Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades / von Jörn Arnold Behre
The dissertation covers the topic of structural robustness of metabolic networks on the basis of the concept of elementary flux modes (EFMs). It is shown that the number of EFMs does not reflect the topology of a network sufficiently. Thus, new methods are developed to determine the structural robustness of metabolic networks. These methods are based on systematic in-silico knockouts and the subsequent calculation of dropped out EFMs. Thereby, together with single knockouts also double and multiple knockouts can be used. After evaluation of these methods they are applied to metabolic networks of human erythrocyte and hepatocyte as well as to a metabolic network of Escherichia coli (E. coli). It is found that the erythrocyte has the lowest structural robustness, followed by the hepatocyte and E. coli. These results coincide very well with the circumstance that human erythrocyte and hepatocyte and E. coli are able to adapt to conditions with increasing diversity. In a further part of the dissertation the concept of EFMs is expanded to signal transduction pathways consisting of kinase cascades. The concept of EFMs is based on the steady-state condition for metabolic pathways. It is shown that under certain circumstances this steady-state condition also holds for signalling cascades. Furthermore, it is shown that it is possible to deduce minimal conditions for signal transduction without knowledge about the kinetics involved. On the basis of these assumptions it is possible to calculate EFMs for signalling cascades. But due to the fact that these EFMs do no longer just have mass flux but also information flux, they are now called elementary signalling modes (ESMs).
|Dissertation Note:||Jena, Univ., Diss., 2012|
|Subjects:||Theoretische Biologie > Dynamisches Netzwerk > Robustheit > Methode|
|Type of content:||Hochschulschrift|
|Related resources:||Erscheint auch als Online-Ausgabe: Structural modelling and robustness analysis of complex metabolic networks and signal transduction cascades|
|Physical description:||XXVI, 163 S. : graph. Darst. ; 30 cm|
|Basic Classification:||42.11 Biomathematik, Biokybernetik|