Phone: +49 421 200 3238
Mail: mhuett (at) constructor.university
Position: Professor

CV

Marc Hütt studied physics in Göttingen and Paris and received his PhD in Göttingen in 1997. Following longer research stays in Novosibirsk, Paris, Warsaw and Darmstadt, he became an Assistant Professor of Theoretical Biology and Bioinformatics in 2001 at Darmstadt University of Technology. In 2006 he moved to Jacobs University in Bremen, accepting a Professorship in Computational Systems Biology.

From 2000 to 2005 he was a member of “Die Junge Akademie” (an institution founded by Berlin-Brandenburgische Akademie der Wissenschaften and Deutschen Akademie der Naturforscher Leopoldina). Since 2019 he is a member of the European Academy of Sciences and Arts.

Research

Among his research interests is the development of mathematical tools for analyzing biological pattern formation, the analysis and modeling of large-scale statistical properties of genomes, as well as studying the link between topology and dynamics in biological networks.

He uses methods from nonlinear dynamics, the theory of complex networks and information theory, in order to analyze biological systems.

In particular, he has developed and applied network-based data analysis methods to metabolomics, proteomics and transcriptomics data.

Recent Publications

                    @article{10.1093/pnasnexus/pgaf127,
 abstract = {Due to the ever-increasing wirelessly transmitted data, the development of new transmission standards and higher frequencies in the 5G band is required. Despite basic biophysical considerations that argue against health effects, there is public concern about this technology. Because the skin penetration depth at these frequencies is only 1 mm or less, we exposed fibroblasts and keratinocytes to electromagnetic fields up to ten times the permissible limits, for 2 and 48 h in a fully blinded experimental design. Sham-exposed cells served as negative, and UV-exposed cells as positive controls. Differences in gene expression and methylation due to exposure were small and not higher than expected by chance. These data strongly support the assessment that there is no evidence for exposure-induced damage to human skin cells.},
 author = {Jyoti, Jyoti and Gronau, Isabel and Cakir, Eda and Hütt, Marc-Thorsten and Lerchl, Alexander and Meyer, Vivian},
 doi = {10.1093/pnasnexus/pgaf127},
 eprint = {https://academic.oup.com/pnasnexus/article-pdf/4/5/pgaf127/63049291/pgaf127.pdf},
 issn = {2752-6542},
 journal = {PNAS Nexus},
 month = {05},
 number = {5},
 pages = {pgaf127},
 title = {5G-exposed human skin cells do not respond with altered gene expression and methylation profiles},
 url = {https://doi.org/10.1093/pnasnexus/pgaf127},
 volume = {4},
 year = {2025}
}

                

                    @article{salehzadeh-yazdiAssessingImpactSampling2025,
 abstract = {Centrality measures are crucial for network analysis, offering insights into node importance within complex networks. However, their accuracy is often affected by observational errors and incomplete data. This study investigates how sampling biases systematically impact centrality measures. We simulate six types of biased down-sampling, transitioning networks from dense to sparse states, using the initial network as the ‘ground truth.’ Changes in centrality values reveal the robustness of these measures under various sampling scenarios across synthetic and biological networks. Our results show that in synthetic networks, some sampling methods consistently exhibit higher robustness, particularly in scale-free networks. For biological networks, protein interaction networks are the most robust, followed by metabolite, gene regulatory, and reaction networks. Local centrality measures generally show greater robustness, while global measures are more heterogeneous and less reliable. This study highlights the limitations of centrality measures under sampling biases and informs the development of more robust methodologies.},
 author = {Salehzadeh-Yazdi, Ali and Hütt, Marc-Thorsten},
 copyright = {2025 The Author(s)},
 doi = {10.1038/s41540-025-00526-w},
 file = {Full Text PDF:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/PNED283K/Salehzadeh-Yazdi und Hütt - 2025 - Assessing the impact of sampling bias on node cent.pdf:application/pdf},
 issn = {2056-7189},
 journal = {npj Systems Biology and Applications},
 keywords = {Complexity, Computer modelling, Systems analysis},
 language = {en},
 month = {May},
 note = {Publisher: Nature Publishing Group},
 number = {1},
 pages = {1--11},
 title = {Assessing the impact of sampling bias on node centralities in synthetic and biological networks},
 url = {https://www.nature.com/articles/s41540-025-00526-w},
 urldate = {2025-05-20},
 volume = {11},
 year = {2025}
}

