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Cell simulation tools for systems biology
Rajveer Seyan

Abstract— With the immense amount of data that is becoming
available for cellular biological systems, cell simulation software
is becoming increasingly popular as a tool to aid in the
construction of cell biological models. In particular, two projects
known as E-Cell and Virtual Cell have been developed. These
software simulations have been successfully used to construct
various models including the virtual self surviving cell, human
erythrocyte, nucleocytoplasmic transport and calcium dynamics
in neuronal cells.
E-Cell and Virtual Cell are becoming
increasingly important for in silico modeling of cellular
phenomena alongside laboratory experiments. They are also
being continuously updated and improved upon to overcome
current limitations, and are showing evermore their necessity and
indispensability in a cell biology laboratory.
Index Terms—Cell Simulation, E-Cell, Systems Biology,
Virtual Cell
I.
INTRODUCTION
The main focus of molecular biology and genetic analysis
has been the identification of the essential elements and
fundamental mechanisms that enable cell function. Major
efforts have been made on identifying the cellular mechanisms
(genes and proteins) that are responsible for specific
phenomena, thus creating an exhaustive knowledge of these
genes and proteins and how they are involved. Although
interactions have been investigated to understand the function
of different cellular phenomena, studies have only been done
on a small scale basis and in an ad hoc manner. This situation
is being forced to change due to the emergence of new
experimental methods enabling us to measure large numbers of
components simultaneously. This is opening up the possibility
for system-level studies [1]. This information explosion in
biology has provided and will continue to provide
unprecedented opportunities for the biomedical research
community. However, to make full use of these opportunities,
it is necessary to have tools that facilitate the analysis of such
immense amounts of data. With respect to cell biology, such
tools should allow the construction of quantitative models of
cellular processes, thus enabling researchers to test (by
simulation) whether a set of interacting molecules or structures
in the cell can produce a particular behaviour [2]. The
complex behaviour of the cell cannot be determined or
predicted without the aid of a computer model and simulations
[3]. Many attempts have been made to simulate molecular
processes in cellular systems and several software packages
have been developed for quantitative simulation of
biochemical pathways [4]. This paper will demonstrate the
importance of cell simulations as a tool in systems biology. It
will discuss the abilities, successes, limitations and future
prospects associated with current cell simulation software with
focus on two particular projects, E-Cell and Virtual Cell.
II. E-CELL
The E-Cell project was initiated in 1996 at the ShonanFujisawa Campus of Keio University in Fujisawa, Japan. The
aim was to directly address the challenging task of whole-cell
modeling [3]. E-Cell is essentially a computer software
environment for modeling and simulation of the cell. It is a
generic object-oriented environment for simulating molecular
processes in user-definable models. It is equipped with
graphical interfaces that permit observation and interaction,
thus making the simulation more user friendly [5].
A. How E-Cell Works
The system is, in essence, a rule-based simulation system. It
is written entirely in C++, an object-oriented programming
language and it runs on Linux operating system [5]. The
model is composed of three lists loaded at runtime: the
substance list, the rule list and the system list. The substance
list defines all objects that make up the cell and the culture
medium. The rule list defines all of the reactions that can
occur in the cell. The system list defines functional and/or
spatial structure of the cell and its surrounding environment.
At every time frame, the state of the cell is expressed as a list
of concentration values of all substances that are within the
cell, along with along with global values for cell volume, pH
and temperature. The simulator engine computes all of the
functions that are defined in the reaction rule list, thus
generating the next state in time. A number of sample models
are provided with the system. The user can, however, create
user-defined models in addition to these by writing original
substance and rule lists. The graphical interfaces make the
program user friendly by allowing observation and interaction
throughout the simulation process [4].
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B. Current Models Using E-Cell
The first virtual self-surviving cell (SSC) model has been
developed at Keio University using E-Cell. This was done
using the genome sequence of the micro-organism
Mycoplasma genitalium. M. genitalium has the smallest
number of genes (approximately 480) of all organisms
currently known, and its genomic sequences have been
published. Its small genome makes it an ideal candidate for
whole-cell modeling. It has been demonstrated through
extensive gene-knock-out studies by the Institute for Genomic
Research (TIGR) that many of the 480 genes are not always
necessary for the survival of the organism. Therefore,
collaboratively with TIGR, Keio University constructed the
first hypothetical virtual cell using 127 genes which are
necessary for the cell’s survival and maintenance of
homeostasis [3].
Fig. 1 below shows a diagram of the metabolism overview of
the virtual SSC model discussed in [3] and [4]. This model
takes up glucose into the cytoplasm, metabolizes the glucose
through the glycolysis pathway. This produces ATP as an
energy source, which is consumed mainly for protein
synthesis. The 127 genes are transcribed by RNA polymerase
into mRNAs, and then translated into proteins by the
ribosome. The cell must constantly produce protein to sustain
life, since it has been modeled to degrade spontaneously over
time. The cell’s membrane is also modeled to degrade, thus,
the cell has a phospholipid biosynthesis pathway for
biosynthesis of the cell membrane. A constant supply of ATP
is needed for protein and membrane synthesis, and thus
glucose is essential for the cell to survive [3].
