Prof. Dr. Alexander Bockmayr

Projects as a project leader

• SE16

  Numerical solution of dynamic metabolic resource allocation problems for bioenergy production

  Prof. Dr. Alexander Bockmayr / Prof. Dr. Volker Mehrmann

  Project heads: Prof. Dr. Alexander Bockmayr / Prof. Dr. Volker Mehrmann
  Project members: Dr. Markus Arthur Köbis
  Duration: 01.06.2017 - 31.12.2018
  Status: completed
  Located at: Freie Universität Berlin
  

  Description

  In the field of sustainable energies, microbial cell factories such as yeasts and cyanobacteria are receiving increasing interest due to their potential to produce biofuels. A major question is how the metabolism of these microorganisms is coordinated in a dynamic environment such that the correct macromolecules are synthesized at the right time in order to enable growth and survival. Recent mathematical modeling approaches have made it possible to study this problem using an optimal dynamic resource allocation formalism such as dynamic enzyme-cost flux balance analysis (deFBA). The goal of this project is to study the mathematical properties properties of the underlying optimal control problem involving differential-algebraic constraints and to develop efficient numerical solution strategies.
  
  https://www.mi.fu-berlin.de/en/math/groups/mathlife/projects_neu/SE16/index.html

• SE-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: completed
  Located at: Freie Universität Berlin
  

  Description

  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.
  
  https://www.erasysapp.eu/calls/2nd-call/robustyeast

• 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: -
  Status: completed
  Located at: Freie Universität Berlin / Konrad-Zuse-Zentrum für Informationstechnik Berlin
  

  Description

  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.
  
  http://www.mi.fu-berlin.de/en/math/groups/dibimath/projects/A-CH5/index.html