                

                    @article{rumiantsauCategoriesImportantEdges2025,
 abstract = {How important is a single edge of a graph for a specific dynamical task? This question is of practical relevance to many research fields and is pivotal to understanding the structure–function relationships in complex networks more deeply. Here, we design an analysis strategy to answer it and explore the connection of such importance to network topology. Our approach for evaluating dynamical edge importance is based on the differences in time courses between dynamics on the original graph 𝐺 and on the graph 𝐺− missing an edge. To demonstrate the method’s versatility, we apply it to two drastically different classes of dynamics—a minimal model of excitable dynamics, and totalistic cellular automata on graphs as representatives of pattern formation. Our results suggest that the dynamical usage of a graph relies on markedly different topological attributes for these two classes of processes. Finally, we study dynamical edge importance in the macaque cortical area network, to illustrate possible real-world applications. We find that dynamical importance of edges differ between the network and its switch-randomized counterparts, and these differences can be functionally interpreted. Moreover, they are qualitatively distinct for long-time courses and short transients, highlighting different parts of the network’s intended function.},
 author = {Rumiantsau, Dzmitry and Falk, Johannes and Nyczka, Piotr and Hütt, Marc-Thorsten},
 doi = {10.1098/rsos.241086},
 file = {Full Text PDF:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/36SIMII9/Rumiantsau et al. - 2025 - Categories of important edges in dynamics on graph.pdf:application/pdf},
 journal = {Royal Society Open Science},
 keywords = {dynamical edge importance, dynamical task, excitable dynamics, macaque cortical area network, totalistic cellular automata},
 month = {April},
 note = {Publisher: Royal Society},
 number = {4},
 pages = {241086},
 title = {Categories of important edges in dynamics on graphs},
 url = {https://royalsocietypublishing.org/doi/10.1098/rsos.241086},
 urldate = {2025-05-20},
 volume = {12},
 year = {2025}
}

                

                    @article{messeExcitableDynamicsSimplify2025,
 author = {Messé, Arnaud and Hütt, Marc-Thorsten and Hilgetag, Claus Christian},
 doi = {10.1016/j.xcrp.2025.102510},
 issn = {2666-3864},
 journal = {Cell Reports Physical Science},
 keywords = {excitable dynamics, neural connectomes, weighted versus binary networks},
 language = {English},
 month = {April},
 note = {Publisher: Elsevier},
 number = {4},
 title = {Excitable dynamics simplify neural connectomes},
 url = {https://www.cell.com/cell-reports-physical-science/abstract/S2666-3864(25)00109-2},
 urldate = {2025-05-20},
 volume = {6},
 year = {2025}
}

                

