Heinig Lab

abstract technology ui futuristic concept human digital DNA health care of hud interface hologram elements of digital data chart, communication,innovation on hi tech future design background

The Heinig Lab is located at the Institute of Computational Biology, which is part of the Computational Health Center of Helmholtz Munich. Together with my research group, I develop AI solutions for personalized network-based precision medicine.

Technological advances allow for an unprecedented in-depth characterization of the molecular basis of complex diseases. In particular, SNP genotyping, DNA methylation assays, and gene expression profiling in large cohorts and in single cells have been used to identify numerous disease-associated loci and genes. However, a deeper mechanistic or systems-level understanding of disease processes still remains elusive in most cases.

The aim of our research is the development and application of computational and statistical tools for the identification of molecular regulatory networks underlying common diseases and the genetic and epigenetic mechanisms controlling these networks from population-level DNA and multi-omics data sets. In a second step, we aim to personalize the networks based on single-cell data. This will enable us to implement new concepts for precision medicine. A special focus is the molecular characterization of cardiovascular and metabolic diseases.

Complex trait genetics explores how multiple genes and environmental factors shape diverse biological traits and diseases.

Learn more

Double helix DNA molecule with modified genes , Correcting mutation by genetic engineering

Deciphering cardiovascular disease mechanisms by combining single-cell genomics, transcriptomics, and proteomics in multi-omics approaches.

Learn more

Our research group is dedicated to unraveling the intricate relationships between genetic variation, gene regulation, and complex diseases. We aim to bridge the gap between genome-wide association studies (GWAS) and underlying biological mechanisms through innovative approaches in complex trait genetics. We integrate multi-omics data using advanced computational techniques, including network algorithms, Bayesian modeling, and machine learning. Our work focuses on identifying regulatory networks and core genes implicated in complex traits, providing a systems-level view of genetic influences on biological processes. By examining the interplay between genes and their products, we identify key pathways and potential intervention points in disease processes. Our research contributes to the development of more accurate predictive models for disease risk and progression, driving progress towards personalized medicine. Ultimately, we strive to improve risk prediction, enable earlier interventions, and develop more effective treatments tailored to individual genetic profiles, transforming our understanding of complex diseases and individual variability in health outcomes.

medical illustration - transparent human heart

Our research group pioneers the application of multi-omics approaches to revolutionize cardiovascular disease understanding. We integrate diverse molecular data types, from genomics to proteomics, to examine heart biology and pathology at unprecedented resolution. By combining large-scale genomic studies with cutting-edge single-cell analyses, we bridge the gap between genetic variation and disease manifestation at the cellular level. We develop innovative computational methods to analyze these complex datasets, including the identification of expression quantitative trait loci for transcripts (eQTL) and proteins (pQTL) in human heart tissue and the creation of detailed single-cell atlases in health and disease. Our integrative approach unveils novel insights into the molecular mechanisms underlying cardiovascular diseases, contributing to the AI driven identification of new biomarkers and potential therapeutic targets. Through active collaborations with international consortia such as the CZI seed network for the human heart, we aim to transform cardiovascular health understanding and pave the way for precision medicine. Our ultimate goal is to translate molecular insights into improved prediction of disease risk and patient outcomes, bringing us closer to personalized cardiovascular treatments.

Max-Malte Hansen

Bachelor Student

Max is currently completing his Bachelor’s degree in Bioinformatics in Munich. He is working on his bachelor’s thesis on biomarker discovery for atherosclerosis using data from the UK Biobank, focusing on ultrasonic imaging, proteomics, and electronic health records, and applying deep learning–based approaches to identify predictive molecular signatures.

Email • LinkedIn

Orhan Bellur

PhD Student

Orhan studied Molecular Biology and Genetics and has a broad interest in investigating metabolic diseases, particularly neurodegenerative diseases, using a systems biology approach. Currently, he is pursuing his PhD, focusing on understanding the molecular mechanisms of Alzheimer's disease and identifying candidate drugs by employing several in silico drug repurposing algorithms.

Email • Linkedin

Rupshali Dasgupta

PhD Student

Rupshali studied Computer Science and Engineering with a focus on artificial intelligence, and completed her master’s thesis on privacy-preserving machine learning. She is now working on developing AI-driven knowledge graph and large language model–based frameworks for cardiometabolic diseases, with the aim of integrating heterogeneous biomedical evidence and enabling structured, interpretable reasoning over clinical data.

