What is E-Cell project?

The E-Cell Project develops general technologies and theoretical supports for computational biology with the grand aim to make precise whole cell simulation at the molecular level possible.

Some of the research foci of the Project include:

  • Modeling methodologies, formalisms and techniques, including technologies to predict, obtain or estimate parameters such as reaction rates and concentrations of molecules in the cell.
  • E-Cell System, a software platform for modeling, simulation and analysis of complex, heterogeneous and multi-scale systems like the cell.
  • Numerical simulation algorithms.
  • Mathematical analysis methods.

The E-Cell Project is open to anyone who shares the view with us that development of cell simulation technology, and, even if such ultimate goal might not be within ten years of reach yet, solving various conceptual, computational and experimental problems that will continue to arise in the course of pursuing it, may have a multitude of eminent scientific, medical and engineering impacts on our society.

What is E-Cell System?

E-Cell System is a software platform for modeling, simulation and analysis of complex, heterogeneous and multi-scale systems like the cell. See projects page for more details on E-Cell System.

Who is using E-Cell System?

Research using E-Cell system

Many publications are citing Tomita et al. (1999)

View Full Listing at Google Scholar

History

Launching of the E-Cell Project — 1996

In the spring of 1996, students at the Labratory for Bioinformatics at Keio University SFC (Shonan-Fujisawa campus), started a study group devoted to investigating the molecular biology of Mycoplasma genitalium, the orgamism with the smallest known genome. Under the leadership of Prof. Masaru Tomita, we transformed this group into the E-Cell Project by the end of the year, which aims at reconstruction of a whole cell in silico. The initial ambition of the project was to create a simulation model of a hypothetical cell that contains a minimum gene set for survival based on the tiny organism M. genitalium, whose complete 580kb genome sequence had only been determined in the previous year. This led to the successful design and development of the first working version of the E-Cell System by Koichi Takahashi (see History of E-Cell System below).

Self Sustaining Cell model with 127 genes — 1997

By August and September 1997, the software had been used in a collaborative project between Keio and TIGR (The Institute for Genomic Research), which aimed at in silico reconstruction of a virtual hypothetical cell with 127 genes, based on Mycoplasma genitalium, which was designed to self-sustain by producing energy from glucose with the enzymes created from its genes. In addition to Prof. Tomita and Takahashi, Kenta Hashimoto, Thomas Shimizu, Katsuyuki Yugi, Yuri Matsuzaki, Fumihiko Miyoshi, Kanako Saito, and Sakura Tanida participated in this project, held at a temporary lab in Baltimore, MD, USA. Drs. C. Hutchson and J. C. Ventor of TIGR ultimatly co-authored papers based on the results of this work.

The self-sustaining cell with 127 genes is a hypothetical cell that contains a minimum gene set for survival. We borrowed the genomic construction from Mycoplasma genitalium to build our first virtual cell, designed to conduct what we call “minimum cellular metabolism”. This model takes up glucose from the culture medium using a phosphotransferase system, generates ATP by catabolizing glucose to lactate through glycolysis and fermentation, and exports lactate out of the cell. Enzymes and substrates are spontaneously synthesized and degraded over time to sustain ‘life’. Protein synthesis is implemented by modeling the molecules necessary for transcription and translation, namely RNA polymerase, ribosomal subunits, rRNAs, tRNAs and tRNA ligases. The cell also takes up glycerol and fatty acid, and produces phosphatidyl glycerol for membrane structure using a phospholipid biosynthesis pathway. The model cell is ‘self supporting’, but not capable of proliferating; the cell does not have pathways for DNA replication or the cell cycle.

Towards modeling real cells — 1998 –

Our next project was to model real cells and to develop a more sophisticated simulation environment for biological simulations. New modeling projects for modeling a human erythrocyte, mitochondrion, E.coli chemotaxis, and gene expression/replication were set up. Over time, the number of projects and members grew to encompass large scale modeling projects, such as E2coli, myocardial cells, neural cells, plant cells, and also mathematical analysis projets for developing methods to estimate parameters and analyzing simulation results. To reach a wider group of scientists and modelers, a Windows version of the software, E-Cell version 2, was ported by Naota Ishikawa and Mitsui Knowledge Industry, while developers at the E-Cell project concentrated on constructing a more versatile simulation environment for cell modeling, E-Cell version 3.

