Prof. Dr. Alexander Bockmayr

Head of group "Mathematics in Life Sciences"

Institut für Mathematik
Arnimallee 6
14195 Berlin
+49 (0) 30 838 75867

Research focus

Mathematical systems biology, metabolic and regulatory networks

Projects as a project leader

  • A-CH5

    Model classification under uncertainties for cellular signaling networks

    Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert

    Project heads: Prof. Dr. Alexander Bockmayr / Prof. Dr. Susanna Röblitz / Prof. Dr. Heike Siebert
    Project members: Stefanie Kasielke / Adam Streck
    Duration: 01.06.2014 - 31.05.2017
    Status: running
    Located at: Freie Universität Berlin / Konrad-Zuse-Zentrum für Informationstechnik Berlin


    Mathematical modelling in biological and medical applications is almost always faced with the problem of incomplete and noisy data. Rather than adding unsupported assumptions to obtain a unique model, a different approach generates a pool of models in agreement with all available observations. Analysis and classification of such models allow linking the constraints imposed by the data to essential model characteristics and showcase different implementations of key mechanisms. Within the project, we aim at combining the advantages of logical and continuous modeling to arrive at a comprehensive system analysis under data uncertainty. Model classification will integrate qualitative aspects such as characteristics of the network topology with more quantitative information extracted from clustering of joint parameter distributions derived from Bayesian approaches. The theory development is accompanied by and tested in application to oncogenic signaling networks.
  • A-AP25

    Optimizing metabolic regulation in yeast production strains for dynamic conditions

    Prof. Dr. Alexander Bockmayr

    Project heads: Prof. Dr. Alexander Bockmayr
    Project members: Alexandra-M. Reimers
    Duration: 01.12.2015 - 30.11.2018
    Status: running
    Located at: Freie Universität Berlin


    Microbial strains used in biotechnological industry need to produce their biotechnological products at high yield and at the same time they are desired to be robust to the intrinsic nutrient dynamics of large-scale bioreactors, most noticeably to transient limitations of carbon sources and oxygen. The engineering principles for robustness of metabolism to nutrient dynamics are however not yet well understood. The ROBUSTYEAST project aims to reveal these principles for microbial strain improvement in biotechnological applications using a systems biology approach. This will contribute to establishing evolutionary optimization protocols for making microbial production strains robust against dynamic nutrient conditions. The consortium will study the robustness of Saccharomyces cerevisiae in experiments during the dynamics associated with two cyclic nutrient transitions that are each of major relevance to industry: repeated cycles of glucose and ethanol growth and of aerobic and anaerobic growth. We shall monitor the physiological changes during the evolutionary adaptation of yeast to those transitions, using laboratory-evolution in lab-scale bioreactors (chemostat mode). By combining this data with computational modelling we shall identify the metabolic features that make yeast robust to these industrially relevant condition cycles. The theoretical and computational approaches that the consortium will develop involve optimisation methods applicable to metabolism transiting from one steady state to the next via dynamic regulation. We shall iterate experiments and modelling to improve our models given experimental data, to identify new measurements critical to improve our understanding, and to finally identify key regulatory mechanisms for a robust metabolism of S. cerevisiae, given changes in glucose, ethanol and oxygen concentrations. The robustness of metabolic regulation under dynamic conditions will be evaluated from the kinetic models, and the regulatory interactions that confer such robustness will be determined.