The circadian clock of Drosophila is a model pathway for research in biological clock mechanisms, both with traditional experimental approaches and with emerging systems biology approaches utilizing mathematical modeling and in silico computer simulation. Dynamic diurnal oscillations are achieved by the complex interaction of components as a system, and mathematical reconstruction has proven to be an invaluable means of understanding such systematic behavior. In this study, we implemented eight published models of the Drosophila circadian clock in Systems Biology Markup Language (SBML) for comparative systems biology studies using E-Cell Simulation Environment version 3, to examine the system-level requirements for the clock mechanism to be robust, by calculating the period and amplitude sensitivity coefficients with simulation experiments. While all models were generally robust as determined by the network topology of the oscillatory feedback loop structure, existing models place relatively strong emphasis on transcription regulation, although this is a limitation on robustness. We suggest that more comprehensive modeling including protein phosphorylation, polymerization, and nuclear transport with regard to amplitude sensitivity will be necessary for understanding the light entrainment and temperature compensation of circadian clocks.

BACKGROUND: Successful realization of a "systems biology" approach to analyzing cells is a grand challenge for our understanding of life. However, current modeling approaches to cell simulation are labor-intensive, manual affairs, and therefore constitute a major bottleneck in the evolution of computational cell biology. RESULTS: We developed the Genome-based Modeling (GEM) System for the purpose of automatically prototyping simulation models of cell-wide metabolic pathways from genome sequences and other public biological information. Models generated by the GEM System include an entire Escherichia coli metabolism model comprising 968 reactions of 1195 metabolites, achieving 100% coverage when compared with the KEGG database, 92.38% with the EcoCyc database, and 95.06% with iJR904 genome-scale model. CONCLUSION: The GEM System prototypes qualitative models to reduce the labor-intensive tasks required for systems biology research. Models of over 90 bacterial genomes are available at our web site.

BACKGROUND: Elucidating the dynamic behaviour of genetic regulatory networks is one of the most significant challenges in systems biology. However, conventional quantitative predictions have been limited to small networks because publicly available transcriptome data has not been extensively applied to dynamic simulation. RESULTS: We present a microarray data-based semi-kinetic (MASK) method which facilitates the prediction of regulatory dynamics of genetic networks composed of recurrently appearing network motifs with reasonable accuracy. The MASK method allows the determination of model parameters representing the contribution of regulators to transcription rate from time-series microarray data. Using a virtual regulatory network and a Saccharomyces cerevisiae ribosomal protein gene module, we confirmed that a MASK model can predict expression profiles for various conditions as accurately as a conventional kinetic model. CONCLUSION: We have demonstrated the MASK method for the construction of dynamic simulation models of genetic networks from time-series microarray data, initial mRNA copy number and first-order degradation constants of mRNA. The quantitative accuracy of the MASK models has been confirmed, and the results indicated that this method enables the prediction of quantitative dynamics in genetic networks composed of commonly used network motifs, which cover considerable fraction of the whole network.

MOTIVATION: Molecular biotechnology now makes it possible to build elaborate systems models, but the systems biology community needs information standards if models are to be shared, evaluated and developed cooperatively. RESULTS: We summarize the Systems Biology Markup Language (SBML) Level 1, a free, open, XML-based format for representing biochemical reaction networks. SBML is a software-independent language for describing models common to research in many areas of computational biology, including cell signaling pathways, metabolic pathways, gene regulation, and others. AVAILABILITY: The specification of SBML Level 1 is freely available from http://www.sbml.org/

Abstract Summary: A new version of the E-Cell simulation system, which runs on Windows as well as Linux, has been released as free software under the terms of the GNU General Public License. Availability: Downloadable from http://www.E-Cell.org

Article Free Access Share on The E-CELL project: towards integrative simulation of cellular processes Authors: Masaru Tomita Laboratory for Bioinformatics and Department of Environmental Informatics Laboratory for Bioinformatics and Department of Environmental InformaticsView Profile , Kenta Hashimoto Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Koichi Takahashi Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Yuri Matsuzaki Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Ryo Matsushima Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Katsuyuki Yugi Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Fumihiko Miyoshi Laboratory for Bioinformatics and School of Media and Governance Laboratory for Bioinformatics and School of Media and GovernanceView Profile , Hisako Nakano Laboratory for Bioinformatics and Department of Policy Management, Keio University, 5322 Endo, Fujisawa, 252-8520 Japan Laboratory for Bioinformatics and Department of Policy Management, Keio University, 5322 Endo, Fujisawa, 252-8520 JapanView Profile , Yusuke Saito Laboratory for Bioinformatics and Department of Environmental Informatics Laboratory for Bioinformatics and Department of Environmental InformaticsView Profile , S. Shimizu Laboratory for Bioinformatics and Department of Zoology, Cambridge University, CB2 3EJ, UK Laboratory for Bioinformatics and Department of Zoology, Cambridge University, CB2 3EJ, UKView Profile , Yoichi Nakayama Laboratory for Bioinformatics and Department of Environmental Informatics Laboratory for Bioinformatics and Department of Environmental InformaticsView Profile Authors Info & Claims RECOMB '00: Proceedings of the fourth annual international conference on Computational molecular biologyApril 2000 Pages 290–298https://doi.org/10.1145/332306.332567Published:08 April 2000Publication History 1citation569DownloadsMetricsTotal Citations1Total Downloads569Last 12 Months13Last 6 weeks5 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF