David Murrugarra
Research Interests:
- Systems Biology
- Bioinformatics
- Mathematical Biology
Research
I develop computational tools for modeling, analysis, and control of signal transduction and gene regulatory networks. I have focused on discrete methods, which employ techniques from discrete mathematics, combinatorics, graph theory, and computational algebra. I am also developing efficient methods for optimal control of large probabilistic models using techniques from Markov decision processes and reinforcement learning.
I am also interested in the computational prediction of RNA secondary structure using machine learning techniques.
I am also interested on using deep learning techniques for classification problems in Systems Biology and Bioinformatics.
PubMed Publications*:
- "Control of Intracellular Molecular Networks Using Algebraic Methods." Bulletin of mathematical biology 82, 1 (2019): 2. Details. Full text
- "Transcriptional correlates of proximal-distal identify and regeneration timing in axolotl limbs." Comparative biochemistry and physiology. Toxicology & pharmacology : CBP 208, (2018): 53-63. Details. Full text
- "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
- "Regulatory patterns in molecular interaction networks." Journal of theoretical biology 288, (2011): 66-72. 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
- "Estimating Propensity Parameters Using Google PageRank and Genetic Algorithms." Frontiers in neuroscience 10, (0): 513. Details. Full text
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