Kai Willadsen

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Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing(More)
The advances of high-throughput sequencing offer an unprecedented opportunity to study genetic variation. This is challenged by the difficulty of resolving variant calls in repetitive DNA regions. We present a Bayesian method to estimate repeat-length variation from paired-end sequence read data. The method makes variant calls based on deviations in(More)
Complex systems techniques provide a powerful tool to study the emergent properties of networks of interacting genes. In this study we extract models of genetic regulatory networks from an artificial genome, represented by a sequence of nucleotides, and analyse how variations in the connectivity and degree of inhibition of the extracted networks affects the(More)
MOTIVATION Quantitative experimental analyses of the nuclear interior reveal a morphologically structured yet dynamic mix of membraneless compartments. Major nuclear events depend on the functional integrity and timely assembly of these intra-nuclear compartments. Yet, unknown drivers of protein mobility ensure that they are in the right place at the time(More)
The network of interacting regulatory signals within a cell comprises one of the most complex and powerful computational systems in biology. Gene regulatory networks (GRNs) play a key role in transforming the information encoded in a genome into morphological form. To achieve this feat, GRNs must respond to and integrate environmental signals with their(More)
Mapping biology into computation has both a domain specific aspect -- biological theory -- and a methodological aspect -- model development. Computational modelers have implicit knowledge that guides modeling in many ways but this knowledge is rarely communicated. We review the challenge of biological complexity and current practices in modeling genetic(More)
Long used as a framework for abstract modelling of genetic regulatory networks, the Random Boolean Network model possesses interesting robustness-related behaviour. We introduce coherency, a new measure of robustness based on a system’s state space, and defined as the probability of switching between attraction basins due to perturbation. We show that this(More)
Among repetitive genomic sequence, the class of tri-nucleotide repeats has received much attention due to their association with human diseases. Tri-nucleotide repeat diseases are caused by excessive sequence length variability; diseases such as Huntington’s disease and Fragile X syndrome are tied to an increase in the number of repeat units in a tract.(More)
This thesis focuses on characterising and understanding robustness in Boolean models of genetic regulatory systems, both in terms of abstract models — specifically the Random Boolean Network model — and in terms of models of real-world regulatory systems. More specifically, the characterisation of robustness to state perturbation is considered in terms of(More)