Search

Your search keyword '"Kell, D.B."' showing total 84 results
Start Over Author "Kell, D.B."
84 results on '"Kell, D.B."'

5. Oscillations in NF-κB signaling control the dynamics of gene expression

Author

Nelson, D.E., Ihekwaba, A.E.C., Elliott, M., Jonhson, J.R., Gibney, C.A., Foreman, B.E., Nelson, G., See, V., Horton, C.A., Spiller, D.G., Edwards, S.W., McDowell, H.P., Unitt, J.F., Sullivan, E., Grimley, R., Benson, N., Broomhead, D., Kell, D.B., and White, M.R.H.

Subjects
Genetic regulation -- Physiological aspects -- Research, Science and technology, Physiological aspects, Research
Abstract

Signaling by the transcription factor nuclear factor kappa B (NF-κB) involves its release from inhibitor kappa B (IκB) in the cytosol, followed by translocation into the nucleus. NF-κB regulation of IκBα transcription represents a delayed negative feedback loop that drives oscillations in NF-ΚB translocation. Single-cell time-lapse imaging and computational modeling of NF-ΚB (RelA) localization showed asynchronous oscillations following cell stimulation that decreased in frequency with increased IκBα transcription. Transcription of target genes depended on oscillation persistence, involving cycles of RelA phosphorylation and dephosphorylation. The functional consequences of NFκB signaling may thus depend on number, period, and amplitude of oscillations., NF-κB is a family of dimeric transcription factors (usually RelA/p65:p50) that regulates cell division, apoptosis, and inflammation (1). NF-κB dimers are sequestered in the cytoplasm of unstimulated cells by binding [...]

Published
2004

6. A consensus yeast metabolic network obtained from a community approach to systems biology

Author

Herrgard, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Bluthgen, N., Borger, S., Costenoble, E.R., Heinemann, M., Hucka, M., Li, P., Liebermeister, W., Mo, M.L., Oliveira, A.P., Petranovic, D., Pettifer, S., Simeonides, E., Smallbone, K., Spasi, I., Weichart, D., Brent, R., Broomhead, D.S., Westerhoff, H.V., Kirdar, B., Penttila, M., Klipp, E., Paton, N., Palsson, B.O., Sauer, U., Oliver, S.G., Mendes, P., Nielsen, J., Kell, D.B., and Molecular Cell Physiology

Subjects
ComputingMethodologies_PATTERNRECOGNITION
Abstract

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms. © 2008 Nature Publishing Group.

Published
2008
Full Text
View/download PDF

8. Enzyme kinetics informatics: from instrument to browser

Author

Swainston, N., Golebiewski,N., Messiha, H.L., Malys, N., Kania, R., Kengne, S., Krebs, O., Mir, S., Sauer-Danzwith, H., Smallbone, K., Weidemann, A., Wittig, U., Kell, D.B., Mendes, P., Müller, W., Paton, N.W., Rojas, I.

Abstract

A limited number of publicly available resources provide access to enzyme kinetic parameters. These have been compiled through manual data mining of published papers, not from the original, raw experimental data from which the parameters were calculated. This is largely due to the lack of software or standards to support the capture, analysis, storage and dissemination of such experimental data. Introduced here is an integrative system to manage experimental enzyme kinetics data from instrument to browser. The approach is based on two interrelated databases: the existing SABIO-RK database, containing kinetic data and corresponding metadata, and the newly introduced experimental raw data repository, MeMo-RK. Both systems are publicly available by web browser and web service interfaces and are configurable to ensure privacy of unpublished data. Users of this system are provided with the ability to view both kinetic parameters and the experimental raw data from which they are calculated, providing increased confidence in the data. A data analysis and submission tool, the kineticswizard, has been developed to allow the experimentalist to perform data collection, analysis and submission to both data resources. The system is designed to be extensible, allowing integration with other manufacturer instruments covering a range of analytical techniques.

Published
2010

10. Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes

Author

Bryant, C.H., Muggleton, S.H., Oliver, S.G., Kell, D.B., Reiser, P., and King, R.D.

