Computational Biology

Research Areas

Our research field encompasses a variety of topics including, but not limited to, statistical inference for dynamical systems, multi-scale models, metabolic models, statistical analysis, and scientific machine learning. Thus, our group is split into smaller teams dedicated to one overarching research topic each. Generally, we aim to provide insights into biological processes and make predictions through analyses, while also considering the complexities of the underlying mechanisms.

This page provides an overview of the different teams and research areas in the Hasenauer Group.

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.

Scientific Machine Learning

Our team aims to efficiently combine the advantages of interpretable mechanistic models with data-driven machine learning methods. Mechanistic models like differential equations benefit from decades of research in modeling complex biological processes from first principles. These models achieve interpretability and uncertainty analysis, which are crucial tools for hypothesis testing and validating biological assumptions. In contrast, data-driven approaches from machine learning, particularly neural networks, extract information unbiasedly and require minimal knowledge of scientific laws. This team aims to combine both approaches to construct efficient, data-driven, and interpretable modeling strategies for various biological problems.

Statistical Analysis

We are restructuring our group and will update the following text soon.

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.

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.

Mathematical-Epidemiological Modeling

The recent Covid-19 pandemic has shown the urgent need for profound understanding of infectious disease dynamics to adequately react. In order to find sound intervention strategies, mathematical models have demonstrated to be pivotal. The team on mathematical-epidemiological modeling does research on a large variety mathematical models. While methods on ordinary differential equations and metapopulations use a lot of simplifications, they allow for real-time answers without introducing a large computational overhead. Integro-differential equation-based models, come with additional difficulties but also allow for a more realistic modeling of infectious disease transmission. Agent-based models again increase the computational overhead but also enable fine-scale insights in, e.g., household transmission, a key factor in disease dynamics. On top of these classic models for infectious disease dynamics, we do research on hybrid mechanistic and machine or deep learning based models such as graph neural networks.

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