de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

Biochemical network models simplified by balanced truncation

MPG-Autoren

Liebermeister,  Wolfram
Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons50384

klipp,  Edda
Independent Junior Research Groups (OWL), Max Planck Institute for Molecular Genetics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

Liebermeister et al. - FEBS J.pdf
(beliebiger Volltext), 685KB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Liebermeister, W., Baur, U., & klipp, E. (2005). Biochemical network models simplified by balanced truncation. FEBS Journal, 272(16), 4034-4043. doi:10.1111/j.1742-4658.2005.04780.x.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0010-85DF-6
Zusammenfassung
Modelling of biochemical systems usually focuses on certain pathways, while the concentrations of so-called external metabolites are considered fixed. This approximation ignores feedback loops mediated by the environment, that is, via external metabolites and reactions. To achieve a more realistic, dynamic description that is still numerically efficient, we propose a new methodology: the basic idea is to describe the environment by a linear effective model of adjustable dimensionality. In particular, we (a) split the entire model into a subsystem and its environment, (b) linearize the environment model around a steady state, and (c) reduce its dimensionality by balanced truncation, an established method for large-scale model reduction. The reduced variables describe the dynamic modes in the environment that dominate its interaction with the subsystem. We compute metabolic response coefficients that account for complexity-reduced dynamics of the environment. Our simulations show that a dynamic environment model can improve the simulation results considerably, even if the environment model has been drastically reduced and if its kinetic parameters are only approximately known. The speed-up in computation gained by model reduction may become vital for parameter estimation in large cell models.