The Institute for Advanced Biosciences — 2001 –

Significant advances in techniques and the abundance of genome, proteome and metabolome data have had a major impact on development in the field of computational biology. The Institute for Advanced Biosciences, Keio University, was established in April 2001, consisting of several research units, including the Metabolome unit, Bioinformatics unit, and Genome Engineering unit. These research groups focus on advanced systems biology research such as proteomics, transcriptomics, metabolomics, and genome engineering, with a strong coupling with genome informatics, cell modeling, biological simulation, and computational (in silico) biology. Scientists across various different disciplines are currently working together towards the realization of cell simulation and engineering.

Growing enterprise — present

The year 2006 will be marked as the start of the next stage of evolution towards ‘E-Cell Project Version 2.0’. The E-Cell Project has been experiencing a thorough reorganization into an even more distinct focuses in the areas of technological development under the leadership of the E-Cell Project Steering Committee, which has been newly formed in the year. While the Institute for Advanced Biosciences continues to function as the headquarters of the Project, two more institutions joined the project by 2005; The Molecular Sciences Institute, Berkeley, USA, and Mitsubishi Space Software, Co. Ltd, Amagasaki Japan.

History of E-Cell System

In the October of 1996, Prof. Masaru Tomita recruited several undergraduate and graduate students in his lab to join the E-Cell Project, still in its pre-launching stage, for the development of software that can graphically represent dynamic changes in activities of genes. Those students worked in a competitive way with a variety of computational approaches. On November 15th, Koichi Takahashi, a junior undergraduate at the time, gave a presentation in which he proposed (1) the project to focus on the kinetic dynamics of metabolic pathways and the control of enzyme productions through the expressions of genes, and (2) development of a generic software system based on object-orientation in C++ language. The software was initially named ECL (Electronic Cell Laboratory), and was later given the current name ‘E-Cell System’, or ‘Electronic Cell System’, by Kenta Hashimoto, a senior in the lab. In December 29th, Takahashi made an internal release of an alpha version (version 0.0-pre) of the software to lab members. The first user of E-Cell System, who had been testing the software from the ‘pre-alpha’ days, was Riow Matsushima, a junior at the time. This 3,500-line C++ piece of software had a minimum set of functionalities necessary to model and simulate a system of enzymatic reactions in a fully object-oriented way.

The object-model of this version defined two fundamental classes called primitives, ‘Substance’ and ‘Reactor’ for representations of molecular species and reactions, respectively, and was called the ‘Substance-Reactor’ model. By the end of the March, 1997, this object-model had given another primitive class called ‘System’, to model physical and logical (or functional) compartments in the cell. Hashimoto and Thomas S. Shimizu, a master student, contributed the discussion about this ‘Structured Substance-Reactor’ object model. Hashimoto also created a class of Reactor which calculates a chemical equilibrium. By the summer of 1997, Hashimoto participated in the development of a set of classes for simulation of gene expressions, and Yasuhiro Asakawa, a master student, and Katsuyuki Yugi, a junior in that year, developed a GUI component called ‘GeneMapWindow’ to graphically represent activities and amounts of products (mRNA molecules) of many genes.

The version of the software used in the ‘camp’ in Baltimore to create the Self-Sustaining Cell model (see History of the E-Cell Project above) further developed to become the version ‘1.0beta’ with helps from many contributors. The initial public release of the version happened on March 2nd, 2000, under the E-Cell Beta-testing License. On October 13th, 2000, E-Cell 1 had been accepted by the Bioinformatics.org as an OpenSource project under the terms of GNU General Public License.

Development projects of two other versions of E-Cell System, E-Cell 2 and E-Cell 3, had started in 2000. E-Cell 2, developed by Naota Ishikawa and Mitsui Knowledge Industory, is a Windows port of E-Cell 1 which ran only on Linux operating system. E-Cell 3, initially started its development on Bioinformatics.org and later moved to Sourceforge.net in early 2003, is a complete reconstruction of E-Cell 1. E-Cell 3 can be viewed as an object-oriented computation platform on which any types of simulation algorithms can work together in any combination. In E-Cell 3, the ‘Substance-Reactor’ model of E-Cell 1 has been renamed to ‘Variable-Process’ model for further flexibility in modeling.

