D
David Murrugarra
Researcher at University of Kentucky
Publications - 54
Citations - 757
David Murrugarra is an academic researcher from University of Kentucky. The author has contributed to research in topics: Boolean network & Computer science. The author has an hindex of 13, co-authored 43 publications receiving 570 citations. Previous affiliations of David Murrugarra include Georgia Institute of Technology & Virginia Bioinformatics Institute.
Papers
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Journal ArticleDOI
Modeling stochasticity and variability in gene regulatory networks.
David Murrugarra,David Murrugarra,Alan Veliz-Cuba,Boris Aguilar,Seda Arat,Seda Arat,Reinhard Laubenbacher,Reinhard Laubenbacher +7 more
TL;DR: In this article, the authors propose an approach to model stochasticity in gene regulatory networks within the discrete paradigm, where genes, proteins, and other molecular components of gene regulatory network are modeled as discrete variables and are assigned as logical rules describing their regulation through interactions with other components.
Journal ArticleDOI
A mathematical framework for agent based models of complex biological networks.
Franziska Hinkelmann,David Murrugarra,Abdul Salam Jarrah,Abdul Salam Jarrah,Reinhard Laubenbacher +4 more
TL;DR: In this paper, the authors propose an extension to the ODD protocol which allows the description of an agent-based model as a dynamical system, which provides access to computational and theoretical tools for its analysis.
Journal ArticleDOI
Modeling Stochasticity and Variability in Gene Regulatory Networks
David Murrugarra,David Murrugarra,Alan Veliz-Cuba,Boris Aguilar,Seda Arat,Seda Arat,Reinhard Laubenbacher,Reinhard Laubenbacher +7 more
TL;DR: This article contributes an approach as an alternative to classical settings that allows a finer analysis of discrete models and provides a natural setup for cell population simulations to study cell-to-cell variability.
Journal ArticleDOI
Boolean nested canalizing functions: a comprehensive analysis
Yuan Li,John O. Adeyeye,David Murrugarra,Boris Aguilar,Reinhard Laubenbacher,Reinhard Laubenbacher +5 more
TL;DR: A detailed analysis of nested canalizing functions based on a novel normal form as polynomial functions over the Boolean field, and experimental evidence that the layer number is an important factor in network stability is contained.
Journal ArticleDOI
Regulatory patterns in molecular interaction networks.
TL;DR: Within the context of the multistate discrete model paradigm, a rule type is introduced that reduces to the concept of nested canalyzing function in the Boolean network case and is shown that networks that employ this type of multivalued logic exhibit more robust dynamics than random networks, with few attractors and short limit cycles.