ATP
Glucose
Phospholipid Bilayer
Lactate
Glycolysis
Lipid Biosynthesis
Phospholipids
Fatty Acids
ATP
Degradation
127 Genes
Glycerol
Proteins
Transcription
mRNA
of experimental data have already been collected about them
[3].
The E-Cell model was developed for the human erythrocyte
by defining reaction rules for all the different metabolic
pathways based on previous erythrocyte models.
All
parameters and kinetic equations used in constructing the
model were obtained from previously published experimental
data.
Simulations have shown that when the E-Cell
erythrocyte model reaches a steady-state, quantities of
intermediate metabolites inside the virtual cell are comparable
with experimental data obtained from living erythrocytes. The
current erythrocyte model is being extended and improved for
more accurate simulations that account factors such as
pressure, pH and variable cell volume [3].
C. Future Prospects
In addition to the ‘virtual self-surviving cell’ and the ‘human
erythrocyte model’, E-Cell is currently being used to construct
other models. These include a ‘mitochondria model’ and a
‘signal transduction’ model for the chemotaxis of the E. Coli
bacterium [3]. A major problem associated with constructing
large-scale cell models is the lack of quantitative data. Most
of the available biological knowledge is qualitative (functions
of genes, pathways, protein interactions). For simulation, it is
necessary to have quantitative data (concentrations of
metabolites and enzymes, flux rates, kinetic parameters,
dissociation constants). A major barrier that must continually
be crossed in order to have improvements towards cell
simulation is the development of better high-throughput
technologies for measurement of inner-cellular metabolites.
Large amounts of data for a variety of cell states can then be
collected to construct quantitative models, and the models can
be refined through an iterative process until the simulation
results match the data [3].
Also despite the developments in E-Cell software itself,
there are still several difficulties with regards to simulating
realistic models. A newer version of E-Cell is being
developed to address these issues. It will be capable of
simultaneously running various different algorithms in a single
simulation. Suitable algorithms will be used for different submodels of various cellular processes at different levels of
abstraction and in different time-scales [5].
Translation
ATP
tRNA
rRNA
Fig. 1. The virtual self-surviving cell model. The minimal cell has 127
genes, sufficient to maintain protein and membrane structure.
Obviously, the SSC model is hypothetical and no such cell
exists in nature. Thus, E-Cell has also been used to model
living cells so that simulation results can be evaluated. Human
erythrocytes (red blood cells) were chosen for the model
because of their limited intracellular metabolism and because
they do not replicate, transcribe or translate genes. This model
can be compared with real red blood cells since vast amounts
III. VIRTUAL CELL
The ‘Virtual Cell’ is another example of a generic software
environment that is being developed at the National Resource
for Cell Analysis and Modeling for cell biological research.
This software is freely accessible to all members of the
scientific community and it is designed to be used by a wide
range of scientists from experimental cell biologists to
theoretical biophysicists. This software provides an integrated
framework within which models of cell biological processes
can be created based on both experimental data and purely
theoretical assumptions [6].
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A. How Virtual Cell Works
Firstly, various experimentally determined data is used to
construct the cell model. The data includes the identity of
molecules, their reactions and transport properties, where they
are compartmentalized within the cell, and the topological
organization of the compartments. This takes care of the
physiology of the cellular process being modeled. For spatial
simulations, the various compartments have to be mapped to
the appropriate geometries. This scheme allows the same
physiology to be reused with different geometries, thus
facilitating ready adaptation and modification of the models.
After the physiology is mapped to the geometry and initial and
boundary conditions are specified, the model is fully defined.
The framework automatically converts the biological
mechanisms into a mathematical system. If desired, the
mathematical system can be further refined and edited. Then,
after choosing appropriate time steps for the simulation, the
model is sent to the appropriate solver and a simulation is
generated. Data visualization resources are provided for ease
of use when navigating through the enormous simulation data
sets [2].
B. Current Models Using Virtual Cell
Nucleocytoplasmic transport, and in particular the transport
of the small GTPase Ran, serves as a good example of an
application of the Virtual Cell to quantitative cell biology.
GTPases are a large family of enzymes that can bind and
hydrolyze GTP (guanosine triphosphate - a chemical
compound essential to signal transduction in living cells). Ran
is a small GTPase that is required for protein import, mRNA
export, and the maintenance of nuclear structures. The Virtual
Cell model was able to simulate both qualitatively and
quantitatively Ran transport over a range of different
conditions. It presented the first estimates for the amount of
steady-state flux of Ran across the nuclear envelope. This in
turn provided a lower estimate for the value of the total
nucleocytoplasmic
flux
(approximately
20
million
macromolecules per cell per minute). It also predicted that a
very high gradient of Ran-GTP exists between the nucleus and
the cytoplasm.
These predictions have been verified
experimentally using a fluorescence energy transfer (FRET)
biosensor [7].
