Colloquium speaker is hosted by the Mathematical Biology Seminar group.
Abstract: Many problems in engineering, biology, and medicine have a control component where the objective is to modify a component of the model (such as a set of nodes and edges in a network model) to obtain a desired effect in the system or to drive the system into a desired state. For instance, in cancer modeling, often the objective is to induce cells to achieve a certain desired phenotype (e.g., programed cell death). In this talk, I will discuss the interplay of different approaches for phenotype control. I will focus on control methods for Boolean networks including algebraic methods, feedback vertex sets, and stable motifs. I will discuss the fundamentals and the requirements for using each of these methods. I will present examples of applications of these approaches on specific models and then compare the results. Finally, I will discuss the challenges such as the complexity and the availability of software for using each of these control methods.
David Murrugarra is an Associate Professor in the Department of Mathematics at the University of Kentucky. He received his PhD from Virginia Tech in 2012. He was a postdoc in the School of Mathematics at Georgia Tech before moving to Kentucky in 2014. His research program focuses on the development of computational tools for modeling, analysis, and control of regulatory networks. His research interests also include the computational prediction of RNA secondary structure using machine learning techniques. He has been a research mentor for high school, undergraduate, and graduate students