Subjects
Datavetenskap (datalogi), Computer Sciences
Abstract

We aim to partially automate some aspects of scientific work, namely the processes of forming hypotheses, devising trials to discriminate between these competing hypotheses, physically performing these trials and then using the results of these trials to converge upon an accurate hypothesis. We have developed ASE-Progol, an Active Learning system which uses Inductive Logic Programming to construct hypothesised first-order theories and uses a CART-like algorithm to select trials for eliminating ILP derived hypotheses. We have developed a novel form of learning curve, which in contrast to the form of learning curve normally used in Active Learning, allows one to compare the costs incurred by different leaning strategies.We plan to combine ASE-Progol with a standard laboratory robot to create a general automated approach to Functional Genomics. As a first step towards this goal, we are using ASE-Progol to rediscover how genes participate in the aromatic amino acid pathway of Saccharomyces cerevisiae. Our approach involves auxotrophic mutant trials. To date, ASE-Progol has conducted such trials in silico. However we describe how they will be performed automatically in vitro by a standard laboratory robot designed for these sorts of liquid handling tasks, namely the Beckman/Coulter Biomek 2000. Although our work to date has been limited to trials conducted in silico, the results have been encouraging. Parts of the model were removed and the ability of ASE-Progol to efficiently recover the performance of the model was measured. The cost of the chemicals consumed in converging upon a hypothesis with an accuracy in the range 46-88% was reduced if trials were selected by ASE-Progol rather than if they were sampled at random (without replacement). To reach an accuracy in the range 46-80%, ASE-Progol incurs five orders of magnitude less experimental costs than random sampling. ASE-Progol requires less time to converge upon a hypothesis with an accuracy in the range 74-87% than if trials are sampled at random (without replacement) or selected using the naive strategy of always choosing the cheapest trial from the set of candidate trials. For example to reach an accuracy of 80%, ASE-Progol requires 4 days while random sampling requires 6 days and the naive strategy requires 10 days.

Published
2001

12. Systematic integration of experimental data and models in systems biology.

Author

Li, P., Dada, J.O., Jameson, D., Spasic, I., Swainston, N., Carroll, K., Dunn, W., Khan, F., Messiha, E., Simeonides, E., Weichart, D., Winder, C., Broomhead, D.S., Goble, C.A., Gaskell, S.J., Kell, D.B., Westerhoff, H.V., Mendes, P., Paton, N.W., Li, P., Dada, J.O., Jameson, D., Spasic, I., Swainston, N., Carroll, K., Dunn, W., Khan, F., Messiha, E., Simeonides, E., Weichart, D., Winder, C., Broomhead, D.S., Goble, C.A., Gaskell, S.J., Kell, D.B., Westerhoff, H.V., Mendes, P., and Paton, N.W.

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. © 2010 Li et al; licensee BioMed Central Ltd.

Published
2010
Full Text
View/download PDF

14. A proposed framework for the description of plant metabolomics experiments and their results

Author

Jenkins, H., Hardy, N., Beckmann, M, Draper, J., Smith, A.R., Taylor, J., Fiehn, O., Goodacre, R., Bino, R.J., Hall, R., Kopka, J., Lane, G.A., Lange, B.M., Liu, J.R., Mendes, P., Nikolau, B.J., Oliver, S.G., Paton, N.W., Rhee, S., Roessner-Tunali, U., Saito, K., Smedsgaard, Jørn, Sumner, L.W., Wang, T., Walsh, S., Wurtele, E.S., Kell, D.B., Jenkins, H., Hardy, N., Beckmann, M, Draper, J., Smith, A.R., Taylor, J., Fiehn, O., Goodacre, R., Bino, R.J., Hall, R., Kopka, J., Lane, G.A., Lange, B.M., Liu, J.R., Mendes, P., Nikolau, B.J., Oliver, S.G., Paton, N.W., Rhee, S., Roessner-Tunali, U., Saito, K., Smedsgaard, Jørn, Sumner, L.W., Wang, T., Walsh, S., Wurtele, E.S., and Kell, D.B.

Abstract

The study of the metabolite complement of biological samples, known as metabolomics, is creating large amounts of data, and support for handling these data sets is required to facilitate meaningful analyses that will answer biological questions. We present a data model for plant metabolomics known as ArMet (architecture for metabolomics). It encompasses the entire experimental time line from experiment definition and description of biological source material, through sample growth and preparation to the results of chemical analysis. Such formal data descriptions, which specify the full experimental context, enable principled comparison of data sets, allow proper interpretation of experimental results, permit the repetition of experiments and provide a basis for the design of systems for data storage and transmission. The current design and example implementations are freely available (http://www.armet.org/). We seek to advance discussion and community adoption of a standard for metabolomics, which would promote principled collection, storage and transmission of experiment data.

Published
2004

15. Quantitative approaches to the analysis of the control and regulation of microbial metabolism

Author

Westerhoff, H.V., Van Heeswijk, W., Kahn, D., Kell, D.B., Bioinformatique, phylogénie et génomique évolutive (BPGE), Département PEGASE [LBBE] (PEGASE), Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), and Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT]
Published
1991

27. Multiobjective Optimization in Bioinformatics and Computational Biology.

Author

Handl, J., Kell, D.B., and Knowles, J.

