Grants allocated to our research at the Helmholtz Zentrum München.
Further projects are listed on the pages of the Data Science Group at Bielefeld University.

Uncertainty Quantification - From Data to Reliable Knowledge

Helmholtz Association, Pilot Project in Information & Data Science

Consortium coordinators: Martin Frank, Christiane Fuchs

Funding period: 2019-2023

Uncertainty is ubiquitous in models and data. From stochastic modelling, where fluctuations play a central role in the dynamics of the process, to data collection, where measurement and sampling error permeate the data, it is central to understand the effects of uncertainty. The UQ project is centered on placing these elements in focus. The consortium spans 10 institutions with domain researchers, statistical and mathematical methods researchers, and research software engineers who care about quantification of uncertainty.

More information: Project webpage 

An Integrated Systems Approach for Incompletely Penetrant Onco-Phenotypes

National Institutes of Health

Principal investigator: Kevin Janes

Co-Investigators: Christiane Fuchs, Fabian Theis, Kristen Atkins, Jennifer Harvey

Funding period: 2017-2022

HER2+ breast cancer is an aggressive subtype of breast cancer with targeted drugs available, but not all of these patients respond to HER2-directed therapies. Our project will develop an experimental approach to determine why cancer cells do not respond uniformly when their molecular pathways are perturbed by stimuli or drugs. The results from this work could one day lead to combination therapies that increase the percentage of cancer patients who respond to anti-cancer agents.

More information: Project webpage

Stochastic and Statistical Modeling of Biomarkers in Breast Cancer Disease Progression

Federal Ministry of Education and Research

Principal investigators: Christiane Fuchs, Hamid El Maroufy

Funding period: 2017-2022

Breast cancer is one of the most common cancers in women worldwide. An effective assessment of multiple variants of therapies and treatments requires information about the progression of the disease from one stage to another. The exact diesease stage is often unobserved due to the invasive nature of measurements. One hence aims to well understand the relationship between tumor markers and disease progression. In this project, we develop and apply stochastic Hidden Markov Models and according inference techniques to characterize breast cancer disease progression with the help of biomarkers. The strategic goal of this collaborative project is to improve the efficacy of treatments for women with breast cancer and to reduce their costs.

Computational Models of Neoplasmic Heterogeneities and Lineage Choice

Collaborative Research Centre 1243 "Genetic and Epigenetic Evolution of Hematopoietic Neoplasms", Subproject A17

Principal investigators of the subproject: Christiane Fuchs, Fabian Theis

Funding period: 2016-2019

Acute myeloid leukemia (AML) often results from the myelodysplastic syndrome (MDS). Here, the differentiation hierarchy from hematopoietic stem cells to mature, functional cells is disturbed. AML patients, again, frequently carry a mixture of different cancer cell types, so-called subclones. This is reflected by a mixture of genomic signatures and heterogeneous transcriptome profiles. Applying statistical and dynamical models to data from our clinical and biological collaborators in this CRC, we want to identify altered differentiation hierarchies of MDS subclones and characterize the development of related heterogeneities in AML while the tumor undergoes evolution.

More information: Webpage of the CRC 1243

Making Inference of Biological Processes more Reliable using Stochastic Models

Postdoc Fellowship Programme of the Helmholtz Zentrum München

Grant holder: Christiane Fuchs

Funding period: 2014-2017

The molecular biology of life seems inaccessibly complex. It is subject to random variation and not exactly predictable. Still, mathematical models and statistical inference pave the way towards the understanding of biological mechanisms. In contrast to deterministic models, stochastic processes capture the randomness of natural phenomena and result in more reliable predictions of cellular dynamics. In this project, we develop and apply stochastic models and statistical inference techniques to dynamic biological processes.

More information: Webpage of the Postdoctoral Fellowship Program