                    @article{10.1093/bioinformatics/btaf095,
 abstract = {Inferring microbial interaction networks from microbiome data is a core task of computational ecology. An avenue of research to create reliable inference methods is based on a stylized view of microbiome data, starting from the assumption that the presences and absences of microbiomes, rather than the quantitative abundances, are informative about the underlying interaction network. With this starting point, inference algorithms can be based on the notion of attractors (asymptotic states) in Boolean networks. Boolean network framework offers a computationally efficient method to tackle this problem. However, often existing algorithms operating under a Boolean network assumption, fail to provide networks that can reproduce the complete set of initial attractors (abundance patterns). Therefore, there is a need for network inference algorithms capable of reproducing the initial stable states of the system.We study the change of attractors in Boolean threshold dynamics on signed undirected graphs under small changes in network architecture and show, how to leverage these relationships to enhance network inference algorithms. As an illustration of this algorithmic approach, we analyse microbial abundance patterns from stool samples of humans with inflammatory bowel disease (IBD), with colorectal cancer and from healthy individuals to study differences between the interaction networks of the three conditions. The method reveals strong diversity in IBD interaction networks. The networks are first partially deduced by an earlier inference method called ESABO, then we apply the new algorithm developed here, EDAME, to this result to generate a network that comes nearest to satisfying the original attractors.Implementation code is freely available at https://github.com/Jojo6297/edame.git.},
 author = {Jyoti, Jyoti and Hütt, Marc-Thorsten},
 doi = {10.1093/bioinformatics/btaf095},
 eprint = {https://academic.oup.com/bioinformatics/article-pdf/41/4/btaf095/62210140/btaf095.pdf},
 issn = {1367-4811},
 journal = {Bioinformatics},
 month = {03},
 number = {4},
 pages = {btaf095},
 title = {Evaluating changes in attractor sets under small network perturbations to infer reliable microbial interaction networks from abundance patterns},
 url = {https://doi.org/10.1093/bioinformatics/btaf095},
 volume = {41},
 year = {2025}
}

                

                    @article{Hiesmayr_Hütt_2024,
 abstractnote = {<p>A recent trend in mathematical modelling is to publish the computer code together with the research findings. Here we explore the formal question, whether and in which sense a computer implementation is distinct from the mathematical model. We argue that, despite the convenience of implemented models, a set of implicit assumptions is perpetuated with the implementation to the extent that even in widely used models the causal link between the (formal) mathematical model and the set of results is no longer certain. Moreover, code publication is often seen as an important contributor to reproducible research, we suggest that in some cases the opposite may be true. A new perspective on this topic stems from the accelerating trend that in some branches of research only implemented models are used, e.g., in artificial intelligence (AI). With the advent of quantum computers, we argue that completely novel challenges arise in the distinction between models and implementations.</p>},
 author = {Hiesmayr, Beatrix C. and Hütt, Marc-Thorsten},
 doi = {10.4081/peasa.26},
 journal = {Proceedings of the European Academy of Sciences and Arts},
 month = {Jun.},
 title = {Is a mathematical model equivalent to its computer implementation?},
 url = {https://www.peasa.eu/site/article/view/26},
 volume = {3},
 year = {2024}
}

                

                    @article{messe2024binary,
 author = {Messé, Arnaud and Hütt, Marc-Thorsten and Hilgetag, Claus C},
 journal = {bioRxiv},
 pages = {2024--06},
 publisher = {Cold Spring Harbor Laboratory},
 title = {Binary Brains: How Excitable Dynamics Simplify Neural Connectomes},
 year = {2024}
}

                

                    @incollection{merten2024comparative,
 author = {Merten, Daniel Christopher and Hütt, Marc-Thorsten and Uygun, Yilmaz and Özgür, Atilla and Klein, Carsten Andreas},
 booktitle = {Steel 4.0: digitalization in steel industry},
 pages = {125--141},
 publisher = {Springer},
 title = {Comparative study of two genetic algorithms for steel production planning under different order backlog circumstances},
 year = {2024}
}

                

                    @article{lyutov2024machine,
 author = {Lyutov, Alexey and Uygun, Yilmaz and Hütt, Marc-Thorsten},
 journal = {Scientific Reports},
 number = {1},
 pages = {21906},
 publisher = {Nature Publishing Group UK London},
 title = {Machine learning misclassification networks reveal a citation advantage of interdisciplinary publications only in high-impact journals},
 volume = {14},
 year = {2024}
}

                

                    @article{mendler2024microbiome,
 author = {Mendler, Isabella-Hilda and Drossel, Barbara and Hütt, Marc-Thorsten},
 journal = {Physica A: Statistical Mechanics and its Applications},
 pages = {129658},
 publisher = {Elsevier},
 title = {Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks},
 volume = {639},
 year = {2024}
}