Christoph Lüders

PhD Student WG Weber
Endenicher Allee 19A, Raum
D-53115 Bonn
Telefon: +49 (0) 228 73-69623
Fax: +49 (0) 228 73-4212
E-Mail: chris@REMOVETHISPART.cfos.de

Christoph Lüders studied computer science at the University of Bonn, Germany and received his MS (Dipl.-Inform.) in 2014. Since 2014 he is a doctoral student of computer science, advised by Andreas Weber. From 2015 to 2016 he had a Stiftungsfonds Johannes Kepler scholarship. He co-founded cFos Software GmbH in 1993.

Research interests

  • Tropical geometry, fast calculation of tropical prevarieties
  • Chemical reaction networks, bifurcation and robustness analysis
  • Fast multiplication of large integers, from optimized Karatsuba to Schönhage-Strassen and beyond (like De, Kurur, Saha and Saptharishi's algorithm)
  • Machine learning


Satya Samal, Jeyashree Krishnan, Christoph Lüders, Andreas Schuppert, Andreas Weber, and Ovidiu Radulescu
International Conference on Systems Biology of Human Disease (SBHD), Heidelberg. Poster, 2017
In proceedings of Proceedings of the 2015 ACM on International Symposium on Symbolic and Algebraic Computation, pages 267-274, 2015
Diploma thesis, Universität Bonn, Apr. 2014

Ongoing Projects

SYMBIONT is an interdisciplinary project ranging from mathematics via computer science to systems biology and systems medicine. The project has a clear focus on fundamental research on mathematical methods, and prototypes in software, which is in turn benchmarked against models from computational biology databases. Computational models in systems biology are built from molecular interaction networks and rate parameters resulting in large systems of differential equations. These networks are foundational for systems medicine. The currently prevailing numerical approaches shall be complemented with our novel algorithmic symbolic methods, which will address fundamental problems in this area. One important problem is that statistical estimation of model parameters is computationally expensive and many parameters are not identifiable from experimental data. In addition, there is typically a considerable uncertainty about the exact form of the mathematical model itself. The parametric uncertainty (with wide potential variations of parameters by several orders of magnitudes) leads to severe limitations of numerical approaches even for rather small and low dimensional models. Furthermore, extant model inference and analysis methods suffer from the curse of dimensionality that sets an upper limit of about ten variables to the tractable models. For those reasons, the formal deduction of principle properties of large and very large models has a very high relevance. The main goal of SYMBIONT is to combine symbolic methods with model reduction methods for the analysis of biological networks. We propose new methods for symbolic analysis, which overcome the above mentioned obstacles and therefore can be applied to large networks. In order to cope more effectively with the parameter uncertainty problem, we impose an entirely new paradigm replacing thinking about single instances with thinking about orders of magnitude. Our computational methods are diverse and involve various branches of mathematics such as tropical geometry, real algebraic geometry, theories of singular perturbations, invariant manifolds and symmetries of differential systems. The foundations and validity of our methods will be carefully secured by mathematical investigation. Corresponding computer algebra problems are NP-hard, but experiments point at their feasibility for biological networks. We have already shown that complexity parameters such as tree-width or number of distinct metastable regimes grow only slowly with size for models available in existing biological databases. We will exploit this observation to solve challenging problems in network analysis including determination of parameter regions for the existence and stability of attractors, model reduction, and characterization of qualitative dynamics of nonlinear networks. The methods developed in this project will be benchmarked against existing biological models and also against more challenging models, closer to the needs of systems and precision medicine that will be generated using biological pathways databases.