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Systems Biology and Anticancer Drug Discovery
Wen Jiang. Author, Student Member, IEEE
Abstract - The traditional anticancer drug discovery
process is inefficiency due to the lack of predictiveness of
the models used. Systems biology will provide a better
understanding of the cellular activities and pathways
related to a certain disease such as cancer. This
understanding will enable researchers to create predictive
computer models to simulate in vivo organisms, for
instance, human colon cancer cell. These models are based
on the integration of genomic, proteomic, and metabolomic
data. They can be used to optimize drug leads, in which it
will maximize the effectiveness against cancer cells with
minimal or no side effects on normal cells.
Keyword – bioinformatic, in silico, systems biology.
I. INTRODUCTION
Historically, the drug discovery process for anticancer
drugs has faced many challenges. The most difficult problem
to deal with is the fact that the models used in the laboratory to
predict drug responses are not predictive at all. Most of
today’s models used to predict drug response are made of
laboratory animals that carry transplanted human tumor cells.
These models are called the xenografts [1]. However, the drug
handling ability is different between humans and the
xenografts. A drug that is effective in the animal might not be
so effective within the human body; on the other hand, there
are many effective cancer drugs been discarded because of the
non-effectiveness in the animal model. Largely due to the
above mentioned problem, of the thousands of anticancer
drugs discovered to be effective in cell culture or animal
models, only 39 are approved by the Food and Drug
Administration for exclusive use in chemotherapy. Therefore,
the main focus for better and more efficient anticancer drug
discovery is to obtain an accurate model that can predict the
human response to the drug.
Currently, extensive research efforts have been conducted
in the area of better understanding the genetics of cells. The
Human Genome Project for instance, has succeeded with the
decoding of the human genome sequence in 15 February 2001
[3]. Many other projects involving the studies of protein
interactions and the decoding of three-dimensional structure of
proteins, which are coded by a genome, are also underway.
The purpose of these studies is to understand all components
and interactions of a complex functional system and their
underlying dynamics [4]. By gaining this knowledge about
the system, a systems biology model can be created. Such
model uses computer-aided modelling techniques, which can
model all molecular interactions with a cell or even complex
system as an integrated computational process. However, the
creation of the model is a complicated process, it requires the
integration of genomics, proteomics and bioinformatics to
create a whole system view of complex biological entities [2].
At the same time, better information processing technology,
database and high-speed computation technology are needed
to analyze complex systems such as human body.
In this report, a brief introduction of the concepts of
systems biology will be given. Such concepts include what is
systems biology and what are the methods of creating system
models. It will be followed by the detailed discussion of
systems biology approach to cancer treatments, such as drug
discovery and therapy. The main focus for this part will be on
how systems biology can generate more accurate models that
can predict the effectiveness and related side effects of drugs.
The final part of the report will focus on the current progress
made within the field. Examples such as the development of
human colon cell model will be mentioned.
II. BACKGROUND
The concept of systems biology has been around for
sometime, it is only until recently that researchers begin to
look at biological entities at a systems level. This is mainly
due to the availability of high-throughput measurements of
DNA, RNA and proteins [5]. Because of the fact that the
modeling and simulation of biological systems which is the
core of systems biology require quantitative data at all
hierarchical levels, therefore, massive data is needed.
Information related to the DNA, mRNA, proteins, protein
interactions, information pathways, networks, cells, tissues,
organisms or even entire populations are required [6]. Also,
this information should contain external parameters related
such as the concentration and absolute number of molecules
within each cell. Without the high-throughput measurement
techniques available today, such information capture would be
impossible.
Computer models for biological systems can be generated
based on the available information by using two approaches:
chemical kinetic models and discrete circuit models.
Chemical kinetic models attempt to represent cellular
processes as a system of chemical equations. The reaction
process can be represented mathematically as differential
equations, where the change in concentrations of reactants and
post-reaction products are recorded based on the reaction rate.
