Conference Session B1
Paper #229
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COMPUTATIONAL MODELS AND SIMULATIONS AND THEIR
PREVALENCE IN RESEARCHING ALZHEIMER’S DISEASE
Jaida Barker-English, [email protected], Mahboobin 4pm, Janet Canady, [email protected], Mahboobin 4pm
Abstract—This paper addresses the applications of
computational models and simulations in tracking and
predicting the development of various mechanisms in the
nervous system linked to the worsening of Alzheimer’s
Disease and brain atrophy. Research surrounding the
Amyloid plaque theory involves building databases of brain
scans from Alzheimer’s patients over a set period of time to
effectively track the growth of Amyloid plaque. This database
allows researchers to conduct predictive simulations that can
help determine where plaque buildup is most common, as well
as the expected detrimental effect the plaque may have on a
patient’s brain. Predictive simulations and computer models
allow researchers to develop methods of counteracting the
growth of Amyloid plaque, as well as test theoretical solutions
without using costly or potentially dangerous human trials.
This paper provides examples of the efforts and successes of
researchers in using simulations to predict Amyloid plaque
growth, and further discuss the significance of computer
models in biomedical fields to improve the lives of
Alzheimer’s patients. Furthermore, this paper assesses the
societal impact of Amyloid plaque simulations on the future
of Alzheimer’s Disease research, as well as the ethical
implications of using computer models according to the Code
of Professional Ethics for Simulationists.
tomography, computational modeling has populated the
biomedical field and continues to be one of its most viable
options for sustainability.
As computer modeling continues to assist researchers in
science, it has impacted Alzheimer’s Disease research
significantly. Because of the limited understanding of the
disease, victims of Alzheimer’s disease have minimal options
available for treatment. There are currently only four Food
and Drug Administration Approved Medicines for
Alzheimer’s Disease symptom treatment and none to reverse
the disease [1]. It is estimated to affect around 20% of the
American population age 65 or higher at a total of 88.5 million
people by the year 2050. It is linked to aging, and for every 5
years a person lives beyond age 65 they are twice as likely to
develop Alzheimer’s [2]. The disease seems to be increasing
its impact with time, so the need for research increases each
year. Simulations have allowed Alzheimer’s Disease
researchers to predict patterns of brain atrophy in an
individual patient, which in turn opens many opportunities for
the development of specific and individualized treatment
regimens and the development of new vaccines to counteract
the spread of atrophy.
Simulations and computer models tailored specifically for
tracking the development of Alzheimer’s Disease are already
in use, and their success could very well steer the direction of
both Alzheimer’s Disease treatment and the acceptance of
simulations as a beneficial and legitimate means of
experimentation. The success of simulations in providing
reliable and trustworthy results is paramount to their
continued use in the future, and with its growth comes the
concerns of the safety and ethical implications of using
simulations instead of real life trials, as well as taking their
lasting effect on society into account. With hope, the rapid
expansion and continued innovation of computational
modeling will continue to promote the general epidemiology
and eventual eradication of Alzheimer’s Disease, as well as
the plethora of other afflictions that computer models and
simulations serve to provide priceless intelligence.
Key Words—Alzheimer's Disease, Amyloid plaque,
Biomarker, Brain Atrophy, Computer Modeling, Simulation
INTRODUCTION: MODELING AND
SIMULATION IN THE BIOMEDICAL
SPHERE
As computer technology becomes increasingly more
efficient and convenient with time, computational models and
simulations have become more popular in the experimental
steps of hypothesis testing and development. From the
creation of magnetic resonance imaging, to positron emission
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University of Pittsburgh Swanson School of Engineering
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Jaida Barker-English
Janet Canady
Computational Model Types and Application to
Bioengineering
COMPUTER MODELING IN
BIOENGINEERING
The development of computational modeling heavily
aligns with its extensive use in the field of biomedical
engineering. Upon the re-discovery and recognition of
computational modeling for brain disease research,
researchers studied brain recognition patterns through
formation of network models. This allowed them to observe
anatomical and functional relationships along regions of the
brain by localizing lobe regions [3]. Other biological studies
that are assisted with computational modeling include blood
flow, optometry surgeries, drug design and limb
rehabilitation. For each of these processes, computational
modeling serves a unique, yet major significance.
