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{kosmidis2023dna,
 author = {Kosmidis, Kosmas and Hütt, Marc-Thorsten},
 journal = {Physica A: Statistical Mechanics and its Applications},
 pages = {129043},
 publisher = {Elsevier},
 title = {DNA visibility graphs},
 volume = {626},
 year = {2023}
}


                

                    @inproceedings{hutt2023machine,
 author = {Hütt, Marc and Lyutov, Alexey and Uygun, Yilmaz},
 booktitle = {Networks in Science of Science at Network Science Conference},
 title = {Machine learning misclassification networks reveal that interdisciplinary publications are rewarded only in high-impact journals},
 year = {2023}
}


                

                    @article{zimmermann2023marine,
 author = {Zimmermann, Heike H and Stoof-Leichsenring, Kathleen R and Dinkel, Viktor and Harms, Lars and Schulte, Luise and Hütt, Marc-Thorsten and Nürnberg, Dirk and Tiedemann, Ralf and Herzschuh, Ulrike},
 journal = {Nature Communications},
 number = {1},
 pages = {1650},
 publisher = {Nature Publishing Group UK London},
 title = {Marine ecosystem shifts with deglacial sea-ice loss inferred from ancient DNA shotgun sequencing},
 volume = {14},
 year = {2023}
}


                

                    @article{mendler2023microbiome,
 author = {Mendler, Isabella-Hilda and Drossel, Barbara and Hütt, Marc-Thorsten},
 journal = {arXiv preprint arXiv:2306.02100},
 title = {Microbiome abundance patterns as attractors and the implications for the inference of microbial interaction networks},
 year = {2023}
}


                

                    @article{rumiantsau2023predicting,
 author = {Rumiantsau, Dzmitry and Lesne, Annick and Hütt, Marc-Thorsten},
 journal = {arXiv preprint arXiv:2301.10370},
 title = {Predicting attractors from spectral properties of stylized gene regulatory networks},
 year = {2023}
}


                

                    @article{voutsa2023attractor,
 author = {Voutsa, Venetia and Papadopoulos, Michail and Papadopoulou Lesta, Vicky and Hütt, Marc-Thorsten},
 journal = {Chaos: An Interdisciplinary Journal of Nonlinear Science},
 number = {8},
 publisher = {AIP Publishing},
 title = {The attractor structure of functional connectivity in coupled logistic maps},
 volume = {33},
 year = {2023}
}


                

                    @article{messe2023purpose,
 author = {Messé, Arnaud and Hütt, Marc-Thorsten and Hilgetag, Claus},
 title = {The purpose of weights in excitable brain networks},
 year = {2023}
}


                

                    @article{falk2023topological,
 author = {Falk, Johannes and Eichler, Edwin and Windt, Katja and Hütt, Marc-Thorsten},
 journal = {arXiv preprint arXiv:2301.10550},
 title = {Topological insulators and enhancers in networks under generic problem-solving dynamics},
 year = {2023}
}


                

                    @article{merten2022effect,
 author = {Merten, Daniel Christopher and Hütt, Marc-Thorsten and Uygun, Yilmaz},
 journal = {Computers & Industrial Engineering},
 pages = {108--120},
 title = {A network analysis of decision strategies of human experts in steel manufacturing},
 year = {2022}
}


                

                    @article{hajaliInferringMissingEdges2022,
 abstract = {Many real-life networks are incomplete. Dynamical observations can allow estimating missing edges. Such procedures, often summarized under the term ‘network inference’, typically evaluate the statistical correlations among pairs of nodes to determine connectivity. Here, we offer an alternative approach: completing an incomplete network by observing its collective behavior. We illustrate this approach for the case of patterns emerging in reaction-diffusion systems on graphs, where collective behaviors can be associated with eigenvectors of the network's Laplacian matrix. Our method combines a partial spectral decomposition of the network's Laplacian matrix with eigenvalue assignment by matching the patterns to the eigenvectors of the incomplete graph. We show that knowledge of a few collective patterns can allow the prediction of missing edges and that this result holds across a range of network architectures. We present a numerical case study using activator-inhibitor dynamics and we illustrate that the main requirement for the observed patterns is that they are not confined to subsets of nodes, but involve the whole network.},
 author = {Haj Ali, Selim and Hütt, Marc-Thorsten},
 doi = {10.1103/PhysRevE.105.064610},
 file = {APS Snapshot:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/HS5VIBKF/PhysRevE.105.html:text/html;Full Text PDF:/home/johannes/.mozilla/firefox/5ah0aj87.default/zotero/storage/I3PDVLD8/Haj Ali und Hütt - 2022 - Inferring missing edges in a graph from observed c.pdf:application/pdf},
 journal = {Physical Review E},
 month = {June},
 note = {Publisher: American Physical Society},
 number = {6},
 pages = {064610},
 title = {Inferring missing edges in a graph from observed collective patterns},
 url = {https://link.aps.org/doi/10.1103/PhysRevE.105.064610},
 urldate = {2022-09-09},
 volume = {105},
 year = {2022}
}