Some other biological processes such as transcription or
translation operate in a random fashion, thus stochastic
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relations should be used for the model [6]. A kinetic model of
the lambda phage is shown in Figure 1.
Figure 1: kinetic model for the lambda phage circuit.
The discrete circuit model does not represent biological
process as a continuous event. It models discrete event as
feedback loops. Such model is made of a network consisting
of nodes and directed edges between the nodes. The nodes
stand for the quantity of a certain molecule, and the edge is the
effect of the level of a given node on the neighboring nodes. In
a simplified model, the node assumes one of two discrete
states, indicating the presence or absence of a certain
molecule. For the initial state, a start value was given to each
node, and subsequent values can be determined by the
respective functions of the nodes. The network state of all
nodes will evolve over a series of discrete time steps, where
the next step is based on the condition determined from the
current step. Discrete circuit model is a much simplified
compare to the real life situation, especially for the binary
value assigned to the state of each node. This is not very
realistic, since real biological systems often can have more
than two states [4].
III. SYSTEMS BIOLOGY APPROACH TO ANTICANCER
DRUG DISCOVERY
The traditional drug discovery process is a linear process
based on the sequential approaches of biology and chemistry.
The limitation of such process is the vast screening of
randomized chemical libraries against a small number of
biological targets [7]. This approach works for single target,
one drug system. For multifactorial diseases, where multiple
targets or pathways have to be affected for successful
treatment, this approach has limited success. Systems biology
approach on the other hand will target a broader range of
biological structure, thereby creating the potential for
discovery of drugs that are effective to multiple diseases by
targeting common pathways implicated in pathogenesis [8].
Cancer, and other genetic or metabolic disorders often are
caused by complex multi-molecular interactions that cannot be
explained by an alteration in a single gene. In order to develop
drugs that are effective, the first step is to identify all the
molecular targets with a connection to the disease. This can be
done by using systems biology to identify novel molecular
targets or new uses for the existing molecular targets
available. Such targets include mutant proteins, which have
not previously identified and therefore have not established
connections with the disease. Next, it is necessary to decipher
complex inter- and intra-cellular signaling relationships, which
would provide information of the entire signaling networks.
With all these data known, a system model can be created to
simulate the effectiveness and side effects of a certain drug.
The identification of novel molecular targets or new uses
for existing targets can be achieved by the integrative systems
biology approach. For this approach, the proteomic and
metabolomic data acquisition and analysis are performed in
parallel, obtaining a link of the particular protein to the
interested disease. An example of the new uses of existing
targets is the discovery and successful development of
sildenafil (ViagraTM; Pfizer) [9]. With the identification of
these targets, a more accurate model can be generated to
predict the efficiency of certain drugs or lead compound. The
cellular signaling relationship is also important for the purpose
of drug discovery. By defining the entire signaling networks,
the researcher can generate models that can predict the toxicity
and side effects of the drugs. This will allow the development
of efficacious and safe therapeutic agents that can focus on the
most appropriate region of a signaling cascade [8].
The computer models currently been generated rely
extensively on the information provided from the
bioinformatic database. However, the amount of data is still
not detailed enough to be able to model a whole cell. The
approaches used for computer modeling today is to set
constraints on cellular activities, and generating a solution
space for predicting the behaviors of cells, as shown in Figure
2. If all the constraints for cellular process are known, the final
solution space reduces to a single solution. For systems
biology, the goal is to obtain as much information as possible.
Therefore reduce the size of the solution space; hence make
the model more predictable [10].
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Figure 2: Constraining possible behaviors. Because biological information
is incomplete, it is necessary to take into account the fact that cells are subject
to certain constraints that limit their possible behaviors. By imposing these
constraints in a model, one can then determine what is possible and what is
not, and determine how a cell is likely to behave [10].
The building of computer models for simulation of
complex biological systems is an iterative process. In silico
organism models will be constructed to represent their in vivo
counterparts. The models will be able to provide interpretive
and predictive capabilities. However, due to the incomplete
knowledge of true cell behaviors, the in silico models often are
missing features comparing to the in vivo organisms.
