Claus Kadelka

The paper analyzes attractor coherence in Boolean gene regulatory networks, revealing that attractors representing mature cell types are less stable than their basins due to canalization.

CLEAR improves gene set enrichment analysis by jointly modeling gene sets with continuous statistics, reducing redundancy and improving sensitivity.

The paper introduces a coherence matrix to analyze directional regulatory coordination in gene regulatory networks, showing how hierarchical structures mitigate scaling penalties in sparse networks.

This study develops a mathematical model to determine optimal fungicide application rates for soybean crops to maximize profits while managing Sclerotinia stem rot.

This paper develops a deterministic model to study how competition and phenotypic plasticity affect adaptive therapy outcomes in cancer, showing that phenotypic transitions can complicate therapy and that asymmetric competition benefits treatment.

This study evaluates two threshold formalisms for Boolean gene regulatory networks, finding that while Ising rules match fewer functions exactly, they provide better average agreement with biological dynamics.

The paper analyzes function annotation biases in the human proteome over time, revealing persistent inequality in knowledge distribution and a gap between annotation and scientific interest.

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.

CRFE improves functional enrichment by using gene rankings to identify non-redundant, specific categories, outperforming existing methods in four RNA-seq datasets.

This paper introduces mathematical tools to analyze robustness in Boolean networks using k-canalizing functions and provides formulas for c-sensitivity and activities.