The Virtual Cell has also been used to simulate calcium
dynamics in a neuronal cell. In certain neuronal cells, the
neurotransmitter bradykinin (BK) triggers IP3 (inositol 1,4,5trisphosphate) dependent calcium waves that consistently start
in the neurite proximal to the soma and rapidly propagate in
both directions.
Using calcium imaging techniques,
quantitative uncaging of microinjected IP3 and simulations
from the Virtual Cell, it was found that IP3 levels build up in
the neurite at a rate and to an extent much greater than in the
soma. By coupling experiments and simulations involving
BK, it was confirmed that the proximal segment of the neurite
is the crucial region for response to a BK stimulus. This
proximal segment is necessary and sufficient to initiate and
propagate the calcium signal to other regions of the cell [2].
This study is a good representation of the interplay between
experiment and modeling. The initial calcium increase was
observed in the middle of the neurite, and spread bidirectionally to the soma and growth cone. This pattern was
observed in all of the studied cells as long as they had the same
characteristic neuronal morphology. A quantitative model of
this process was constructed using Virtual Cell software to
determine how all the individual components could interact to
form the observed calcium wave. Data for this model was
obtained from both prior literature and laboratory experiments.
In several instances previously reported experiments had to be
repeated when it was found that a model based on data from
the literature was not correctly predicting the observed calcium
dynamics. A very interest finding that came out of this
iterative modeling and experiment process was that the
observed calcium wave could only be reproduced if the density
of calcium in the soma was about twice as high as its density in
the neurite [2].
C. Future Prospects
The version of Virtual Cell that is presently in use can handle
a large range of modeling problems encompassing reactiondiffusion processes in arbitrary geometries. However, the
system will need to be significantly enhanced for problems that
require a changing geometry, such as cell migration or mitosis.
Also, the current system can only treat some types of
stochastic processes such as Brownian motion, directed
particle mobility along microtubule tracks, and the reaction of
individual particles with continuously distributed molecules.
In situations where the number of interacting particles is too
low for a continuous description, there is a need to expand the
stochastic formulations to include the treatment of reactions
between discrete molecular species. There is also a need to
develop a discrete state treatment for models of single ionchannel currents and locations. The system architecture and
the user interface will be adapted to fully accommodate
stochastic models.
In addition, Virtual Cell hopes to
incorporate more efficient numerical algorithms that take
advantage of massively parallel computer architectures. This
feature, along with external database support, will allow the
formation of larger scale biochemical network models within a
high-resolution cellular geometry [2].
IV. CONCLUDING REMARKS
Cell simulations are undeniably an important tool towards
advancement in systems biology. With the rapid accumulation
of biological data, it is becoming increasingly clear that it is no
longer practical to try and understand the dynamic behaviour
of various cellular processes through experimentation alone.
Software simulation tools such as Virtual Cell and E-Cell are
necessary to truly realize the full potential of biological data
for elucidating cellular mechanisms.
Clearly however,
sophisticated cell simulations alone are not sufficient. It is
also necessary to have continued improvement in laboratory
techniques for obtaining accurate quantitative data, since such
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data is necessary to generate a reliable model in the first place.
Therefore, the use of in silico modeling of cellular phenomena
in combination with laboratory experiments will lead to more
reliable predictions and results than could be obtained by
either method alone. Thus, the addition of software modeling
tools to a cell biology laboratory will become as important as
the microscope [7].
REFERENCES
[1]
[2]
[3]
[4]
[5]
[6]
[7]
H. Kitano, “Looking beyond the details: a rise in system-oriented
approaches in genetics and molecular biology,” Current Genetics,
(2002) 41: 1-10.
L. Loew and J. Schaff, “The Virtual Cell: a software environment for
computational cell biology,” TRENDS in Biotechnology, vol. 19, no. 10,
Oct. 2001, pp. 401-406.
M. Tomita, “Whole-cell simulation: a grand challenge of the 21st
century,” TRENDS in Biotechnology, vol. 19, no. 6, June 2001, pp. 205210.
M. Tomita, K. Hashimoto, K. Takahashi, T. Shimizu, Y. Matsuzaki, F.
Miyoshi, K. Saito, S. Tanida, K. Yugi, J. Venter, C. Hutchison, “E-Cell:
software environment for whole cell simulation,” Bioinformatics, vol.
15, no. 1, 1999, pp. 72-84.
K. Takahashi, N. Ishikawa, Y. Sadamoto, H. Sasamoto, S. Ohta, A.
Shiozawa, F. Miyoshi, Y. Naito, Y. Nakayama and M. Tomita, “E-Cell
2: Multi-platform E-Cell simulation system,” Bioinformatics, vol. 19,
no. 13, 2003, pp. 1727-1729.
Moraru, J. Schaff, B. Slepchenko and L. Loew, “The Virtual Cell An
Integrated Modeling Environment for Experimental and Computational
Cell Biology,” Ann. N.Y. Acad. Sci. 971: 595-596 (2002).
B. Slepchenko, J. Schaff, I. Macara and L. Loew, “Quantitative cell
biology with the Virtual Cell,” TRENDS in Cell Biology, vol. 13, no. 11,
Nov. 2003, pp. 570-576.