Réka Albert

The paper introduces SD Markov chains to analyze Boolean network dynamics by reducing state space using trap spaces, accurately capturing attractors and their properties.

This paper introduces a genetic algorithm-based workflow for refining biological network models using perturbation-observation pairs, demonstrating its effectiveness in a plant signaling model.

The paper analyzes experimentally-supported biomolecular networks to show they are ordered and far from the edge of chaos, with new measures and simulations revealing greater rigidity than previously thought.

The paper builds a Boolean dynamic model of the CO2-induced stomatal closure network in Arabidopsis, revealing a feedback core that dictates cellular decisions and validating predictions through experiments.

This paper develops an ODE-based GRN inference method using dynamic transcriptional data and validates it on synthetic yeast and Candida albicans GRNs.

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

The paper analyzes a mammalian cell cycle model to show how conditionally stable motifs drive oscillations through cell cycle checkpoints.

The paper identifies four SCLC subtypes using a systems approach, with NEv2 being a new subtype, and finds master regulators and destabilizers using BooleaBayes.

The paper introduces a method to identify control-robust subsystems in dynamical systems, demonstrating its application to biological networks like Drosophila and T-cell signaling.

The paper introduces a method for target control in logical biomolecular network models using domain of influence concepts and greedy randomized adaptive search.