A comprehensive, mechanistically detailed, and executable model of the Cell Division Cycle in Saccharomyces cerevisiae U Münzner, E Klipp, M Krantz Nature communications 10 (1), 1-12, 2019 | 49 | 2019 |
Information content and scalability in signal transduction network reconstruction formats M Rother, U Münzner, S Thieme, M Krantz Molecular BioSystems 9 (8), 1993-2004, 2013 | 16 | 2013 |
A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models J Romers, S Thieme, U Münzner, M Krantz npj Systems Biology and Applications 6 (1), 1-13, 2020 | 12 | 2020 |
A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models JC Romers, S Thieme, U Muenzner, M Krantz bioRxiv, 107235, 2018 | 12 | 2018 |
Toward genome-scale models of signal transduction networks U Münzner, T Lubitz, E Klipp, M Krantz Systems Biology 6, 2017 | 11 | 2017 |
A yeast cell cycle model integrating stress, signaling, and physiology SO Adler, TW Spiesser, F Uschner, U Münzner, J Hahn, M Krantz, E Klipp FEMS Yeast Research, 2022 | 6 | 2022 |
Bipartite Boolean modelling-a method for mechanistic simulation and validation of large-scale signal transduction networks S Thieme, J Romers, U Muenzner, M Krantz bioRxiv, 107235, 2017 | 6 | 2017 |
Using rxncon to Develop Rule-Based Models J Romers, S Thieme, U Münzner, M Krantz Modeling Biomolecular Site Dynamics, 71-118, 2019 | 4 | 2019 |
Identification of periodic attractors in Boolean networks using a priori information U Münzner, T Mori, M Krantz, E Klipp, T Akutsu PLOS Computational Biology 18 (1), e1009702, 2022 | 2 | 2022 |
From birth to birth A cell cycle control network of S. cerevisiae UTE Münzner Humboldt-Universität zu Berlin, 2017 | 1 | 2017 |