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Thursday, December 5, 2013 Talk:
4:00 p.m., Avery 115
Reception:
3:30 p.m., Avery 348
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Tomas Helikar, Ph.D.
Assistant Professor, University of Nebraska-Lincoln (Biochemistry)Abstract
Qualitative computational frameworks, especially those based on the logical discrete (e.g., Boolean) formalism, are widely used to model biochemical and biological networks. A major advantage of these frameworks is that they do not require quantitative (kinetic) parameters, and that they are well-suited for studies of large-scale networks and their nonlinear dynamics and emergent properties.
In this talk, discussed will be examples of some of the largest logical models created to study various diseases, including HIV, breast cancer, and influenza. Specifically, this computational framework has been applied to identify most/least influential components based on their perturbation effects on the dynamics of the rest of complex biochemical network under a number of different disease conditions.
I will also share various software technologies developed to provide new tools to build, simulate, and analyze large-scale logical models, as well as to share these models via a new extension of the Systems Biology Mark-up Language (SBML). Finally, I will explore new opportunities for innovation in the area of STEM education in life sciences through the use of interactive computational modeling.
Speaker Bio
Tomas Helikar received his Ph.D. in computational systems biology/bioinformatics from the University of Nebraska Medical Center in 2010. Prior to joining the Biochemistry Department the University of Nebraska–Lincoln, Tomas was a Research Associate in the Department of Mathematics at UNO. His research focus is three-fold: 1) using large-scale computational models to better understand the dynamics of molecular and cellular mechanisms in complex networks, namely ones governing the immune system; 2) development of software technologies to enable more efficient and integrated utility of computational systems biology; and 3) implementation of interactive computational modeling in life science courses to improve STEM education.