The E-Cell System version 4

About

The E-Cell System version 4 is a software platform for modeling, simulation and analysis of complex, heterogeneous and multi-scale systems like the cell.

See http://www.e-cell.org/ecell4/.

The E-Cell System version 3

Models

Model Reference Download
Mitochondrion A general computational model of mitochondrial metabolism in a whole organelle scale. Yugi and Tomita, 2004 E-Cell3 model
Kyoto Model (cardiac myocyte) Simulation of ATP metabolism in cardiac excitation-contraction coupling. Matsuoka et. al., 2004
Role of individual ionic current systems in the SA node hypothesized by a model study. Sarai et. al., 2003
Role of individual ionic current systems in ventricular cells hypothesized by a model study. Matsuoka et. al., 2003		 E-Cell3 model
Goldbeter1995 (Drosophila circadian rhythm) A model for circadian oscillations in the Drosophila period protein (PER). Goldbeter, 1995 E-Cell3 model
SBML
Tyson1999 (circadian rhythm) A simple model of circadian rhythms based on dimerization and proteolysis of PER and TIM. Tyson, et.al., 1999 E-Cell3 model
SBML
Scheper1999 (molecular circadian clocks) A model of molecular circadian clocks: multiple mechanisms for phase shifting and a requirement for strong nonlinear interactions. Scheper, et. al., 1999 E-Cell3 model
SBML
Leloup1999 (Drosophila circadian oscillations) Chaos and birhythmicity in a model for circadian oscillations of the PER and TIM proteins in drosophila. Leloup and Goldbeter, 1999 E-Cell3 model
Petri2001 (molecular feedback loop of D.melanogaster clock genes) Phase response curves of a molecular model oscillator: implications for mutual coupling of paired oscillators. Petri and Stengl, 2001 E-Cell3 model
SBML
Ueda2001 (Drosohila circadian rhythm) Robust oscillations whithin the interlocked feedback model of Drosophila circadian rhythm. Ueda, et. al., 2001 E-Cell3 model
Smolen2002 (Drosophila circadian oscillator) A reduced model clarifies the role of feedback loops and time delays in the Drosophila circadian oscillator. Smolen, el. al., 2002 E-Cell3 model
SBML
Smolen2004 (Drosophila circadian rhythm generation) Simulation of Drosophila circadian oscillations, mutations, and light responses by a model with VRI, PDP-1, and CLK. Smolen, et. al., 2004 E-Cell3 model
SBML

Scopyon

About

Scopyon is a Monte Carlo simulation toolkit for bioimaging systems.

See https://github.com/ecell/scopyon/.

API document is available here.

Spatiocyte

Publications

  • Shimo H, Arjunan SNV, Machiyama H, Nishino T, Suematsu M, Fujita H, Tomita M, Takahashi K: Particle Simulation of Oxidation Induced Band 3 Clustering in Human Erythrocytes. PLoS computational biology. 11:e1004210 (2015) Open_Access

  • Nishino T, Yachie-Kinoshita A, Hirayama A, Soga T, Suematsu M, Tomita M. Dynamic Simulation and Metabolome Analysis of Long-Term Erythrocyte Storage in Adenine–Guanosine Solution. PLoS One. 8(8):e71060 (2013) Open_Access

  • Okubo C, Sano HI, Naito Y, Tomita M. Contribution of quantitative changes in individual ionic current systems to the embryonic development of ventricular myocytes: a simulation study. J Physiol Sci. 63(5):355-67 (2013) Open_Access PubMed

  • Mugler A, Bailey AG, Takahashi K, ten Wolde PR. Membrane clustering and the role of rebinding in biochemical signaling. Biophys J. 102(5):1069-78 (2012) PubMed

  • Seike M, Saitou T, Kouchi Y, Ohara T, Matsuhisa M, Sakaguchi K, Tomita K, Kosugi K, Kashiwagi A, Kasuga M, Tomita M, Naito Y, Nakajima H. Computational assessment of insulin secretion and insulin sensitivity from 2-h oral glucose tolerance tests for clinical use for type 2 diabetes. J Physiol Sci 61(4):321-30 (2011) Open_Access PubMed

