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.

UNLABELLED: A computational tool for mitochondrial systems biology has been developed as a simulation model of E-Cell2, a publicly available simulation system. The general model consists of 58 enzymatic reactions and 117 metabolites, representing the respiratory chain, the TCA cycle, the fatty acid beta-oxidation and the inner-membrane transport system. It is based on previously published enzyme kinetics studies in the literature; we have successfully integrated and packaged them into a single large model. The model can be easily extended and modified so that mitochondrial biologists/physiologists can integrate their own models and evaluate them in the context of the whole organelle metabolism. AVAILABILITY: The mitochondrial model is bundled up with E-Cell2 simulation system, which can be downloaded from http://www.e-cell.org. CD-ROMs are also available and are distributed at major conferences. SUPPLEMENTARY INFORMATION: All the kinetic data are available via 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

MOTIVATION: Genome sequencing projects and further systematic functional analyses of complete gene sets are producing an unprecedented mass of molecular information for a wide range of model organisms. This provides us with a detailed account of the cell with which we may begin to build models for simulating intracellular molecular processes to predict the dynamic behavior of living cells. Previous work in biochemical and genetic simulation has isolated well-characterized pathways for detailed analysis, but methods for building integrative models of the cell that incorporate gene regulation, metabolism and signaling have not been established. We, therefore, were motivated to develop a software environment for building such integrative models based on gene sets, and running simulations to conduct experiments in silico. RESULTS: E-CELL, a modeling and simulation environment for biochemical and genetic processes, has been developed. The E-CELL system allows a user to define functions of proteins, protein-protein interactions, protein-DNA interactions, regulation of gene expression and other features of cellular metabolism, as a set of reaction rules. E-CELL simulates cell behavior by numerically integrating the differential equations described implicitly in these reaction rules. The user can observe, through a computer display, dynamic changes in concentrations of proteins, protein complexes and other chemical compounds in the cell. Using this software, we constructed a model of a hypothetical cell with only 127 genes sufficient for transcription, translation, energy production and phospholipid synthesis. Most of the genes are taken from Mycoplasma genitalium, the organism having the smallest known chromosome, whose complete 580 kb genome sequence was determined at TIGR in 1995. We discuss future applications of the E-CELL system with special respect to genome engineering. AVAILABILITY: The E-CELL software is available upon request. SUPPLEMENTARY INFORMATION: The complete list of rules of the developed cell model with kinetic parameters can be obtained via our web site at: http://e-cell.org/.