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

Tomita, Masaru; Hashimoto, Kazuaki; Takahashi, Koichi; Shimizu, Thomas S.; Matsuzaki, Yasushi; Miyoshi, Fumito; Saito, Kanako; Tanida, Sakura; Yugi, Katsuyuki; Venter, J. Craig; Hutchison, Clyde A.

doi:10.1093/bioinformatics/15.1.72

Cited by 648 publications

(344 citation statements)

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“…However, there are very few dynamic models developed, which include treatment of genes. One example is the cell simulation model, known as E-cell (Tomita et al 1999), an ongoing project at Keio University in Japan. It is a modelling and simulation environment for biochemical and genetic processes and its software package allows for a user-defined model of a single cell.…”

Section: Modellingmentioning

confidence: 99%

Modelling of Mammalian Cells and Cell Culture Processes

Sidoli¹,

Mantalaris

Asprey³

2004

Cytotechnology

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Mammalian cell cultures represent the major source for a number of very high-value biopharmaceutical products, including monoclonal antibodies (MAbs), viral vaccines, and hormones. These products are produced in relatively small quantities due to the highly specialised culture conditions and their susceptibility to either reduced productivity or cell death as a result of slight deviations in the culture conditions. The use of mathematical relationships to characterise distinct parts of the physiological behaviour of mammalian cells and the systematic integration of this information into a coherent, predictive model, which can be used for simulation, optimisation, and control purposes would contribute to efforts to increase productivity and control product quality. Models can also aid in the understanding and elucidation of underlying mechanisms and highlight the lack of accuracy or descriptive ability in parts of the model where experimental and simulated data cannot be reconciled. This paper reviews developments in the modelling of mammalian cell cultures in the last decade and proposes a future direction -the incorporation of genomic, proteomic, and metabolomic data, taking advantage of recent developments in these disciplines and thus improving model fidelity. Furthermore, with mammalian cell technology dependent on experiments for information, model-based experiment design is formally introduced, which when applied can result in the acquisition of more informative data from fewer experiments. This represents only part of a broader framework for model building and validation, which consists of three distinct stages: theoretical model assessment, model discrimination, and model precision, which provides a systematic strategy from assessing the identifiability and distinguishability of a set of competing models to improving the parameter precision of a final validated model. NomenclatureC ij -component i concentration in jth model compartment; R ijk -transport rate of component i into or out of compartment j from the kth source or sink compartment; N in -number of source compartments; N outnumber of sink compartments; r ijl -reaction rate of component i in compartment j in the lth i-generating or i-consuming reaction; N gen -number of reactions generating component i; N con -number of reactions consuming component i; -specific growth rate; app max -apparent maximum specific growth rate; K dspecific death rate; N v -viable cell number; N t -total cell number; D -dilution rate; -parameter; 0 -parameter; -parameter; m -cell mass; m 0 -mass of a dividing cell; t -time; N -cell number; r -single cell growth rate; S -nutrient concentration; s -nutrient concentration vector; À -cell transition rate; ppartition probability density function; Y -yield coefficient; f (m) -the division probability density function; B -coefficient for the beta distribution incorporating the gamma function; x max -vector of maximum XPS 120742 (CYTO) -ms-code CYTO853 -product element 5254543 -23 September 2004 -Newgen

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Section: Modellingmentioning

confidence: 99%

Modelling of Mammalian Cells and Cell Culture Processes

Sidoli¹,

Mantalaris

Asprey³

2004

Cytotechnology

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“…Related work in cell modeling and simulation may be placed in three broad categories, 1) simulation software systems that model the internal workings of a single cell (Tomita et al, 1999), such as cell proliferation and apoptosis, deformation and adhesion, 2) 2-D grid-based models based on cellular automata, ordinary differential equations, or a combination of the two (Graner and Glazier, 1992), and 3) a hybrid representation of cells as discrete units that move in a continuous space (Fleischer and Barr, 1994). The grid-based approach has been used to simulate cell sorting, the morphogenesis of numerous cell-level biological structures, and tumor development.…”