Email • LinkedIn

Gruppenfoto ICB Heinig Lab 7924 — Gruppenfoto Heinig Lab

Annie Westerlund
Toray Akcan
Ines Assum
Johann Hawe
Katharina Schmid
Barbara Höllbacher
Kartheeswaran Thangathurai
Cem Gülec
Simon Wengert
Andreas Schmidt
Sergey Vilov
Corinna Losert
Florin Ratajczak
Ziming Huang

Korbinian Traeuble, Matthias Munz, Jessica Pauli, Nadja Sachs, Eshan Vafadarnejad, Tania Carrillo-Roa, Lars Maegdefessel, Peter Kastner & Matthias Heinig

Integrated single-cell atlas of human atherosclerotic plaques

Florin Ratajczak, Matthias Heinig, Pascal Falter-Braun

Exploring the omnigenic architecture of selected complex traits

Talyn Chu, Ming Wu, Barbara Hoellbacher, Gustavo P. de Almeida, Christine Wurmser, Jacqueline Berner, Lara V. Donhauser, Ann-Katrin Gerullis, Siran Lin, J. Diego Cepeda-Mayorga, Iman I. Kilb, Lukas Bongers, Fabio Toppeta, Philipp Strobl, Ben Youngblood, Anna M. Schulz, Alfred Zippelius, Percy A. Knolle, Matthias Heinig, Carl-Philipp Hackstein & Dietmar Zehn

Precursors of exhausted T cells are pre-emptively formed in acute infection

Florian Gaertner, Hellen Ishikawa-Ankerhold, Susanne Stutte, Wenwen Fu, Jutta Weitz, Anne Dueck, Bhavishya Nelakuditi, Valeria Fumagalli, Dominic van den Heuvel, Larissa Belz, Gulnoza Sobirova, Zhe Zhang, Anna Titova, Alejandro Martinez Navarro, Kami Pekayvaz, Michael Lorenz, Louisa von Baumgarten, Jan Kranich, Tobias Straub, Bastian Popper, Vanessa Zheden, Walter Anton Kaufmann, Chenglong Guo, Guido Piontek, Saskia von Stillfried, Peter Boor, Marco Colonna, Sebastian Clauß, Christian Schulz, Thomas Brocker, Barbara Walzog, Christoph Scheiermann, William C. Aird, Claus Nerlov, Konstantin Stark, Tobias Petzold, Stefan Engelhardt, Michael Sixt, Robert Hauschild, Martina Rudelius, Robert A. J. Oostendorp, Matteo Iannacone, Matthias Heinig & Steffen Massberg

Plasmacytoid dendritic cells control homeostasis of megakaryopoiesis

Kami Pekayvaz*, Corinna Losert*, Viktoria Knottenberg*, Christoph Gold, Irene V. van Blokland, Roy Oelen, Hilde E. Groot, Jan Walter Benjamins, Sophia Brambs, Rainer Kaiser, Adrian Gottschlich, Gordon Victor Hoffmann, Luke Eivers, Alejandro Martinez-Navarro, Nils Bruns, Susanne Stiller, Sezer Akgöl, Keyang Yue, Vivien Polewka, Raphael Escaig, Markus Joppich, Aleksandar Janjic, Oliver Popp, Sebastian Kobold, Tobias Petzold, Ralf Zimmer, Wolfgang Enard, Kathrin Saar, Philipp Mertins, Norbert Huebner, Pim van der Harst, Lude H. Franke, Monique G. P. van der Wijst, Steffen Massberg, Matthias Heinig†, Leo Nicolai†, Konstantin Stark†

Multiomic analyses uncover immunological signatures in acute and chronic coronary syndromes

Florin Ratajczak, Mitchell Joblin, Marcel Hildebrandt, Martin Ringsquandl, Pascal Falter-Braun, Matthias Heinig

Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases

Hawe JS*, Wilson R*, Schmid KT*, Zhou L, Lakshmanan LN, Lehne BC, Kühnel B, Scott WR, Wielscher M, Yew YW, Baumbach C, Lee DP, Marouli E, Bernard M, Pfeiffer L, Matías-García PR, Autio MI, Bourgeois S, Herder C, Karhunen V, Meitinger T, Prokisch H, Rathmann W, Roden M, Sebert S, Shin J, Strauch K, Zhang W, Tan WLW, Hauck SM, Merl-Pham J, Grallert H, Barbosa EGV; MuTHER Consortium, Illig T, Peters A, Paus T, Pausova Z, Deloukas P, Foo RSY, Jarvelin MR, Kooner JS, Loh M†, Heinig M†, Gieger C†, Waldenberger M†, Chambers JC†.

Genetic variation influencing DNA methylation provides insights into molecular mechanisms regulating genomic function

Assum I*, Krause J*, Scheinhardt MO, Müller C, Hammer E, Börschel CS, Völker U, Conradi L, Geelhoed B, Zeller T, Schnabel RB†, Heinig M†

Tissue-specific multi-omics analysis of atrial fibrillation

Schmid KT, Höllbacher B, Cruceanu C, Böttcher A, Lickert H, Binder EB, Theis FJ, Heinig M

scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies