Intelligence and Robotics (CAIR) in Bangalore, under the Ministry of Defence, Government of India. In 2000 he moved to the Indian private sector as an Executive Vice President of India's largest software company, Tata Consultancy Services, where he created the Advanced Technology Center, an industrial R&D laboratory of around 80 engineers. In 2009 he retired from TCS and joined the Erik Jonsson School of Engineering & Computer Science at the University of Texas at Dallas, as a Cecil & Ida Green Chair in Systems Biology. In March 2010 he was named as the Founding Head of the Bioengineering Department. He is a Fellow of several scientific societies including IEEE, and has received several awards in recognition of his research, including the IEEE Control Systems Award in 2008. Earlier he had received the Hendrik W. Bode Lecture Prize in 2000, and the Distinguished Service Citation from his alma mater, the University of Wisconsin.
Recent advances in experimental techniques have made it possible to generate an enormous amount of `raw' biological data, with cancer biology being no exception. The main challenge faced by cancer biologists now is the generation of plausible hypotheses that can be evaluated against available data and/or validated through further experimentation. For persons trained in control theory, there is now a significant opportunity to work with biologists to create a virtuous cycle of hypothesis generation and experimentation. In this talk, we discuss four specific problems in cancer biology that are amenable to study using probabilistic methods. These are: reverse engineering gene regulatory networks, constructing context-specific gene regulatory networks, analyzing the significance of expression levels for collections of genes, and discriminating between drivers (mutations that cause cancer) and passengers (mutations that are caused by cancer or have no impact). Some research problems that merit the attention of the controls community are also suggested.