TOPIC: from whole-cell models and other high-through put technologies




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A cell is a fundamental unit of all living
organism, it is composed by organelles which perform some activities like
cellular respiration (mitochondrial), digestion (lysosomes), and it is
enveloped with a cell membrane. Simulation is the process of performing
experiments on a model. Using simulation techniques instead of actually
performing the experiments can save time and money; in addition, there are
experiments that are impossible to carry out in reality, that may be possible
to simulate. The validity of the model is crucial; if the model is invalid, the
simulation results will also be invalid. As the science is advancing, cell need to be
visualized and modelled at a molecular level in other to strengthen researches
on living organism. This modelling will help many field of medicine in
designing drugs, treating diseases, vaccines designing. Whole cell simulation
as a way of modelling organism cell provides information on cell in a virtual
way by giving out detailed images of cells organelles, DNA, RNA, and proteins
etc… It involves developing efficient algorithms, data structures,
visualization and communication tools to orchestrate the integration of large
quantities of biological data with the goal of computer modelling.




The purpose
of whole-cell modeling is to predict how genotype determines phenotype. It accounts
for the specific function of every gene product and predict the dynamics of
every molecule over the entire life cycle. It has also the potential to enable
engineers to rationally design cells to perform useful functions such as
synthesizing drugs, sensing disease, and decontaminating waste. In addition,
they have the potential to enable physicians to provide personalized medical
care and accelerate
biomedical science. However, discovering new biology from whole-cell models and
other high-through put technologies requires novel tools for exploring and
analyzing complex high-dimensional data.



There are several possible uses of
whole-cell simulations based on computer:

Firstly, its important use is to
verify that the underlying model behaves as its real-world counterpart in
different environments. Secondly, when provided the model is good enough,
simulation can also be used to predict the outcome of experiments. This way, it
may be possible to simulate some experiments that simply cannot be carried out
on real cells. At the last not the least, whole cell simulation provides a
framework where a biologist can construct cell models and perform simulations
on them in an intuitive way.

Whole-cell models identify the limits of our
current knowledge for a given biological system. With all the data that is
generated for a particular cell or organism, there remains a dramatic gap
between what is known and what remains to be discovered. To completely “solve”
a cell would ideally involve rigorous and coordinated statistical design of
experiments to comprehensively identify the main and interaction effects in a
given network. Anything short of this will inevitably lead to both
under-explored areas of the network, which are essentially gaps in our knowledge,
as well as controversy when published experimental results exhibit seemingly
inconsistent results in the absence of sufficient network context.

-Predict complex, multi-network

-Suggest future experiments that
may lead to new knowledge

-Provide a framework for the safe
and effective design of genetically-modified organisms


• Medical applications of models of human cells:

 Human models could revolutionize medicine. For
example, in the future, we envision that computational oncologists will use
accurate, personalized wholecell models of tumors, parameterized by each
tumor’s genetic variations, to find the optimal combination and dosage of drugs
to treat each patient’s cancer. Similar approaches could be used to personalize
therapy for any patient with any disease who is being evaluated for any drug.

• Industrial applications of genetically optimized

Many economically
transformative genetic engineering applications of bacteria are currently under
investigation Khalil 2010, including drug production Ajikumar 2010,
renewable fuel synthesis, generating energy from sunlight, and hazardous waste
disposal Lee 2012. However, these efforts are hindered by challenges in
predicting and testing the benefits of possible genetic modifications. This
process could be improved by (1) using whole-cell models to rationally design
genomes by optimizing in silico phenotypes and (2) using genome editing methods
such as CRISPR  or genome synthesis
methods to implement designer genomes.













With the advancement in
computerization, softwares have already been developed to stimulate
multi-algorithm models:

WholeCellKB is used to
organize experimental training data.

WholeCellSimDB helps to store
simulation results, and

WholeCellViz is needed to
visualize simulations results.


All the
above softwares were designed to facilitate exploration, analysis, and
communication of whole-cell model data. And this helps researchers use whole-cell
model simulations to drive advances in biology and bioengineering.


How they work

Using wholeCellviz

It is a
web-based software program for visually exploring and analyzing whole-cell

It enables users to interactively analyze many aspects of cell
physiology including:

                             -Cell mass,
volume, and shape,

                            -Metabolite, RNA,
and protein copy numbers,

reaction fluxes,

                             -Molecular machine
statuses – DNA polymerase, RNA polymerase.                                         

                             -Chromosome copy
number, superhelicity, integrity, and DNA binding status.

provides 14 animated visualizations, including metabolic and chromosome maps.
These visualizations help researchers analyze model predictions by displaying
predictions in their biological context. Furthermore, WholeCellViz enables researchers to compare predictions within and
across simulations by allowing users to simultaneously display multiple


5. THE

It has been said “You discover things a lot faster with a
computer model than you do without it”. Assistant professor of bioengineering Markus
Covert, PhD at Stanford university created the first software
simulation of an entire organism of M. genitalium. This smallest free
organism  commonly known as Mgen, is a sexually transmitted, small and  pathogenic bacterium that lives
on the ciliated epithelial cells
of the urinary and genital tracts in humans. The Mycoplasma genitalium simulation have
been introduced in order to predict   the dynamics of every molecule over their entire life


In other words, about 75
percent of what biologists know about Mycoplasma genitalium is now being simulated on Covert’s
computer. That’s already enough to give him two things: confidence about
integrating the rest of the coding regions; and a simulation that already works
well enough for his group to ask biologically interesting questions about the
organism, such as why it grows so much more slowly than even its close