Computer Modeling and Simulation Defined
The National Institute of Health defines Computational
Modeling as “The use of computers to simulate and study the
behavior of complex systems using mathematics, physics and
computer science” [3]. Computational Modeling is extremely
useful to the field of biology and research because it allows
scientists to simplify complex systems by visiting them one
variable at a time. In addition to replicating whole systems,
computer technology can be used to adjust models to be as
detailed or as specific as needed. For example, multiscale
modeling handles experimentation by testing materials on
both micro and macro scales. This gives researchers the
power to replicate complex systems layer by layer without
having to do physical experimentation. Also, while the testing
is being done through the computer, numerical data is being
recorded as well. Additionally, computer systems organize
data, making it easier for scientists to reach their own
conclusion while saving a significant amount of time and
resources. If operated properly, computers will allow
experiments to virtually run themselves.
Computational modeling is extremely effective for
laboratory testing. Models with modified variables are
monitored for reactions, and the reactions that happen are
accurate predictions of outcomes that would be produced
during physical experimentation. This makes computational
modeling a very sustainable option to solving medical issues.
Computational modeling can be sustainable in areas such as
including optimization, modeling, data management and
analytics, advanced sensing techniques, human computer
interaction, and intelligent systems, but the aspects of
sustainability we will mention in this paper will be system
design, optimization of processes, data management and
analytics and human computer interaction [4]. For more
complicated systems, computational modeling assist with
optimization of processes through its ability to replicate an
entire functioning system in particular. Also, unlike humans,
computers can simultaneously categorize and organize the
result of testing multiple variables under multiple conditions
making it helpful for data management and analytics. For
example, Magnetic Resonance Imaging (MRI) regularly
models medical phenomena such as brain atrophy and
Positron
Emission
Tomography
(PET)
measures
hypometabolism by three-dimensional mapping [3]. These
models can provide the scientist with information about
abnormal brain regions and locations of the abnormalities,
neither of which can be observed by the naked eye.
FIGURE 1 [3]
A three-dimensional computational model of platelet
system reveals basic structure
The figure above depicts a model of a platelet system
compared to its actual appearance under a microscope. With
this accurate model, predictions and experiments regarding
platelets can be developed and tested with ease.
The process of blood flow can be seen with instruments
such as microscopes, but it cannot be altered without proper
examination and knowledge of capillaries. For blood clot
removal surgeries, doctors examine the style of the blockage
and decide on the best way to clear plaques. The ability to
make such a decision wisely is enhanced by the preparation
of computer models where surgeons replicate the
environment of the capillary so that they can form a predicted
response for actual blockage removal of plaques. The usage
of computer models in drug design helps researchers simulate
the body’s unwanted responses to medicine. Although some
of these processes seem independent of Alzheimer’s Disease
research, the combination of concrete visualization and
medicinal testing are key components of how computer
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Janet Canady
modeling is used in Alzheimer’s Disease research. The
responses of computer simulated systems or features can
predict the outcome of testing on biological material because
they are extremely accurate.
stages of Alzheimer’s Disease where atrophy causes
noticeable changes. The first stage may include mild shortterm memory loss or small changes in behavior. The patient
then experiences symptoms that make it harder for them to
operate independently—patients in the second stage of AD
may have trouble with complex tasks, may not recognize
close friends and family, and will often lose the ability to
handle personal responsibilities such as daily chores,
finances, or hygiene routines. When the patient reaches the
third, most severe stage of decline, they are usually declared
unable to operate independently and are hospitalized for their
safety. Symptoms of this stage include delusions, long-term
memory loss, Dementia, and inability to recognize basic
human needs such as thirst or hunger on their own. The
Alzheimer’s association predicts that the number of senior
citizens with Alzheimer’s per year will increase by 400,000,
but the cost of treatment will inflate by 13.1% [7]. Therefore,
Alzheimer’s research and modeling proves itself necessary
for economic sustainability of the population over age 65.
Currently, there are several main hypotheses regarding the
development of Alzheimer’s Disease. The first hypothesis
involves the formation of plaque blockages along the nervous
system. These plaques, known as amyloid plaques, are formed
between nerve cells by a protein called beta-amyloid peptide,
and are normally broken down and removed by the body.
However, in cases of AD, the plaques accumulate and form
insoluble blockages [8]. Using cerebrospinal fluid biomarkers
with amyloid deposition, Dickerson and Wolk concluded the
principle that the amyloid deposition hypothesis heavily
correlates with development of Alzheimer’s Disease [9].
