Mathematical modelling in cell biology : from highthroughput data to systems analysis ; Mathematische Modellierung in der Zellbiologie : von Daten- zur Systemanalyse
Titel:
Mathematical modelling in cell biology : from highthroughput data to systems analysis ; Mathematische Modellierung in der Zellbiologie : von Daten- zur Systemanalyse
Anmerkungen:
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Beschreibung:
The development of new experimental techniques generating higher-quality quantitative data paves the way to a predictive science in cell biology. This inevitably requires the establishment of mathematical models for cell biological processes. The aim of this thesis is to draw the way from the reduction of a complex system to its key components over the generation of a mathematical model to the systems analysis giving insights into its design principles. It is structured in three parts, corresponding to the three cornerstones systems reduction, model establishment and systems analysis. The problem of identifying key components in a very complicated system is addressed in Chapter 2. The microarray technology has set new standards in the generation of high-throughput gene expression data in cell biology within the last decade. A typical experimental setup of microarray studies is the comparison of two or more biological entities showing differences in their phenotype, like patients suffering from a disease and a control group. One goal of these setups is to detect the key components that are responsible for the evolution of these differences. In the recent years, several statistical approaches have been proposed that integrate biological prior knowledge into the analysis of microarray data by determining the regulation of functionally related groups of genes. These approaches have shown to have certain advantages compared to single gene analyses, like the better reproducibility and interpretability. Nevertheless, all of the proposed gene set approaches show some drawbacks, like for example the need of an threshhold value, the requirement of a comparison gene set, or an inaccurate formulation of the tested null hypotheses. In Chapter 2, a new gene set analysis approach is introduced that is based on the estimation of the number of differentially expressed genes in a gene set. It is based on the fact that the p-values resulting from a multiple tested valid null hypothesis follow a uniform distribution. The method ...