99 citations · 492 across the 22 of their papers we have counts for
22 papers
Dynamics Decomposition of Boolean Networks: An algebraic foundation
Alan Veliz-Cuba, Claus Kadelka, David Murrugarra +4
Understanding the dynamics of Boolean networks is central to problems such as network reduction, design, control, and reverse engineering. As Boolean network models continue to gro…
Personalizing Cancer Models under Data Scarcity via Parameter Decomposition
Logan Rose, Jonathan Martinez, Juho Kim +3
Personalized cancer modeling for clinical applications requires robust and efficient parameter calibration, particularly in settings with limited patient data. This need is especia…
Canalization reduces the nonlinearity of regulation in biological networks
Claus Kadelka, David Murrugarra
Biological networks such as gene regulatory networks possess desirable properties. They are more robust and controllable than random networks. This motivates the search for structu…
Modular Construction of Boolean Networks
Matthew Wheeler, Claus Kadelka, Alan Veliz-Cuba +2
Boolean networks have been used in a variety of settings, as models for general complex systems as well as models of specific systems in diverse fields, such as biology, engineerin…
Modular control of Boolean network models
David Murrugarra, Alan Veliz-Cuba, Elena Dimitrova +3
The concept of control is crucial for effectively understanding and applying biological network models. Key structural features relate to control functions through gene regulation,…
Stability of Linear Boolean Networks
Karthik Chandrasekhar, Claus Kadelka, Reinhard Laubenbacher +1
Stability is an important characteristic of network models that has implications for other desirable aspects such as controllability. The stability of a Boolean network depends on…
Decomposition of Boolean networks: An approach to modularity of biological systems
Claus Kadelka, Reinhard Laubenbacher, David Murrugarra +2
This paper presents the foundation for a decomposition theory for Boolean networks, a type of discrete dynamical system that has found a wide range of applications in the life scie…
Revealing the canalizing structure of Boolean functions: Algorithms and applications
Elena Dimitrova, Brandilyn Stigler, Claus Kadelka +1
Boolean functions can be represented in many ways including logical forms, truth tables, and polynomials. Additionally, Boolean functions have different canonical representations s…
Improving RNA secondary structure prediction via state inference with deep recurrent neural networks
Devin Willmott, David Murrugarra, Qiang Ye
The problem of determining which nucleotides of an RNA sequence are paired or unpaired in the secondary structure of an RNA, which we call RNA state inference, can be studied by di…
Quantifying the Total Effect of Edge Interventions in Discrete Multistate Networks
David Murrugarra, Elena Dimitrova
Developing efficient computational methods to assess the impact of external interventions on the dynamics of a network model is an important problem in systems biology. This paper…
Estimating Propensity Parameters using Google PageRank and Genetic Algorithms
David Murrugarra, Jacob Miller, Alex Mueller
Stochastic Boolean networks, or more generally, stochastic discrete networks, are an important class of computational models for molecular interaction networks. The stochasticity s…
Identification of control targets in Boolean molecular network models via computational algebra
David Murrugarra, Alan Veliz-Cuba, Boris Aguilar +1
Motivation: Many problems in biomedicine and other areas of the life sciences can be characterized as control problems, with the goal of finding strategies to change a disease or o…
Molecular Network Control Through Boolean Canalization
David Murrugarra, Elena S. Dimitrova
Boolean networks are an important class of computational models for molecular interaction networks. Boolean canalization, a type of hierarchical clustering of the inputs of a Boole…
Stabilizing Gene Regulatory Networks Through Feedforward Loops
Claus Kadelka, David Murrugarra, Reinhard Laubenbacher
The global dynamics of gene regulatory networks are known to show robustness to perturbations in the form of intrinsic and extrinsic noise, as well as mutations of individual genes…
Boolean nested canalizing functions: a comprehensive analysis
Yuan Li, John O. Adeyeye, David Murrugarra +2
Boolean network models of molecular regulatory networks have been used successfully in computational systems biology. The Boolean functions that appear in published models tend to…
Modeling Stochasticity and Variability in Gene Regulatory Networks
David Murrugarra, Alan Veliz-Cuba, Boris Aguilar +2
Modeling stochasticity in gene regulatory networks is an important and complex problem in molecular systems biology. To elucidate intrinsic noise, several modeling strategies such…
Multi-state Canalyzing Functions over Finite Fields
Yuan Li, David Murrugarra, John O. Adeyeye +1
In this paper, we extend the definition of Boolean canalyzing functions to the canalyzing functions over finite field , where is a power of a prime. We obtain t…
The Number of Multistate Nested Canalyzing Functions
David Murrugarra, Reinhard Laubenbacher
Identifying features of molecular regulatory networks is an important problem in systems biology. It has been shown that the combinatorial logic of such networks can be captured in…
Bifurcations in Boolean Networks
Chris J. Kuhlman, Henning S. Mortveit, David Murrugarra +1
This paper characterizes the attractor structure of synchronous and asynchronous Boolean networks induced by bi-threshold functions. Bi-threshold functions are generalizations of c…
Structure and Dynamics of Polynomial Dynamical Systems
Reinhard Laubenbacher, David Murrugarra, Alan Veliz-Cuba
Discrete models have a long tradition in engineering, including finite state machines, Boolean networks, Petri nets, and agent-based models. Of particular importance is the questio…
Regulatory patterns in molecular interaction networks
David Murrugarra, Reinhard Laubenbacher
Understanding design principles of molecular interaction networks is an important goal of molecular systems biology. Some insights have been gained into features of their network t…
A Mathematical Framework for Agent Based Models of Complex Biological Networks
Franziska Hinkelmann, David Murrugarra, Abdul Salam Jarrah +1
Agent-based modeling and simulation is a useful method to study biological phenomena in a wide range of fields, from molecular biology to ecology. Since there is currently no agree…