The second hypothesis is described as a neurofibrillary
tangle, in which another species of protein, the tau protein,
creates tangles inside nerve cells in the brain. These tau
proteins are necessary for transferring important nutrients
between nerve cells, and abnormalities can lead to a complete
collapse of the nutrient-transfer system. These conditions
cause neurons to lose their connections to other neurons, and
the brain begins to shrink as more neurons are killed. This
process is known as brain atrophy [8]. Using computational
models, Dr. Braak morphometrically and cross-sectionally
mapped out the spread of abnormal tau proteins following
each stage of Alzheimer’s Disease. With MRI assistance, he
kept track of morphological changes and could then follow
the pathological progression of the disease [9].
Lastly, the cholinergic hypothesis proposes that the
memory deterioration observed in AD patients is caused by
reduced synthesis of acetylcholine. When cholinergic
neurons in the forebrain aren’t formed properly, acetylcholine
cannot be released and synapse does not occur. Because
synaptic loss correlates with disease progression and loss of
cholinergic neurons produces AD symptoms, scientists
proposed the theory that if they create a drug to act on the
cholinergic system, they can treat patients with Alzheimer’s
Why Modeling and Simulation is Important
Computer modeling serves a vital role in simplifying
complex processes, making them a very efficient way to
conduct experiments. For example, examination of brains
through MRIs has reduced the necessity for opening the skull
cap to examine issue with the brain, a practice that has proven
to be fatal by case of stroke, coma, brain damage, and
anesthesia accidents. With MRIs, this practice can be avoided
and the chances of living increases for those who require brain
examinations. Many brain disease diagnoses are only possible
by computational modeling because of the utilization of MRI
and PET scans.
An MRI-developed model of transneuronal transmission
made by Dr. Ashish Raj at the Department of Radiology in
the Weill Medical College of Cornell University consisted
only of the brain’s connectivity network which greatly
mathematically reduced the topography of Alzheimer’s
Disease in the brain [5]. The result is a connectome, a clear
map of the brain’s neural connections. With simplified
models, Alzheimer’s Disease can be inspected one factor at a
time and through this, theories of the cause have been
developed. Because of computer modeling, researchers have
kept track of plaque growth and have associated accumulation
of specific plaques with the cause of Alzheimer’s Disease.
This result has not only amplified, but transformed the role of
computational modeling of plaques and brain deterioration in
research. The goal of Raj’s study is now “to develop the
theoretical model into a clinically useful computational
biomarker with the ability to predict future patterns of atrophy
in susceptible individuals” [5]. Being able to detect the
likeliness of the disease early in patients greatly increases the
amount of treatment a person can receive ahead of time thus
increasing their chances of survival. The continuation of
computational modeling of atrophy patterns and other key
Alzheimer’s Disease principles is important if the disease is
to eventually be eradicated.
COMPUTER MODELING IN
ALZHEIMER’S DISEASE RESEARCH
Defining and Explaining Alzheimer’s Disease
Alzheimer’s Disease (AD) is defined as “a progressive,
degenerative disorder that attacks the brain's nerve cells, or
neurons, resulting in loss of memory, thinking and language
skills, and behavioral changes” [6]. While physical
deterioration within the brain is not visible, there are several
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Disease [1]. The medicines developed from this model have
not yet been completely proven.
the Network Diffusion (ND) predictive model. This type of
computational simulation, known as a biomarker, is a unique
theoretical model “with the ability to predict future patterns
of atrophy in susceptible individuals” and is used in clinical
settings to monitor potential development of atrophy and
target areas of concern before they develop [5].
Once the predictive model is constructed, new data is
collected to test the model's’ ability to predict patterns of
atrophy that correlate with the experimental data. The
predictive results from the Network Diffusion model are
displayed using “glass brains,” a computer-generated model
of a brain with superimposed, color-coded spheres to
represent growth and spread of atrophy over time within
respective lobes. The glass brains show that the ND model
correctly predicts the experimental pattern of atrophy spread
throughout the brain, to the extent that it accurately displays
patterns that are specific to different categories of AD
symptoms. The ND model is able to differentiate between
cases of Mild Cognitive Disease and cases of Alzheimer’s
Disease based on the severity and pattern of atrophy [5].
Concluded Inconclusive: The Necessity for More
Research
Of the listed proposed theories, none have produce the
results that clearly prove the cause of Alzheimer’s Disease.
The results often conflict or are difficult to interpret. Because
Alzheimer patients often experience the same set of
symptoms, it is necessary for the causes of each of those
symptoms to be studied in junction. Computational modeling
has the ability to tie together in a single framework advances
from various levels of detail so they can examine the
interaction of mechanisms to find the specific interactions (or
lack thereof) that are the causes of the disease. Modeling has
been used to analyze each of the factors separately, but in
order figure out the pathogenesis of the disease, more research
must be done on tau hyper-phosphorylation, increased Aβ
production, reduction of and acetylcholine synthesisprincipal characteristics of AD.
