Computational Systems Medicine

Research Group @ Chair of Experimental Bioinformatics @ TUM Our group aims to elucidate the molecular mechanisms behind phenotypes and diseases. To that end, we develop integrative bioinformatics methods leveraging network analysis, machine learning, and statistics. We apply own and existing approaches in close collaboration with biologists and physicians to derive insights from multi-omics data.

News

A preprint of the paper regarding the sPLINK tool (privacy-preserving tool for GWAS) is now available on bioRxiv.

A new Reference Module in Biomedical Science “Multi-Omics Analysis in a Network Context” is now available on Elsevier.

To address the COVID-19 pandemic we developed a drug repurposing tool, which integrates SARS-CoV-2 and SARS-CoV interaction data.

Members

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Judith Bernett

Student Research Assistant

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Rahel Caspar

BSc Student

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Thomas Eska

BSc Student

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Tim Faro

Student Research Assistant

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Amit Fenn

PhD Student

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Gihanna Galindez

PhD Student

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Lena Hackl

MSc Student

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Valentin Hildemann

BSc Student

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Tim Kacprowski

Group Leader

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Manuela Lautizi

PhD Student

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Chit Tong Lio

PhD Student

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Zakaria Louadi

PhD Student

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Thomas Mauermeier

BSc Student

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Amrei Menzel

MSc Student

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Ertida Muka

BSc Student

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Rafaela Relota

BSc Student

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Fanny Rößler

Student Research Assistant

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Sepideh Sadegh

PhD Student

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Jonas Schäfer

Student Research Assistant

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Evelyn Scheibling

BSc Student

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Anton Smirnov

MSc Student

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Olga Tsoy

Post-Doc

Alumni

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Christoph Kloppert

BSc Student

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Olga Lesina

Student Research Assistant

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Chris Li

PREP Student

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Weilong Li

PhD Student

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Nils Mehrtens

BSc Student

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Pauline Nickel

BSc Student

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Niklas Probul

BSc Student

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Andreas Stelzer

BSc Student

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Lorenzo Viola

MSc Student

Projects

Sys_CARE

Systems Medicine Investigation of Alternative Splicing in Cardiac and Renal Diseases.

REPO-TRIAL

The EU H2020 project REPO-TRIAL aims at developing an in silico approach to optimise the efficacy and precision of drug repurposing trials. To this end we integrate heterogeneous data into a comprehensive interactome of disease-drug-gene interactions (a new diseasome) and develop graph-based machine learning approaches to investigate this highly complex data.

FeatureCloud

The EU H2020 project FeatureCloud aims at developing methods for privacy-preserving, federated machine learning.

Network-Based Epistasis Detection

We tackle the challenge of higher-order epistasis detection using biological networks to narrow the search space and GPU computing to improve the efficiency. Phenotype-specific epistasis-modules extracted from larger networks will help to better understand the underlying biological mechanisms of different phenotypes.

De novo network enrichment

We develop tools that leverage information from molecular interaction networks in understanding molecular profiling data. De novo network enrichment tools extract subnetworks that mechanistically explain a phenotype of interest, e.g. a disease.

Software & Resources

CoVex

To address the pandemic of the Coronavirus Disease-2019 (COVID-19), drug repurposing can be a helpful approach since it offers the possibility to find alternative fields of application for already approved drugs. CoVex is the first network and systems medicine online data analysis platform that integrates virus-human interaction data for SARS-CoV-2 and SARS-CoV. It is available as interactive webtool. More information and current updates can be found at the CoVex blog at the Chair of Experimental Bioinformatics website.

Scellnetor

Scellnetor is a novel scRNA-seq clustering tool. It allows the analysis of pseudo time-courses in single-cell sequencing data via a network-constrained clustering algorithm. Scellnetor is available as interactive online application at the Scellnetor website.

EpiGEN

EpiGEN is a Python pipeline for simulating epistasis data. It supports epistasis models of arbitrary size, which can be specified either extensionally or via parametrized risk models. Moreover, the user can specify the minor allele frequencies (MAFs) of both noise and disease SNPs, and provide a bias target distribution for the generated phenotypes to simulate observation bias. EpiGEN is freely available as python 3 package on GitHub.

Fastlogranktest

Fastlogranktest is a software package providing wicked-fast implementations of the logrank test in C++, R, and Python.

BiCoN

BiCoN is a powerful new systems medicine tool to stratify patients while elucidating the responsible disease mechanisms. BiCoN is a network-constrained biclustering approach which restricts biclusters to functionally related genes connected in molecular interaction networks and maximizes the expression difference between two subgroups of patients. A package for network-constrained biclustering of patients and multi-omics data can also be used. Download and installation instructions can be found here.

Recent Publications

Genome-wide association studies (GWAS) have been widely used to unravel connections between genetic variants and diseases. Larger …

Multi-omics data analysis holds great potential for treatment optimization, molecular diagnostics and disease prognosis. To gain …