Alan VelizCubaSummaryAffiliation: University of Nebraska Country: USA Publications
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Publications
 On the relationship of steady states of continuous and discrete models arising from biologyAlan VelizCuba
University of Nebraska Lincoln, Lincoln, NE 68505, USA
Bull Math Biol 74:277992. 2012..Our results also provide a novel method to analyze certain classes of nonlinear models using discrete mathematics...  Reduction of Boolean network modelsAlan VelizCuba
Department of Mathematics, University of Nebraska Lincoln, USA
J Theor Biol 289:16772. 2011..In particular, we use the reduction method to study steady states of Boolean networks and apply our results to models of Thlymphocyte differentiation and the lac operon...  Identification of control targets in Boolean molecular network models via computational algebraDavid Murrugarra
Department of Mathematics, University of Kentucky, Lexington, 40506 0027, KY, USA
BMC Syst Biol 10:94. 2016..The mathematical model type considered is that of Boolean networks. The potential control targets can be represented by a set of nodes and edges that can be manipulated to produce a desired effect on the system...  Steady state analysis of Boolean molecular network models via model reduction and computational algebraAlan VelizCuba
Department of Mathematics, University of Houston, 651 PGH Building, Houston TX, USA
BMC Bioinformatics 15:221. 2014..While these methods represent a substantial improvement in scalability over exhaustive enumeration, the problem for large networks is still unsolved in general...  Boolean models can explain bistability in the lac operonAlan VelizCuba
Department of Mathematics, University of Nebraska Lincoln, Lincoln, Nebraska, USA
J Comput Biol 18:78394. 2011..This work suggests that the key to model qualitative dynamics of gene systems is the topology of the network and Boolean models are well suited for this purpose...  The neural ring: an algebraic tool for analyzing the intrinsic structure of neural codesCarina Curto
Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE, USA
Bull Math Biol 75:1571611. 2013..This allows us to algorithmically extract the canonical form associated to any neural code, providing the groundwork for inferring stimulus space features from neural activity alone...  Modeling stochasticity and variability in gene regulatory networksDavid Murrugarra
Department of Mathematics, Virginia Tech, Blacksburg, VA 24061 0123, USA
EURASIP J Bioinform Syst Biol 2012:5. 2012..We applied our methods to two of the most studied regulatory networks, the outcome of lambda phage infection of bacteria and the p53mdm2 complex...  Polynomial algebra of discrete models in systems biologyAlan VelizCuba
Virginia Bioinformatics Institute, Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
Bioinformatics 26:163743. 2010..There is increasing evidence that such models can capture key dynamic features of biological networks and can be used successfully for hypothesis generation...