Postdoctoral/Visiting Fellow
 
DIMACS, Center for Discrete Mathematics
& Theoretical Computer Science
Rutgers, The State University of New Jersey
96 Frelinghuysen Rd, Piscataway, NJ 08854
and The Mathematics Department
Rutgers, The State University of New Jersey
 
E-mail: mveralic at math.rutgers.edu
 
Professional Preparation Ph.D., Mathematics, Virginia Tech, 2007 Master of Science, Mathematics, Virginia Tech, 2003 B.S. in Mathematics, Universidad Nacional Autonoma de Mexico (UNAM), 2001 Research Interests  My research interests arise within the area of mathematical biology, specifically systems biology and systems neuroscience. I am interested on the development, software implementation and application of mathematical algorithms for the modeling and simulation of biological networks. Biological applications include inference and analysis of gene regulatory, metabolic and brain connectivity networks as well as signaling pathways and modeling of stem cell dynamics and morphogenesis. The mathematical areas that I commonly use computational algebra, combinatorics, graph theory and finite dynamical systems. Application tools also include evolutionary and deterministic algorithms within the Finite Dynamical Systems framework, specifically FDS’s described by polynomial functions over a finite field also called Polynomial Dynamical Systems (PDS). With my training in algebra, combinatorics and computational algebra, I find the PDSs mathematical framework to be very useful and fruitful from its extensive power to generalize some of the well-known modeling frameworks such as Boolean networks, Cellular Automata, certain types of Petri nets and Logical Models. I am particularly interested in problems on network inference, also known as reverse-engineering or inverse problems. Much of my research has been strongly related to the different tasks involved within the reverse-engineering modeling paradigm for discrete systems and that can be summarize as follows: (1) Data Discretization, Static and Dynamic Network Inference, (3) Reverse Engineering Validation and Benchmarking, (4) Model Simulation and, (5) Applications of these developed algorithms. More recently at Rutgers university, I am developing methods based on dynamic Bayesian networks and hybrid agent-based models. ../REACT/REACT.htmlhttp://www3.interscience.wiley.com/journal/117986052/abstract?CRETRY=1&SRETRY=0../PDS%20Reverse%20eng/PDS%20Reverse%20Engineering.html../Stem%20Cell/Stem%20Cell%20%20Model.htmlshapeimage_1_link_0shapeimage_1_link_1shapeimage_1_link_2shapeimage_1_link_3
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