Web-based kinetic modelling using JWS Online

Olivier, Brett G.; Snoep, Jacky L.

doi:10.1093/bioinformatics/bth200

Cited by 302 publications

(201 citation statements)

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“…The silicon cell-JWS Online approach [30,38], inspired by the erythrocyte glycolysis model of the Berlin group (for a recent version see [22], which is available on JWS; JWS stands for JJava Web Simulation), accepts this complexity and integrates the differential equations numerically. In addition in some cases allosteric effectors may be active in vivo, or enzymes may be modified covalently, which may not have been taken into account in the in vitro assays, causing a difference between the situation in vitro and that in vivo.…”

Section: Integration Of the Precise Rate Equations; Silicon Cell And mentioning

confidence: 99%

Systems biology towards life in silico: mathematics of the control of living cells

et al. 2008

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Systems Biology is the science that aims to understand how biological function absent from macromolecules in isolation, arises when they are components of their system. Dedicated to the memory of Reinhart Heinrich, this paper discusses the origin and evolution of the new part of systems biology that relates to met- abolic and signal-transduction pathways and extends mathematical biology so as to address postgenomic experimental reality. Various approaches to modeling the dynamics generated by metabolic and signal-transduction pathways are compared. The silicon cell approach aims to describe the intracellular network of interest precisely, by numerically integrating the precise rate equations that characterize the ways macromolecules' interact with each other. The non-equilibrium thermodynamic or 'lin-log' approach approximates the enzyme rate equations in terms of linear functions of the logarithms of the concentrations. Biochemical Systems Analysis approximates in terms of power laws. Importantly all these approaches link system behavior to molecular interaction properties. The latter two do this less precisely but enable analytical solutions. By limiting the questions asked, to optimal flux patterns, or to control of fluxes and concentrations around the (patho)physiological state, Flux Balance Analysis and Metabolic/Hierarchical Control Analysis again enable analytical solutions. Both the silicon cell approach and Metabolic/Hierarchical Control Analysis are able to highlight where and how system function derives from molecular interactions. The latter approach has also discovered a set of fundamental principles underlying the control of biological systems. The new law that relates concentration control to control by time is illustrated for an important signal transduction pathway, i.e. nuclear hormone receptor signaling such as relevant to bone formation. It is envisaged that there is much more Mathematical Biology to be discovered in the area between molecules and Life.

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Section: Integration Of the Precise Rate Equations; Silicon Cell And mentioning

confidence: 99%

Systems biology towards life in silico: mathematics of the control of living cells

et al. 2008

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“…Their values were determined in Hynne et al (2001) at the onset of glycolytic oscillations. Model equations and parameter values were taken from the JWS online model database (Olivier and Snoep, 2004). (Fig.…”

Section: Glycolysis Modelmentioning

confidence: 99%

Response to temporal parameter fluctuations in biochemical networks

Liebermeister

2005

Journal of Theoretical Biology

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Metabolic response coefficients describe how variables in metabolic systems, like steady state concentrations, respond to small changes of kinetic parameters. To extend this concept to temporal parameter fluctuations, we define spectral response coefficients that relate Fourier components of concentrations and fluxes to Fourier components of the underlying parameters. It is also straightforward to generalize other concepts from metabolic control theory, such as control coefficients with their summation and connectivity theorems. The first-order response coefficients describe forced oscillations caused by small harmonic oscillations of single parameters: they depend on the driving frequency and comprise the phases and amplitudes of the concentrations and fluxes. Close to a Hopf bifurcation, resonance can occur: as an example, we study the spectral densities of concentration fluctuations arising from the stochastic nature of chemical reactions. Second-order response coefficients describe how perturbations of different frequencies interact by mode coupling, yielding higher harmonics in the metabolic response. The temporal response to small parameter fluctuations can be computed by Fourier synthesis. For a model of glycolysis, this approximation remains fairly accurate even for large relative fluctuations of the parameters. r

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“…JWS Online is a Web-based repository for curated mathematical models of biological systems (Olivier and Snoep, 2004). It supports construction, simulation, and exchange of models in standard formats.…”

Section: Introductionmentioning

confidence: 99%

The JWS online simulation database

et al. 2017

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Summary JWS Online is a web-based platform for construction, simulation and exchange of models in standard formats. We have extended the platform with a database for curated simulation experiments that can be accessed directly via a URL, allowing one-click reproduction of published results. Users can modify the simulation experiments and export them in standard formats. The Simulation database thus lowers the bar on exploring computational models, helps users create valid simulation descriptions and improves the reproducibility of published simulation experiments. Availability and Implementation The Simulation Database is available on line at https://jjj.bio.vu.nl/models/experiments/.

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Web-based kinetic modelling using JWS Online

Abstract: The JWS Online website is publicly accessible at http://jjj.biochem.sun.ac.za/ with mirrors at http://www.jjj.bio.vu.nl/ and http://jjj.vbi.vt.edu/

Cited by 302 publications

References 2 publications

Systems biology towards life in silico: mathematics of the control of living cells

Systems biology towards life in silico: mathematics of the control of living cells

Response to temporal parameter fluctuations in biochemical networks

The JWS online simulation database

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