(Large-scale) signaling models

For different cellular processes it is nowadays possible to develop genome-scale mathematical models, for example constraint-based-models of metabolism. However, in large-scale signaling networks the multitude of states for single system components as well as the elaborate interactions between these components render mathematical models in most cases far to complex for the available data and/or the computational possibilities. In this section we present approaches aiming to overcome those limitations by providing descriptions and formalisms, tools and finally mathematical models dedicated to large-scale signaling networks.








rxncon 2.0 publications


  1. U Münzner, T Lubitz, E Klipp and M Krantz.
    Toward genome-scale models of signal transduction networks.
    In J Nielsen and S Hohmann (eds.). Systems Biology. Wiley-VCH, 2017, pages 215–242.

  2. J C Romers and M Krantz.
    rxncon 2.0: a language for executable molecular systems biology.
    bioRxiv, 2017.

  3. Jesper Romers, Sebastian Thieme, Ulrike Münzner and Marcus Krantz.
    A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models..
    NPJ systems biology and applications 6:2, 2020.


rxncon 1.0 publications


  1. C-F Tiger, F Krause, G Cedersund, R Palmér, E Klipp, S Hohmann, H Kitano and M Krantz.
    A framework for mapping, visualisation and automatic model creation of signal-transduction networks.
    Mol. Syst. Biol. 8:578, April 2012.

  2. M Flöttmann, F Krause, E Klipp and M Krantz.
    Reaction-contingency based bipartite Boolean modelling.
    BMC Syst. Biol. 7:58, July 2013.

  3. M Rother, U Münzner, S Thieme and M Krantz.
    Information content and scalability in signal transduction network reconstruction formats.
    Mol. Biosyst. 9 (8):1993–2004, August 2013.

  4. T Lubitz, N Welkenhuysen, S Shashkova, L Bendrioua, S Hohmann, E Klipp and M Krantz.
    Network reconstruction and validation of the Snf1/AMPK pathway in baker's yeast based on a comprehensive literature review.
    npj Syst. Biol. Appl. 1:15007, 2015.












TRR 175 Green Hub Consortium







Humboldt-Universität zu Berlin, Institute of Biology,
Theoretical Biophysics, 10099 Berlin, Germany