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Peter Li,
Joseph Dada,
Daniel Jameson,
Irena Spasic,
Neil Swainston,
Kathleen Carroll,
Warwick Dunn, Farid Khan,
Naglis Malys,
Hanan Messiha, [......],
Dieter Weichart,
Catherine Winder,
Jill Wishart,
David Broomhead,
Carole Goble,
Simon Gaskell,
Douglas Kell,
Hans Westerhoff,
Pedro Mendes,
Norman Paton
[show abstract]
[hide abstract]
ABSTRACT: Abstract Background The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources. Results Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis. Conclusions Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.
BMC Bioinformatics. 01/2010;
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Peter Li,
Joseph O Dada,
Daniel Jameson,
Irena Spasic,
Neil Swainston,
Kathleen Carroll,
Warwick Dunn, Farid Khan,
Naglis Malys,
Hanan L Messiha, [......],
Dieter Weichart,
Catherine Winder,
Jill Wishart,
David S Broomhead,
Carole A Goble,
Simon J Gaskell,
Douglas B Kell,
Hans V Westerhoff,
Pedro Mendes,
Norman W Paton
[show abstract]
[hide abstract]
ABSTRACT: The behaviour of biological systems can be deduced from their mathematical models. However, multiple sources of data in diverse forms are required in the construction of a model in order to define its components and their biochemical reactions, and corresponding parameters. Automating the assembly and use of systems biology models is dependent upon data integration processes involving the interoperation of data and analytical resources.
Taverna workflows have been developed for the automated assembly of quantitative parameterised metabolic networks in the Systems Biology Markup Language (SBML). A SBML model is built in a systematic fashion by the workflows which starts with the construction of a qualitative network using data from a MIRIAM-compliant genome-scale model of yeast metabolism. This is followed by parameterisation of the SBML model with experimental data from two repositories, the SABIO-RK enzyme kinetics database and a database of quantitative experimental results. The models are then calibrated and simulated in workflows that call out to COPASIWS, the web service interface to the COPASI software application for analysing biochemical networks. These systems biology workflows were evaluated for their ability to construct a parameterised model of yeast glycolysis.
Distributed information about metabolic reactions that have been described to MIRIAM standards enables the automated assembly of quantitative systems biology models of metabolic networks based on user-defined criteria. Such data integration processes can be implemented as Taverna workflows to provide a rapid overview of the components and their relationships within a biochemical system.
BMC Bioinformatics 01/2010; 11:582. · 2.75 Impact Factor
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Chemical Reviews 08/2009; 109(9):4025-53. · 40.20 Impact Factor
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[show abstract]
[hide abstract]
ABSTRACT: Mapping the landscape of possible macromolecular polymer sequences to their fitness in performing biological functions is a challenge across the biosciences. A paradigm is the case of aptamers, nucleic acids that can be selected to bind particular target molecules. We have characterized the sequence-fitness landscape for aptamers binding allophycocyanin (APC) protein via a novel Closed Loop Aptameric Directed Evolution (CLADE) approach. In contrast to the conventional SELEX methodology, selection and mutation of aptamer sequences was carried out in silico, with explicit fitness assays for 44,131 aptamers of known sequence using DNA microarrays in vitro. We capture the landscape using a predictive machine learning model linking sequence features and function and validate this model using 5500 entirely separate test sequences, which give a very high observed versus predicted correlation of 0.87. This approach reveals a complex sequence-fitness mapping, and hypotheses for the physical basis of aptameric binding; it also enables rapid design of novel aptamers with desired binding properties. We demonstrate an extension to the approach by incorporating prior knowledge into CLADE, resulting in some of the tightest binding sequences.
Nucleic Acids Research 12/2008; 37(1):e6. · 8.03 Impact Factor