Therefore, experimental testable hypotheses must be
formulated based on the in silico analysis, and perform the
experiment to update the model as shown in Figure 3. Several
iteration processes is needed to make the model more accurate
and predictive. For anticancer drug discovery, such models
can be used to predict the effectiveness of the drug against its
target or multiple targets, and the potential undesired side
effects. This allows high through-put screening of new drugs
without going through the time consuming laboratory animal
experiments. The model can also be used for other non-drug
therapies such as gene therapy for cancer treatments.
Figure 3: Iterative in silico model building in biology involves the formulation
of experimentally testable hypotheses based on the in silico analysis,
collection of experimental data, and subsequent refinement of the models
based on these data.
During the Beyond Genome conference in Boston, 2002,
Gene Network Sciences (GNS) Inc. unveiled the world’s first
in silico model of a human colon cancer cell. The in silico
colon cancer cell contains over 2,000 variables, representing
the activities of more than 500 genes and proteins. The model
details connecting signal transduction and gene expression
networks involved in human cell growth, and contains about
one-third of all the targets for current cancer drugs such as
BCL-2, Ras, IKK and p53. [11]. The GNS model can speed
up the drug discovery process by identifying high value drug
targets, testing the efficacy of lead compounds, and running
virtual clinical trials. Currently, the model has made
predictions on targets that sensitize cancer cells towards
apoptosis and secondary targets that can be used in
combination to lead to significant cell death in cancer cells,
but not in normal cells. This will enable researchers to develop
drugs that can look for specific targets or combination of
targets related to the disease can increase the effectiveness and
reduce the side effects [12].
The model still has limitations since its database is
limited. According to the company, by the end of next year,
the model will contain around 5000 genes and proteins, and
incorporate all known regulatory pathway information about
the cell and every known drug target for cancer. This will
allow more accurate predictions for the purpose of anticancer
drug discovery. Any progress beyond this point will require
the advances in cellular genomics, proteomics, and
metabolomic data gathering. As mentioned before, systems
biology approach is needed to integrate various disciplines to
generate new knowledge that cannot be obtained by “isolated
methods”.
GNS is not the only company in the area of developing in
silico models for biological systems, company and
organizations such as Physiome Sciences and University of
Connecticut are also developing novel tools to simulate cell
behaviors [7]. Pharmaceutical companies are also joining the
field, and use the modeling tools to validate targets,
understand biological mechanisms, or optimize drug leads.
IV. CURRENT PROGRESS IN MODELING
V. CONCLUSION
Many progresses have been made in the area of in silico
modeling of biological systems. Projects such as the E-Cell
led by Prof. Masaru Tomita of the Keio University created a
virtual cell on the computer. The E-Cell allows the simulation
of the metabolism of a single cell organism with 127 genes,
the simplest genome [12]. The simulation of much more
complex systems, such as the human body, which consists of
60 trillion cells is extremely difficult. In order to achieve
simulation of such complex organisms, not only a more
detailed understanding of fundamental cellular activities is
needed, the hardware and software to support such simulation
is also required.
Systems biology opens up new ways for high through-put
drug screening by understanding the fundamentals of cellular
activities and utilizing predictive models based on those
understanding. However, at present time, systems biology
research in the area of anticancer drug discovery is still in the
early development stage. This is mainly due to the lack of
knowledge of many of the potential targets that causes cancer.
Also, the development of predictive models is limited to single
cell organism only. For complex multi-cell organisms, such as
human, not only a better and through understanding of all
components, their interactions, related parameters are
required. The software and hardware that can support such
massive simulation also needs to be substantially upgraded. As
a result, systems biology is really a multi-disciplinary field
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that integrates the field of biology, engineering, computer
science, and many other areas of study together. The progress
in every one of the above areas is essential for the success of
systems biology as a new and emerging field of study.
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