E-CELL: software environment for whole-cell simulation. 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/.

References in zbMATH (referenced in 11 articles )

Showing results 1 to 11 of 11.
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  1. Chen, Ming; Hariharaputran, Sridhar; Hofestädt, Ralf; Kormeier, Benjamin: Petri net models for the semi-automatic construction of large scale biological networks (2011)
  2. Aleman-Meza, Boanerges; Yu, Yihai; Schüttler, Heinz-Bernd; Arnold, Jonathan; Taha, Thiab R.: KINSOLVER: A simulator for computing large ensembles of biochemical and gene regulatory networks (2009)
  3. Iba, Hitoshi: Inference of differential equation models by genetic programming (2008)
  4. Webb, Ken; White, Tony: Cell modeling with reusable agent-based formalisms (2006)
  5. Popova-Zeugmann, Louchka; Heiner, Monika; Koch, Ina: Time Petri nets for modelling and analysis of biochemical networks (2005)
  6. Heiner, Monika; Koch, Ina: Petri net based model validation in systems biology (2004)
  7. Timmer, J.; Müller, T.G.; Swameye, I.; Sandra, O.; Klingmüller, U.: Modeling the nonlinear dynamics of cellular signal transduction. (2004)
  8. Aluffi-Pentini, F.; De Fonzo, V.; Parisi, V.: A Novel algorithm for the numerical integration of systems of ordinary differential equations arising in chemical problems (2003)
  9. Kikuchi, Shinichi; Fujimoto, Kenji; Kitagawa, Noriyuki; Fuchikawa, Taro; Abe, Michiko; Oka, Kotaro; Takei, Kohtaro; Tomita, Masaru: Kinetic simulation of signal transduction system in hippocampal long-term potentiation with dynamic modeling of protein phosphatase 2A. (2003)
  10. Weitzke, Elizabeth L.; Ortoleva, Peter J.: Simulating cellular dynamics through a coupled transcription, translation, metabolic model. (2003)
  11. Kitano, Hiroaki: Perspectives on systems biology. (2000)

Further publications can be found at: http://www.e-cell.org/publications/