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Design principles and control mechanisms of signal transduction networksBinder, Bernd 10 June 2005 (has links)
Diese Arbeit basiert auf der grundlegenden Annahme, dass Signaltransduktionsnetzwerke in lebenden Organismen als das Ergebnis eines evolutionären Prozesses betrachtet werden können, der sich durch Mutations- und Selektionsprinzipien auszeichnet. Basierend auf dieser Hypothese werden zwei Ansätze vorgestellt, um Design und Kontrollmechanismen von Netzwerken zu untersuchen. In Kapitel 2 wird ein Modell entwickelt, welches die Struktur analysiert. Ein vereinfachtes Modell wird benutzt, um Systeme, bestehend aus Rezeptoren, Kinasen und Phosphatasen, zu beschreiben. Zwei dynamische Eigenschaften sollten für die Überlebensfähigkeit der Organismen eine wichtige Rolle spielen: (i) Um eine Autoaktivierung zu verhindern, z.B. gegenüber stochastischen Schwankungen von Rezeptorliganden, muss der Signal-Aus Zustand dynamisch stabil sein. (ii) Das Ausgangssignal sollte verstärkt werden. Um Netzwerke zu charakterisieren, die beide Kriterien gleichzeitig erfüllen, wird eine systematische Analyse von kleinen Netzwerken durchgeführt. Die Untersuchungen machen deutlich, dass für solche Netzwerke die folgenden zwei Design-Eigenschaften gelten: (i) Mit steigender Netzwerkgröße verringert sich die Konnektivität, was einer steigenden Spezifität der Kinasen gleichkommt. (ii) Die Anzahl der "Feedback"-Zyklen nimmt mit zunehmender Netzwerkgröße ab, was eine abnehmende Tendenz anzeigt, dass "nachgeschaltete" Kinasen "vorgelagerte" Kinasen aktivieren. Die allgemeine Gültigkeit dieser Eigenschaften wird durch die Untersuchung eines großen Kinase-Netzwerks aus der Datenbank TRANSPATH gestützt, welches hinsichtlich verschiedener Strukturmerkmale weitergehend untersucht. Das Netzwerk-Design unterscheidet sich aussagekräftig von Zufallsnetzen. In Kapitel 3 werden Kontrollmechanismen unterschiedlicher Signalnetzwerke analysiert, indem die Metabolische Kontroll Theorie auf transiente Aktivierungsprofile angewendet wird, indem Kontrollkoeffizienten für Amplitude, Signaldauer berechnet werden. / This work is based on the hypothesis that signal transduction networks in living cells are the result of an evolutionary development which is governed by mutations and natural selection principles. Based on this working hypothesis, two approaches are presented to investigate design and control mechanisms of signal transduction networks. In the first approach, covered in chapter 2, a model is developed to analyse the structural design of signaling networks. A simplified model is used to describe systems consisting of receptors, kinases and phosphatases. Two dynamic features of signaling systems are assumed to be crucial for the organism''s surviving capacity: (i) To avoid autoactivation, e.g. due to stochastic fluctuations of receptor ligand, the signal off-state must be dynamically stable. (ii) The signal output should be amplified. To characterise networks fulfilling both criteria simultaneously, a systematic analysis is performed for small networks. The investigations reveal that for such networks the following two design principles hold true: (i) With increasing network size the connectivity decreases connoting an increasing specificity of kinase activities. (ii) The number of feedback cycles decrease with increasing network size indicating a decreasing tendency of downstream kinases to activate upstream kinases. The general validity of these design principles is supported by the analysis of a large kinase network retrieved from the TRANSPATH database. This signaling network is further investigated regarding several design properties. When comparing the design and dynamic features of the TRANSPATH network to random networks, significant differences are observed. In the second approach, described in chapter 3, control mechanisms of different signaling networks are analysed by applying Metabolic Control Analysis to transient activation profiles. Control coefficients on the signal amplitude, signal duration and integrated response are calculated.
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A Chance Constraint Model for Multi-Failure Resilience in Communication NetworksHelmberg, Christoph, Richter, Sebastian, Schupke, Dominic 03 August 2015 (has links) (PDF)
For ensuring network survivability in case of single component failures many routing protocols provide a primary and a back up routing path for each origin destination pair. We address the problem of selecting these paths such that in the event of multiple failures, occuring with given probabilities, the total loss in routable demand due to both paths being intersected is small with high probability. We present a chance constraint model and solution approaches based on an explicit integer programming formulation, a robust formulation and a cutting plane approach that yield reasonably good solutions assuming that the failures are caused by at most two elementary events, which may each affect several network components.
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A Chance Constraint Model for Multi-Failure Resilience in Communication NetworksHelmberg, Christoph, Richter, Sebastian, Schupke, Dominic 03 August 2015 (has links)
For ensuring network survivability in case of single component failures many routing protocols provide a primary and a back up routing path for each origin destination pair. We address the problem of selecting these paths such that in the event of multiple failures, occuring with given probabilities, the total loss in routable demand due to both paths being intersected is small with high probability. We present a chance constraint model and solution approaches based on an explicit integer programming formulation, a robust formulation and a cutting plane approach that yield reasonably good solutions assuming that the failures are caused by at most two elementary events, which may each affect several network components.
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