David Murrugarra

This study investigates how auxiliary information improves RNA secondary structure prediction accuracy by analyzing suboptimal structures and using topological data analysis features.

This study links modular restructuring in pancreatic cancer signaling networks to treatment efficacy by showing how mutation locations affect disease aggression and control.

The paper explores the relationship between structural and functional modularity in gene regulatory networks using Boolean models and simulations, showing that structural decomposition correlates with dynamic behavior.

This paper reviews techniques for controlling Boolean gene regulatory networks, comparing methods like algebraic approaches and feedback vertex sets using a T-LGL leukemia model.

The paper identifies phenotype-relevant genes by analyzing dose response data without using models, finding genes with spikes in transcriptional sensitivity that likely regulate phenotypic transitions.

The paper analyzes regulatory nonlinearity in biological networks using Boolean models and finds that biological regulation is generally less nonlinear than expected, with cancer networks showing higher and more variable nonlinearity.

The paper analyzes how changes in model parameters affect optimal control policies, time to absorption, and mixing times in Boolean models of biological systems.

This paper uses a Boolean model to study the impact of mutations in pancreatic cancer and identifies potential drug targets based on simulation and clinical data.

The paper introduces a method to build model prototypes from time-course data using discrete functions and wiring diagrams, applied to gene expression data from salamander tail regeneration.

The paper builds a stochastic Boolean network model of pancreatic cancer microenvironment to identify control targets that can shift the gene regulatory network from a diseased to a healthy state.

This study builds a Boolean model of the C. albicans yeast-to-hyphal transition, identifies key control strategies, and validates them experimentally.

This paper introduces a control method for multistate biological networks using algebraic techniques and applies it to cancer and iron metabolism models to identify intervention strategies.