Previous theoretical work on DNA replication neglected how the starting points (origins) take their place and how replication time is a ected when origins fail to activate. It is however crucial that origin loci are chosen so that too large gaps between them are avoided; otherwise the time until completion of chromosome replication becomes much longer than is allowed by the cell cycle. We investigate what the optimal origin location should be depending on the likelihood of origins failing. We show analytically and numerically that there exist regimes for origins, either to be positioned together in groups spaced far away from the next, or as equally scattered single origins depending on the uncertainty when activation occurs. The model reproduces origin distributions of frog embryos which are thought to be random, and shows contrarily that grouping must occur in order to swiftly complete replication; known as the random completion problem. The model also holds when considering a circular DNA topology for archaeal genomes, as well as if applied to the whole replication pro ling data of yeast. We study how an optimal origin distribution can arise and propose a mechanism to solve the random completion problem. We show that regular spacing emerges as an inherent property of the car parking model. We introduce a spatial requirement for origins to bind to DNA; origins occupy space on the DNA and can only bind stably if there is su cient space for them. Such a model leads to a well ordered origin distribution with minimal gaps as required for on time DNA replication in frog embryos. The optimal origin distribution emerges directly from our model because origins have a higher chance to bind to large empty regions instead of small once, therefore destroying large inter origin gaps. We also introduce a model to account for the interaction of replication forks with each other which leads to their assembly into replication factories. We show using Boltzmann statistics that their assembly is stochastic. A rst model only considers two pairs of forks which we then extend to describe properties of measured experimental distributions such as fork numbers per factory during on a whole yeast genome approach. Our in silico distribution of forks per factory matches in vivo data well; which suggests that active forks encounter each other randomly for an association into replication factories.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:589508 |
| Date | January 2013 |
| Creators | Karschau, Jens |
| Publisher | University of Aberdeen |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=202763 |
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