Abstract

This paper reviews the application of multiobjective optimization in the fields of bioinformatics and computational biology. A survey of existing work, organized by application area, forms the main body of the review, following an introduction to the key concepts in multiobjective optimization. An original contribution of the review is the identification of five distinct "contexts," giving rise to multiple objectives: These are used to explain the reasons behind the use of multiobjective optimization in each application area and also to point the way to potential future uses of the technique [ABSTRACT FROM PUBLISHER]

Published
2007
Full Text
View/download PDF

38. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology

Author

Herrgård, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novère, N., Li, P, Liebermeister, W., Mo, M.L., Oliveira, A.P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasić, I., Weichart, D., Brent, R., Broomhead, D.S., Westerhoff, H.V., Kırdar, B.I., Penttilä, M., Klipp, E., Palsson, B.Ø., Sauer, U., Oliver, S.G., Mendes, P., Nielsen, J., Kell, D.B., Herrgård, M.J., Swainston, N., Dobson, P., Dunn, W.B., Arga, K.Y., Arvas, M., Blüthgen, N., Borger, S., Costenoble, R., Heinemann, M., Hucka, M., Le Novère, N., Li, P, Liebermeister, W., Mo, M.L., Oliveira, A.P., Petranovic, D., Pettifer, S., Simeonidis, E., Smallbone, K., Spasić, I., Weichart, D., Brent, R., Broomhead, D.S., Westerhoff, H.V., Kırdar, B.I., Penttilä, M., Klipp, E., Palsson, B.Ø., Sauer, U., Oliver, S.G., Mendes, P., Nielsen, J., and Kell, D.B.

Abstract

Genomic data allow the large-scale manual or semi-automated assembly of metabolic network reconstructions, which provide highly curated organism-specific knowledge bases. Although several genome-scale network reconstructions describe Saccharomyces cerevisiae metabolism, they differ in scope and content, and use different terminologies to describe the same chemical entities. This makes comparisons between them difficult and underscores the desirability of a consolidated metabolic network that collects and formalizes the 'community knowledge' of yeast metabolism. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. In drafting it, we placed special emphasis on referencing molecules to persistent databases or using database-independent forms, such as SMILES or InChI strings, as this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language (http://www.comp-sys-bio.org/yeastnet). It can be maintained as a resource that serves as a common denominator for studying the systems biology of yeast. Similar strategies should benefit communities studying genome-scale metabolic networks of other organisms.

39. Something from nothing: bridging the gap between constraint-based and kinetic modelling

Author

Smallbone, K., Simeonidis, E., Broomhead, D.S., Kell, D.B., Smallbone, K., Simeonidis, E., Broomhead, D.S., and Kell, D.B.

Abstract

Two divergent modelling methodologies have been adopted to increase our understanding of metabolism and its regulation. Constraint-based modelling highlights the optimal path through a stoichiometric network within certain physicochemical constraints. Such an approach requires minimal biological data to make quantitative inferences about network behaviour; however, constraint-based modelling is unable to give an insight into cellular substrate concentrations. In contrast, kinetic modelling aims to characterize fully the mechanics of each enzymatic reaction. This approach suffers because parameterizing mechanistic models is both costly and time-consuming. In this paper, we outline a method for developing a kinetic model for a metabolic network, based solely on the knowledge of reaction stoichiometries. Fluxes through the system, estimated by flux balance analysis, are allowed to vary dynamically according to linlog kinetics. Elasticities are estimated from stoichiometric considerations. When compared to a popular branched model of yeast glycolysis, we observe an excellent agreement between the real and approximate models, despite the absence of (and indeed the requirement for) experimental data for kinetic constants. Moreover, using this particular methodology affords us analytical forms for steady state determination, stability analyses and studies of dynamical behaviour.

49. Formation and resuscitation of 'non-culturable' cells of Rhodococcus rhodochrous and Mycobacterium tuberculosis in prolonged stationary phase.

Author

Shleeva, M.O., Bagramyan, K., Telkov, M.V., Mukamolova, G.V., Young, M., Kell, D.B., and Kaprelyants, A.S.

Subjects
*MYCOBACTERIUM tuberculosis, *BACTERIAL cell walls
Abstract

Investigates the formation and resuscitation of Rhodococcus rhodochrous and Mycobacterium tuberculosis in stationary phase. Mechanism of latency of tuberculosis; Occurrence of cell wall thickening in the Mycobacterium; Growth of non-culturable cells of R. rhodochrous.

Published
2002

Catalog

Books, media, physical & digital resources
See catalog results