Computational Biology

Research Areas

Our research field encompasses a variety of topics including statistical inference, multi-scale models, metabolic models, mathematical modeling for infectious diseases, and machine learning in bioinformatics. In our research, we aim to provide insights into biological processes and make predictions through analysis, while also considering the complexities of the underlying mechanisms.

Bioinformatics and Machine Learning

Bioinformatics benefits from machine learning and its transfer to various fields, such as proteomics, genomics, and systems biology. The huge amounts of data generated by high-throughput measurement techniques enable data-driven modelling. Our subgroup applies machine learning, such as neural networks, deep learning, and clustering, combined with classical statistical concepts to solve problems in Bioinformatics and Systems Biology. A focus of our group is the establishment of efficient and accessible analysis tools to promote improvements in modelling, and thus to be able to shed light on the underpinnings of biological processes.

Infectious Diseases

The COVID-19 pandemic has dramatically brought to attention the importance of improving our preparedness against the appearance of novel infectious diseases. Mathematical modeling can play a vital role in this effort,bringing to light the fundamental mechanisms underlying the data gathered by clinicians. The two main directions of this subgroup are: epidemiological modeling to understand the spread of the disease and evaluate government actions, and federated analysis of clinical data to combine multiple studies while preserving privacy to assess the evolution of antibodies in transplant patients or risk factors for long COVID.

Metabolic Modeling

Metabolic dysfunctions are the driver of many diseases. Metabolism is governed by intricate regulatory mechanisms, and therefore, intuitively understanding dynamical metabolic responses to perturbations is hardly possible. Therefore, we combine dynamical models of metabolic processes with various types of data to get a mechanistic and quantitative understanding of metabolism in health and disease.

Multi-Scale Modeling

Biology is complex, with processes interacting on various spatial and temporal scales, requiring complex mechanistic descriptions and efficient, accurate inference approaches. In this subgroup, we develop such approaches that scale up to complex problems on multiple fronts, combining methods from classical statistical inference and machine learning. Specifically, we work on likelihood-free inference, amortized neural density estimation, surrogate modeling, non-linear mixed effects modeling, multi-cellular modeling, and differential equations. We develop novel methods, and software tools that make these broadly accessible.

Statistical Inference

Statistical inference plays a crucial role in the application of mathematical modeling to real-world problems, by providing a framework that facilitates identifiability analysis, uncertainty quantification, comparison of models, and more. In this subgroup, we generally work on applications that arise from collaborations with biological and medical researchers, from both academia and industry. We develop models to describe the real-world processes, and we develop methods to do this better.

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