On the use of qualitative reasoning to simulate and identify metabolic pathways

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dc.contributor.author King, Ross Donald
dc.contributor.author Garrett, Simon Martin
dc.contributor.author Coghill, George
dc.date.accessioned 2006-04-24T15:05:33Z
dc.date.available 2006-04-24T15:05:33Z
dc.date.issued 2005-01-12
dc.identifier.citation King , R D , Garrett , S M & Coghill , G 2005 , ' On the use of qualitative reasoning to simulate and identify metabolic pathways ' Bioinformatics , vol 21 , no. 9 , pp. 2017-2026 . en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 68002
dc.identifier.other dspace: 2160/126
dc.identifier.uri http://hdl.handle.net/2160/126
dc.description King, R.D., Garrett, S.M., Coghill, G.M. (2005). On the use of qualitative reasoning to simulate and identify metabolic pathways. Bioinformatics 21(9):2017-2026 RAE2008 en
dc.description.abstract Motivation: Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time) and identification (learning a model cell from observation of states). We propose the use of qualitative reasoning (QR) as a unified formalism for both tasks. The two most commonly used alternative methods of modelling biochemical pathways are ordinary differential equations (ODEs), and logical/graph-based (LG) models. Results: The QR formalism we use is an abstraction of ODEs. It enables the behaviour of many ODEs, with different functional forms and parameters, to be captured in a single QR model. QR has the advantage over LG models of explicitly including dynamics. To simulate biochemical pathways we have developed ‘enzyme’ and ‘metabolite’ QR building blocks that fit together to form models. These models are finite, directly executable, easy to interpret and robust. To identify QR models we have developed heuristic chemoinformatics graph analysis and machine learning procedures. The graph analysis procedure is a series of constraints and heuristics that limit the number of ways metabolites can combine to form pathways. The machine learning procedure is generate-and-test inductive logic programming. We illustrate the use of QR for modelling and simulation using the example of glycolysis. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof Bioinformatics en
dc.title On the use of qualitative reasoning to simulate and identify metabolic pathways en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1093/bioinformatics/bti255
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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