Gene Regulatory Networks: Dynamics and Stability
Life as we know it is based on cells that use proteins and RNA to carry out metabolism, self-replication, and other essential tasks. The genes that code for these molecules are encoded in DNA, and through the processes of transcription and translation the cell expresses its genes. Some proteins are transcription factors that regulate the transcription rate of genes, so genes interact and form a gene regulatory network. In a random Boolean network the genes are modeled as being either on or off, and the regulatory interactions are drawn from some ensemble that may be based on biological observations. Here, the average behavior of observables of dynamics (e.g., attractor count) and stability (e.g., robustness to perturbations) is studied, both in the original Kauffman model and in models based on data from yeast. Signal transduction, the propagation of information about the external and internal environment of the cell, often affects transcription factors, thereby altering gene expression levels. Signaling pathway profiling is proposed as a way to reduce the complexity of microarray data and find biologically relevant signals. The core regulatory system of embryonic stem cells is a concrete example of a network where attractor basins and stability are important for biological function, and we explore its dynamics in a continuous model. Finally, the what effect transcriptional regulation has on fitness is studied in the context of metabolism in a very simple system, and the benefit of regulation is made clear.
Source Type:Doctoral Dissertation
Keywords:NATURAL SCIENCES; Biology; NATURAL SCIENCES; Physics; control; systems; Datalogi; numerisk analys; system; kontroll; Computer science; numerical analysis; Fysik; biomathematics biometrics; Bioinformatik; medicinsk informatik; biomatematik; Physics; medical informatics; transcriptional regulation; gene regulatory networks; Bioinformatics; signaling pathway profiling; stem cell regulation; metabolic pathway; fitness; nested canalyzing rules; Kauffman networks; random Boolean networks
Date of Publication:01/01/2007