  • Shimo H, Nishino T, Tomita M. Predicting the Kinetic Properties Associated with Redox Imbalance after Oxidative Crisis in G6PD-Deficient Erythrocytes: A Simulation Study. Adv Hematol 2011:398945 (2011) Open_Access PubMed

  • Takahashi K, Tanase-Nicola S, ten Wolde PR. Spatio-temporal correlations can drastically change the response of a MAPK pathway. Proc Natl Acad Sci U S A 107(6):2473-8 (2010) PubMed

  • Yachie-Kinoshita A, Nishino T, Shimo H, Suematsu M, Tomita M. A metabolic model of human erythrocytes: practical application of the E-Cell Simulation Environment. J Biomed Biotechnol 2010:642420 (2010) PubMed

  • Nishino T, Yachie-Kinoshita A, Hirayama A, Soga T, Suematsu M, Tomita M. In silico modeling and metabolome analysis of long-stored erythrocytes to improve blood storage methods. J Biotechnol 144(3):212-23 (2009) PubMed

  • Helmy M, Gohda J, Inoue J, Tomita M, Tsuchiya M, Selvarajoo K. Predicting novel features of toll-like receptor 3 signaling in macrophages. PLoS One 4(3):e4661 (2009) PubMed

  • Ogawa Y, Arakawa K, Kaizu K, Miyoshi F, Nakayama Y, Tomita M. Comparative study of circadian oscillatory network models of Drosophila. Artif Life 14(1):29-48 (2008) PubMed

  • Ohno H, Naito Y, Nakajima H, Tomita M. Construction of a biological tissue model based on a single-cell model: a computer simulation of metabolic heterogeneity in the liver lobule. Artif Life 14(1):3-28 (2008) PubMed

  • Selvarajoo K, Takada Y, Gohda J, Helmy M, Akira S, Tomita M, Tsuchiya M, Inoue J, Matsuo K. Signaling flux redistribution at toll-like receptor pathway junctions. PLoS One 3(10):e3430 (2008) PubMed

  • Ishii N, Nakayama Y, Tomita M. Distinguishing enzymes using metabolome data for the hybrid dynamic/static method. Theor Biol Med Model 4:19 (2007) PubMed

  • Ishii N, Nakahigashi K, Baba T, Robert M, Soga T, Kanai A, Hirasawa T, Naba M, Hirai K, Hoque A, Ho PY, Kakazu Y, Sugawara K, Igarashi S, Harada S, Masuda T, Sugiyama N, Togashi T, Hasegawa M, Takai Y, Yugi K, Arakawa K, Iwata N, Toya Y, Nakayama Y, Nishioka T, Shimizu K, Mori H, Tomita M. Multiple high-throughput analyses monitor the response of E. coli to perturbations. Science 316(5824):593-7 (2007) PubMed

  • Kinoshita A, Tsukada K, Soga T, Hishiki T, Ueno Y, Nakayama Y, Tomita M, Suematsu M. Roles of hemoglobin Allostery in hypoxia-induced metabolic alterations in erythrocytes: simulation and its verification by metabolome analysis. J Biol Chem 282(14):10731-41 (2007) PubMed

  • Kinoshita A, Nakayama Y, Kitayama T, Tomita M. Simulation study of methemoglobin reduction in erythrocytes. Differential contributions of two pathways to tolerance to oxidative stress. FEBS J 274(6):1449-58 (2007) PubMed

  • Itoh H, Naito Y, Tomita M. Simulation of developmental changes in action potentials with ventricular cell models. Syst Synth Biol 1(1):11-23 (2007) PubMed

  • Miyoshi F, Nakayama Y, Kaizu K, Iwasaki H, Tomita M. A mathematical model for the Kai-protein-based chemical oscillator and clock gene expression rhythms in cyanobacteria. J Biol Rhythms 22(1):69-80 (2007) PubMed

  • Kitayama T, Kinoshita A, Sugimoto M, Nakayama Y, Tomita M. A simplified method for power-law modelling of metabolic pathways from time-course data and steady-state flux profiles. Theor Biol Med Model 3:24 (2006) PubMed

  • Arakawa K, Yamada Y, Shinoda K, Nakayama Y, Tomita M. GEM System: automatic prototyping of cell-wide metabolic pathway models from genomes. BMC Bioinformatics 7:168 (2006) PubMed