Section: Previous Workmentioning

confidence: 99%

“…These models analyze changes in expression levels of proteins and other chemical compounds within a cell (Tomita et al, 1999) to predict cellular behavior such as growth, signaling, proliferation, differentiation and death (Christopher et al, 2004;Slepchenko et al, 2003). These approaches have been used to simulate several intracellular processes such as energy production and phospholipid synthesis (Tomita et al, 1999), and to simulate intracellular calcium dynamics and nucleocytoplasmic transport (Slepchenko et al, 2003) as well as intercellular processes like bacterial chemotaxis (Emonet et al, 2005). A more detailed approach to modeling and simulation of gene-gene, geneprotein and protein-protein interactions focuses on sporulation, metabolismic activity, signaling, sensing and apoptosis (Bio-SPICE, 2008), while a more general approach to cell simulation provides a modular meta-algorithm that can incorporate any time-driven simulation algorithm (Takahashi et al, 2004).…”

Section: Previous Workmentioning

confidence: 99%

A computational model of chemotaxis-based cell aggregation

et al. 2008

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We present a computational model that successfully captures the cell behaviors that play important roles in 2-D cell aggregation. A virtual cell in our model is designed as an independent, discrete unit with a set of parameters and actions. Each cell is defined by its location, size, rates of chemoattractant emission and response, age, life cycle stage, proliferation rate and number of attached cells. All cells are capable of emitting and sensing a chemoattractant chemical, moving, attaching to other cells, dividing, aging and dying. We validated and fine-tuned our in silico model by comparing simulated 24-hour aggregation experiments with data derived from in vitro experiments using PC12 pheochromocytoma cells. Quantitative comparisons of the aggregate size distributions from the two experiments are produced using the Earth Mover's Distance (EMD) metric. We compared the two size distributions produced after 24 hours of in vitro cell aggregation and the corresponding computer simulated process. Iteratively modifying the model's parameter values and measuring the difference between the in silico and and in vitro results allow us to determine the optimal values that produce simulated aggregation outcomes closely matched to the PC12 experiments. Simulation results demonstrate the ability of the model to recreate large-scale aggregation behaviors seen in live cell experiments.

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“…Because Gillespie's exact stochastic simulation algorithm (Gillespie 1976) limits the reactants of a reaction to less than 3, and in our model the reactions for R to T state conversion of DnaK takes 3 reactants, we could use only ordinary differential equations to simulate the model. E-CELL v3, an open-source computer software package developed at Keio University for large-scale cellular event simulation (www.e-cell.org) (Tomita et al 1999;Takahashi et al 2004), was used to do all the simulations. Table 2 Table 2 Temperature dependence of reaction rates.…”

Section: Simulationmentioning

confidence: 99%

The Hsp70 chaperone system maintains high concentrations of active proteins and suppresses ATP consumption during heat shock

Tomita

2007

Syst Synth Biol

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Hsp70 chaperones assist protein folding by cycling between the ATP-bound T state with low affinity for substrates and the ADP-bound R state with high affinity for substrates. The transition from the T to R state is catalyzed by the synergistic action of the substrate and DnaJ cochaperones. The reverse transition from the R state to the T state is accelerated by the nucleotide exchange factor GrpE. These two processes, T-to-R and R-to-T conversion, are affected differently by temperature change. Here we modeled Hsp70-mediated protein folding under permanent and transient heat shock based on published experimental data. Our simulation results were in agreement with in vitro wild-type Escherichia coli chaperone experimental data at 25°C and reflected R-to-T ratio dynamics in response to temperature effects. Our simulation results suggested that the chaperone system evolved naturally to maintain the concentration of active protein as high as possible during heat shock, even at the cost of recovered activity after return to optimal growth conditions. They also revealed that the chaperone system evolved to suppress ATP consumption at non-optimal high growing temperatures.

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

Abstract: 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/.

Cited by 648 publications

References 0 publications

Modelling of Mammalian Cells and Cell Culture Processes

Modelling of Mammalian Cells and Cell Culture Processes

A computational model of chemotaxis-based cell aggregation

The Hsp70 chaperone system maintains high concentrations of active proteins and suppresses ATP consumption during heat shock

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