With the possibility of eventual AD eradication,
computational modeling will increase population growth.
This is another example of sustainability in this technology
which fits the principal development challenge described by
the United Nations(UN) in chapter 2 of The Report of World
Commission on Environment and Development. The UN
states that “The principal development challenge is to meet
the needs and aspirations of an expanding developing world
population” [7]. Several researchers have developed theories
through modeling techniques to meet these needs.
THE NETWORK DIFFUSION
BIOMARKER: BUILDING A PREDICTIVE
DATABASE
Computer models and simulations have a significant role
in modern Alzheimer’s Disease research, and the success of
their use can prove beneficial not only to those whose
wellbeing relies on the haste and accuracy of the results, but
also to the credibility of computer modeling in all branches of
medicinal experimentation and development. Computer
models and simulations were used in a study conducted by Dr.
Ashish Raj in which patterns of brain atrophy were compiled
to build an algorithm capable of predicting areas of concern
in patients affected by Alzheimer’s Disease. In the study, MRI
scans were collected from subjects diagnosed with Mild
Cognitive Impairment (MCI), a common precursor to
Alzheimer’s Disease, and from subjects diagnosed with
Alzheimer’s Disease. Scans are taken over an extended period
of time to properly track development of atrophy, and are used
to construct an accurate database that accounts for variability
in individual patients. These results were then used to create
FIGURE 2 [5]
Raj’s “Glass Brains” imaging method, displaying
atrophy spread over time in color-coded lobes
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Janet Canady
of atrophy. This evidence makes for a much more reliable
method of categorization of Alzheimer’s subtypes, as
opposed to categorization by symptoms reported by the
patient [5].
Raj states that the Network Diffusion model has
limitations that serve as minor inconveniences to the
reliability of the results, however future projects are in
progress to reduce these inconveniences. The first of these
includes the sample size with which the model was built. The
MRI scans used to construct the model were limited to records
from the Alzheimer’s Disease Neuroimaging Initiative
(ADNI) as opposed to a larger sample from multiple sources,
and only includes atrophy progression scans from a range of
two to four years. Future work is planned to reduce or
eliminate these limitations; However, the current results of the
ND model are still sound and valid for continued use [5].
FIGURE 3 [5]
Network Diffusion Model scatterplot of correlation
between predicted and experimental
Atrophy/Metabolism vs. Exponential and Sigmoid
models
CURRENT LIMITATIONS OF COMPUTER
SIMULATIONS
As with all technology, computer simulations are not
without drawbacks and limitations. It is suggested that
computer simulations create a “false sense of security”
because the models and calculations are performed by a
computer where human error can be easily avoided [11].
Real-world testing is still very necessary for this reason--Computers are not suited for the unpredictability of nonsimulated conditions, nor are they perfectly suited to
accommodate systems with very complex machinations and
processes [11]. Models can only represent specific portions of
a total system, such as a model of the human brain rather than
the entire body, and should the model fail to acknowledge
potential influences outside of its scope, it leaves a chance of
unaccounted for results or possible disaster if an important
detail goes unnoticed [11]. Naturally, there is also the chance
of human error during the construction of a model or
simulation, in the form of coding bugs, or false theories and
assumptions [11]. While computer simulations have proven
to be incredibly useful to engineers and experimenters, they
must be utilized with utmost confidence that the simulation
lacks errors and takes into account any possible influences
outside of its scope. These limitations further support the
necessity for an extensive peer review system suggested by
Tongen and Adam above, for it can help to reduce the risk of
these limitations causing any significant problems. Therefore,
while computer simulations are convenient and cost-effective,
it is also important that computer simulations do not take
absolute precedence over real life testing, as no computer
simulation can capture every nuance and replicate every
precise condition that a real-life test can.
Often, the faults of computer modeling threaten its
accessibility to everyone. High costs of technology raise the
price of healthcare, and being diagnosed with Alzheimer’s
As per figure 3, compared to two similar biomarker
hypotheses, a sigmoid (logistic) model and an exponential
model of brain degeneration, Raj’s Network Diffusion model
provided the highest correlation between experimental and
predicted rate of Atrophy/Metabolism. The Network
Diffusion model proved to be significantly robust in the face
of variability and sampling error, and is capable of
minimizing variance with a precision improvement of 300%
compared to the baseline data [5].