  • Yugi K, Nakayama Y, Kojima S, Kitayama T, Tomita M. A microarray data-based semi-kinetic method for predicting quantitative dynamics of genetic networks. BMC Bioinformatics 6:299 (2005) PubMed

  • Yugi K, Nakayama Y, Kinoshita A, Tomita M. Hybrid dynamic/static method for large-scale simulation of metabolism. Theor Biol Med Model 2:42 (2005) PubMed

  • Nakayama Y, Kinoshita A, Tomita M. Dynamic simulation of red blood cell metabolism and its application to the analysis of a pathological condition. Theor Biol Med Model 2:18 (2005) PubMed

  • Takahashi K, Arjunan SN, Tomita M. Space in systems biology of signaling pathways–towards intracellular molecular crowding in silico. FEBS Lett 579(8):1783-8 (2005) PubMed

  • Ishii N, Robert M, Nakayama Y, Kanai A, Tomita M. Toward large-scale modeling of the microbial cell for computer simulation. J Biotechnol 113(1-3):281-94 (2004) PubMed

  • Yugi K, Tomita M. A general computational model of mitochondrial metabolism in a whole organelle scale. Bioinformatics 20(4):1795-6 (2004) PubMed Supplement

  • Takahashi K, Kaizu K, Hu B, Tomita M. A multi-algorithm, multi-timescale method for cell simulation. Bioinformatics 20(4):538-46 (2004) PubMed\

    Supplement

  • Takahashi K, Ishikawa N, Sadamoto Y, Sasamoto H, Ohta S, Shiozawa A, Miyoshi F, Naito Y, Nakayama Y, Tomita M. E-Cell 2: multi-platform E-Cell simulation system. Bioinformatics 19(13):1727-9 (2003) PubMed

  • Hucka M, Finney A, Sauro HM, Bolouri H, Doyle JC, Kitano H, Arkin AP, Bornstein BJ, Bray D, Cornish-Bowden A, Cuellar AA, Dronov S, Gilles ED, Ginkel M, Gor V, Goryanin II, Hedley WJ, Hodgman TC, Hofmeyr JH, Hunter PJ, Juty NS, Kasberger JL, Kremling A, Kummer U, Le Novère N, Loew LM, Lucio D, Mendes P, Minch E, Mjolsness ED, Nakayama Y, Nelson MR, Nielsen PF, Sakurada T, Schaff JC, Shapiro BE, Shimizu TS, Spence HD, Stelling J, Takahashi K, Tomita M, Wagner J, Wang J; SBML Forum. The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19(4):524-31 (2003) PubMed

  • Tomita M, Hashimoto K, Takahashi K, Shimizu TS, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter JC, Hutchison CA 3rd. E-CELL: software environment for whole-cell simulation. Bioinformatics 15(1):72-84 (1999) PubMed

  • Tomita M, Hashimoto K, Takahashi K, Shimizu T, Matsuzaki Y, Miyoshi F, Saito K, Tanida S, Yugi K, Venter JC, Hutchison CA. E-CELL: Software Environment for Whole Cell Simulation. Genome Inform Ser Workshop Genome Inform 8:147-155 (1997) PubMed

Members

Laboratories

  • Koichi Takahashi Lab. (Laboratory for Biologically Inspired Computing, RIKEN BDR)
  • Yasuhiro Naito Lab. (SFC, Keio Univ.)
  • Masaru Tomita Lab. (SFC, Keio Univ.)

Developer team

Leadership

  • mtomita Masaru Tomita
  • ktakahashi Koichi Takahashi

Core developers

  • Kazunari Kaizu
  • Kozo Nishida
  • Masaki Watabe
  • Toru Niina
  • Suguru Kato

Past Developers, Directors, and Funding Agencies

  • Satya Arjunan
  • Yuki Sakamoto
  • Naoki Nishida
  • Kazunari Iwamoto
  • Moriyoshi Koizumi
  • Yasuhiro Naito
  • Hitomi Sano
  • Naohiro Aota
  • Takeshi Sakurada

Conference

Support for the E-Cell Project

The E-Cell Project is made possible through the financial and institutional support of the following institutions.

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