Claus Kadelka

Gene regulatory networks (GRNs) describe how a collection of genes governs the molecular processes within a cell. Understanding how GRNs perform particular functions and do so consistently in the face of ubiquitous variability constitutes a fundamental biological question. A clear, theory-based understanding of the mechanistic principles underlying the structure of GRNs and how the specific structure helps the regulatory networks in our cells to maintain a stable phenotype is still lacking. My research addresses these questions in the context of discrete dynamical systems, e.g. Boolean and multistate network models, which have become a popular modeling framework in the last few decades. By combining data science with rigorous theoretical and computational analysis, I seek to identify the mechanisms which confer robustness to gene regulatory networks, by relating network topology to the network dynamics. I focus particularly on the biologically motivated concept of canalization, which refers to the process of creating stability in a gene regulation program in the face of variability.

The COVID-19 pandemic has revealed the power of and need for epidemiological models. However, many classical models are too simplistic. I investigate how to generate more accurate infectious disease models that incorporate human behavior (e.g., rates of mask wearing or vaccine uptake that depend on age, education, etc.) and social processes (e.g., homophily in social interaction patterns with respect to age, ethnicity, etc.). 

Moreover, I have an interest in biomedical data science. I work with virologists and clinicians from the Swiss HIV Cohort Study (SHCS), aiming to understand why HIV broadly neutralizing antibodies, which constitute a major hope for an HIV vaccine and therapy development, are only elicited at low frequency in natural HIV infection.