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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/102

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Title: The role of modelling in identifying drug targets for diseases of the cell cycle
Authors: Clyde, Robert G.
Bown, James L.
Hupp, Ted R.
Zhelev, Nikolai Z.
Crawford, John W.
Affiliation: University of Abertay Dundee. Scottish Informatics, Mathematics, Biology and Statistics Centre
Keywords: Cell cycle
Cancer
Drug discovery
Systems biology
Model
Mathematics
Issue Date: Oct-2006
Publisher: The Royal Society
Type: Journal Article
Refereed: peer-reviewed
Rights: Published version (c)The Royal Society, available from http://rsif.royalsocietypublishing.org/content/3/10/617
Citation: Clyde, R. G., et al. 2006. The role of modelling in identifying drug targets for diseases of the cell cycle. Journal of the Royal Society Interface. 3(10): pp.617-627. [Online] Available from: DOI: 10.1098/rsif.2006.0146
Abstract: The cell cycle is implicated in diseases that are the leading cause of mortality and morbidity in the developed world. Until recently, the search for drug targets has focused on relatively small parts of the regulatory network under the assumption that key events can be controlled by targeting single pathways. This is valid provided the impact of couplings to the wider scale context of the network can be ignored. The resulting depth of study has revealed many new insights; however, these have been won at the expense of breadth and a proper understanding of the consequences of links between the different parts of the network. Since it is now becoming clear that these early assumptions may not hold and successful treatments are likely to employ drugs that simultaneously target a number of different sites in the regulatory network, it is timely to redress this imbalance. However, the substantial increase in complexity presents new challenges and necessitates parallel theoretical and experimental approaches. We review the current status of theoretical models for the cell cycle in light of these new challenges. Many of the existing approaches are not sufficiently comprehensive to simultaneously incorporate the required extent of couplings. Where more appropriate levels of complexity are incorporated, the models are difficult to link directly to currently available data. Further progress requires a better integration of experiment and theory. New kinds of data are required that are quantitative, have a higher temporal resolution and that allow simultaneous quantitative comparison of the concentration of larger numbers of different proteins. More comprehensive models are required and must accommodate not only substantial uncertainties in the structure and kinetic parameters of the networks, but also high levels of ignorance. The most recent results relating network complexity to robustness of the dynamics provide clues that suggest progress is possible.
URI: http://hdl.handle.net/10373/102
ISSN: 1742-5662
Appears in Collections:SIMBIOS Collection

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