David Murrugarra
- Mathematical Biology
PhD in Mathematics, Virginia Tech, 2012.
M.S. in Mathematics, Virginia Tech, 2007.
B.S. in Mathematics, Universidad Nacional Mayor de San Marcos, Lima, Peru, 2004.
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My research has concentrated on the development of methods for modeling, analysis, and control of gene regulatory networks. I have focused on discrete methods, which employ techniques from discrete mathematics, combinatorics, graph theory, and computational algebra. Discrete models play an important role in modeling processes that can be viewed as evolving in discrete time, in which state variables have only finitely many possible states. Discrete models do not rely on detailed information about kinetic rate constants and they tend to be more intuitive. My research has also focused on the development of optimal control methods for probabilistic models using techniques from Markov decision processes.
I am also interested in research related to the computational prediction of RNA secondary structure.
- "Conditioning and Robustness of RNA Boltzmann Sampling under Thermodynamic Parameter Perturbations." Biophysical journal 113, 2 (2017): 321-329. Details. Full text
- "Identification of control targets in Boolean molecular network models via computational algebra." BMC systems biology 10, 1 (2016): 94. Details. Full text
- "Molecular network control through boolean canalization." EURASIP journal on bioinformatics & systems biology 2015, 1 (2015): 9. Details. Full text
- "Stabilizing gene regulatory networks through feedforward loops." Chaos (Woodbury, N.Y.) 23, 2 (2013): 025107. Details. Full text
- "Modeling stochasticity and variability in gene regulatory networks." EURASIP journal on bioinformatics & systems biology 2012, 1 (2012): 5. Details. Full text
- "A mathematical framework for agent based models of complex biological networks." Bulletin of mathematical biology 73, 7 (2011): 1583-602. Details. Full text
- "Regulatory patterns in molecular interaction networks." Journal of theoretical biology 288, (2011): 66-72. Details. Full text
- "Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms." Frontiers in neuroscience 10, (0): 513. Details. Full text