Wolfram Liebermeister
Wolfram Liebermeister

Institut für Biochemie
Charité - Universitätsmedizin Berlin
Charitéplatz 1, 10117 Berlin
Tel. +49 30 450 528025

Lecturer (Privatdozent) at
Humboldt Universität zu Berlin


Project | Publications | Presentations | Teaching | Textbook | Personal

Research project "Dynamics and function of enzyme regulation in large metabolic networks"

DFG-funded project at Charité - Universitätsmedizin Berlin

Kinetic models are essential to better understand the dynamics and function of enzyme regulation, and hence to predict the metabolic effects of differential enzyme expression and enzyme-inhibiting drugs. However, kinetic modelling is not yet applicable to large, genome-scale networks. Existing genome-scale modelling approaches, such as flux balance analysis, are based on stoichiometry only and therefore inherently limited in use. This project aims to fill the gap between genome-scale stoichiometric and small-scale kinetic models by the development of a novel kinetic modelling approach for large metabolic networks. The method combines metabolic control analysis with data integration and sampling techniques and accounts for thermodynamic constraints. The results will be probabilistic, reflecting the availability and quality of input data.

The applicability of the new method will be tested through its use to create large network models and to predict flux changes caused by enzyme regulation, to couple these models with detailed kinetic pathway models, to compute synergisms between enzyme-inhibiting drugs, to predict the dynamic effects of alternating enzyme levels, and to study the advantages of enzymatic regulation and alternating enzyme levels in fluctuating environments through a computational cost-benefit approach. Through external collaborations, model predictions will be validated with experimental omics data from bacterial and yeast cultures and from human hepatocytes. By extending dynamic modelling to large metabolic networks, the project will substantially improve the understanding of enzyme regulation, with potential future applications in cell simulation, prediction of drug interactions and side effects, and chronotherapies.

Methods and theory developed in this project

  1. Elasticity sampling
  2. Model embedding
  3. Enzyme cost minimization
  4. Optimal enzyme rhythms
  5. Metabolic economics
  6. Parameter balancing
  7. Replicate time series regression
  8. SBtab data format

Matlab code developed in this project

  1. Metabolic Network Toolbox
  2. Parameter balancing (included in Metabolic Network Toolbox)
  3. Elasticity sampling (based on Metabolic Network Toolbox)
  4. Model embedding (based on Metabolic Network Toolbox)
  5. Enzyme cost minimization (based on Metabolic Network Toolbox)
  6. SBtab data format

Workflows, models and data

  1. Model construction methods overview
  2. Yeast metabolic oscillations (preliminary data and simulations)
  3. Model and data resources