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Lea Seep


Endenicher Allee 64, 53115 Bonn, Germany

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Short CV

Lea Seep has done her Bachelor’s degree in Molecular Biomedicine in Bonn, starting to gain insights into the field of bioinformatics during her bachelor thesis. To further explore the field of life-science related informatics she conducted a full-time work experience at Bayer for 6 months within the Computational Chemistry group, before conducting her M.Sc. in Bioinformatics at the University of Potsdam. There she was introduced to constraint-based modelling, machine learning, systems-biology and network analysis. In

her Master thesis she combined network analysis, statistical analysis and classical machine learning to search for patterns explaining a certain biological phenomena. She has joined the group of Prof. Hasenauer as PhD student in December 2021.


  • Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis. Seep, L., Razaghi-Moghadam, Z., Nikoloski, Z. (2021)
  • Ensemble Completeness in Conformer Sampling: Small Macrocycles. Seep, L. , Bonin, A., Meier, K., Göller, A. H. (2021).
  • Disease severity-specific neutrophil signatures in blood transcriptomes stratify COVID-19 patients. Aschenbrenner, A. C., Mouktaroudi, M., Krämer, B., Antonakos, N., Oestreich, M., Gkizeli, K., Nuesch-Germano, M., Saridaki, M., Bonaguro, L, Reusch, N., Baßler, K., Doulou, S., Knoll, R., Pecht, T., Kapellos, T. S., Rovina, N., Kröger, C., Herbert, M., Holsten, L., Horne, A., Gemünd, I. D., Agrawal, S., Dahm, K., van Uelft, M., Drews, A., Lenkeit, L., Bruse, N., Gerretsen, J., Gierlich, J., Becker, M.,Händler, K., Kraut, M., Theis, H., Mengiste, S., De Domenico, E., Schulte-Schrepping, J., Seep, L., Raabe, J., Hoffmeister, C., ToVinh, M., Keitel, V., Rieke, G., Talevi, V., Aziz, N. A., Pickkers, P., van de Veerdonk, F., Netea, M. G., Schultze, J. L., Kox, M., Breteler, M. M. B., Nattermann, J., Koutsoukou, A., Giamarellos-Bourboulis, E. J., Ulas, T., German COVID-19 Omics Initiative (DeCOI) (2021)
  • Structure-Permeability Relationship of Semi-Peptidic Macrocycles – Understanding and Optimizing Passive Permeability and Efflux Ratio. Le Roux, A., Blaise, E., Boudreault, P., Comeau, C., Doucet, A., Giarrusso, M., Collin, M., Neubauer, T., Koelling, F., Göller, A., Seep, L., Tshitenge, D., Wittwer, M., Kullmann, M., Hillisch, A., Mittendorf, J., Marsault, É.(2020)
  • Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. Warnat-Herresthal, S., Perrakis, K., Taschler, B., Becker, M., Baßler, K., Beyer, M., Günther, P., Schulte-Schrepping, J., Seep, L., Klee, K., Ulas, T., Haferlach, T., Mukherjee, S., Schultze, J.L. (2020).
  • Transcriptional Signatures Derived from Murine Tumor-Associated Macrophages Predict Outcome in Breast Cancer Patients. Tuit, S., Salvagno, C., Kapellos, T. S., Hau, C. S., Seep, L., Oestreich, M., Klee, K., de Visser, K.E., Ulas, T., Schultze, J. L. (2019).
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