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

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Konferenzbeitrag

Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons97261

Larhlimi,  A.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97064

Basler,  G.
Mathematical Modelling and Systems Biology - Nikoloski, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97175

Grimbs,  S.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97409

Selbig,  J.
BioinformaticsCRG, Cooperative Research Groups, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons97320

Nikoloski,  Z.
Mathematical Modelling and Systems Biology, Department Willmitzer, Max Planck Institute of Molecular Plant Physiology, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)
Es sind keine frei zugänglichen Volltexte verfügbar
Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Larhlimi, A., Basler, G., Grimbs, S., Selbig, J., & Nikoloski, Z. (2012). Stoichiometric capacitance reveals the theoretical capabilities of metabolic networks. In 11th European Conference on Computational Biology (ECCB) / Conference of the Intelligent Systems in Molecular Biology (ISMB) (pp. I502-I508).


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-1F42-F
Zusammenfassung
Motivation: Metabolic engineering aims at modulating the capabilities of metabolic networks by changing the activity of biochemical reactions. The existing constraint-based approaches for metabolic engineering have proven useful, but are limited only to reactions catalogued in various pathway databases. Results: We consider the alternative of designing synthetic strategies which can be used not only to characterize the maximum theoretically possible product yield but also to engineer networks with optimal conversion capability by using a suitable biochemically feasible reaction called 'stoichiometric capacitance'. In addition, we provide a theoretical solution for decomposing a given stoichiometric capacitance over a set of known enzymatic reactions. We determine the stoichiometric capacitance for genome-scale metabolic networks of 10 organisms from different kingdoms of life and examine its implications for the alterations in flux variability patterns. Our empirical findings suggest that the theoretical capacity of metabolic networks comes at a cost of dramatic system's changes.