Kyu Hyong Park

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.

This paper introduces biobalm, a tool for constructing succession diagrams of Boolean networks, showing improved performance and demonstrating that non-random models have complex attractor landscapes.

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.