Wolfram Liebermeister
Wolfram Liebermeister
wolfram.liebermeister@gmail.com

INRAE - Unité MaIAGE
Domaine de Vilvert
78352 Jouy-en-Josas, France
Charité Berlin - Institut für Biochemie
Charitéplatz 1
10117 Berlin, Germany
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DFG-funded research project
"Dynamics and function of enzyme regulation in large metabolic networks"
Methods for model construction

A main aim of this project is the semi-automatic construction of kinetic metabolic models. A number of methods, using different model assumptions and various (heterogenous and possibly incomplete) data have been developed. All workflows have been developed in matlab (code is freely available). Data are given in the SBtab data format, models in SBtab or SBML format.

Parameter balancing

  1. Purpose: Determine consistent model parameters (in particular, kinetic constants) for a metabolic model
  2. Input data: Model structure, measured kinetic constants, thermodynamic constants
  3. Output data: Complete, consistent set of model parameters (point estimated and posterior distribution)
  4. Article Parameter balancing
  5. Website Parameter balancing

Elasticity sampling

  1. Purpose: Determine phyiologically reasonable metabolic states (metabolite levels, fluxes, elasticities, kinetic constants) for a metabolic model
  2. Input data: Model structure, possibly predefined fluxes and metabolite levels
  3. Output data: Complete, consistent set of control properties (elasticities, control coefficients) as a well as model parameters; sampling is possible
  4. Article Preprint on Elasticity sampling
  5. Website Structural thermokinetic modelling

Model embedding

  1. Purpose: Embed given kinetic pathway model(s) into metabolic network, translate surrounding parts of the network into a kinetic model
  2. Input data: Kinetic pathway model, metabolic network
  3. Output data: Joint model (pathway embedded in network)
  4. Article Model embedding
  5. Website Model embedding

Enzyme cost minimisation

  1. Purpose: Determine an optimal metabolic state for a given kinetic model with given flux distribution
  2. Input data: Kinetic pathway model, flux distribution
  3. Output data: Metabolite and enzyme levels
  4. Article Enzyme cost minimisation
  5. Website Enzyme cost minimisation

Flux cost minimisation

  1. Purpose: Determine optimal metabolic states, including metabolic fluxes, for a given kinetic model
  2. Input data: Kinetic pathway model
  3. Output data: Metabolic fluxes, metabolite and enzyme levels
  4. Article Preprint on BioRxiv
  5. Website Flux cost minimisation