Significance of the Network Diffusion Results
Overall, the Network Diffusion model trials produced
highly favorable results, both in its capability to predict
locations and severity of atrophy, and its improved results
compared to other predictive models. Due to its success, the
ND model can potentially be used in clinical and diagnostic
settings for predicting the future conditions of an individual
patient. Raj states that “Knowledge of what the future holds
can empower patients and allow informed choices regarding
lifestyle, therapeutic, and nontherapeutic interventions,” [5].
This supports the idea that the Network Diffusion model can
make significant improvements in the lives of both those
diagnosed with Alzheimer’s and those with other conditions
that cause brain atrophy. A prediction model as powerful as
the ND model will allow patients to seek the most viable
treatments for their current and possible future conditions, as
well as allow doctors, clinicians, and other medical
professionals to customize treatment regimens to fit an
individual’s best needs. Furthermore, Raj explains that the
ND model, paired with the Glass Brain imaging method,
provides visual and quantitative evidence of common patterns
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can be financially unbearable to those who do not save enough
for emergencies or have good insurance. As listed in the
United Nations’ Report of the World Commission on
Environment and Development, to be classified as
sustainable, a development must address the problem of the
large number of people who live in absolute poverty [11]. If
over 13.5% of Americans cannot afford basic healthcare,
computational modeling needs to serve a role of sustainability
and become more reasonably priced.
Energy consumption is also an additional limitation of
computational modeling because of the high voltage that MRI
machines and other imaging machines require. Frequent use
of electricity includes to impact the environment due to
inefficient energy consumption. Until power is provided in a
sustainable manner, things using power (i.e., computational
modeling), will not be fully sustainable.
Strides are being made every day to reduce risk of error
and increase sustainability of computer simulations and make
them even more capable of producing reliable results without
fear of mistakes or oversights, and as technology continues to
become more and more complex over time, it can be hoped
that within due time this goal will be achieved.
system design assumptions and known limitations...Be
explicit and unequivocal about the conditions of applicability
of specific models and associated simulation results,” and
“Assure thorough and unbiased interpretations and
evaluations of the results of modelling and simulation
studies.” [12]. These specific codes are very significant to the
practice of simulating and modeling, for their use relies
heavily on the credibility of those conducting the experiment,
and the condition that the results of an experiment are being
presented truthfully regardless of whether or not they are
favorable. In the case that results are not favorable, the
practicality of simulations is revealed—Using computer
models is much more convenient than conducting real-life
experiments, mainly because of their ability to be repeated.
Computer simulations eliminate the risk that money and time
will be wasted on rerunning an experiment only to have the
results turn out poorly. Computer simulations do not risk the
safety of living creatures or people, and do involve wasting
resources in hopes of a favorable outcome. This fact supports
the claim that simulations can prove beneficial to society
because they allow for valuable resources to be better
prioritized on successful plans rather than on works in
progress, and they allow for assurance that a plan is in proper
working order, has no detrimental side effects or
shortcomings, and is safe for use by humans before it is
officially set into action or developed.
The code further explains that simulationists should
regularly seek peer review and advisement, as well as be
willing to provide it when needed [12]. This idea is further
expanded upon by Anthony Tongen and Mary Adam, and
their assessment of peer reviewed simulations in “Ethics
Involved in Simulation-Based Medical Planning”. Tongen
and Adam propose a verification procedure for simulations
created with intent to be used in human clinical tests,
including “proper verification of the mathematical model,
proper understanding of the relationship between the model
and actual human physiology, proper verification of margins
of error, and proper verification of the risks and benefits of
the new technology” [13]. This verification system would
ensure that any possible risks are exposed and dealt with
accordingly, and that simulationists are certain their models
are safe and ready for eventual human trials [13]. It is
suggested that, just as pharmaceuticals and medical tools must
be evaluated for safety before use on human subjects,
simulations used in medical decision-making should be
assessed just as thoroughly, for their results, gone unchecked,
can have detrimental effects just as any medical evaluation
tool [13]. This system of verification can help to improve the
credibility of computer simulations and models, for it assures
that efforts are being made to keep any possible risks at a
minimum, and adds an extra level of verification that an
experiment is being conducted ethically. As computer
simulations become more widely used, implementing this
system of verification is a beneficial idea to ensure the
ETHICAL IMPLICATIONS OF COMPUTER
SIMULATIONS
Computer models and simulations can prove to have
lasting benefits for not only the progression of the medical
world, but also for the many people whose livelihoods depend
on new and innovative medical technology. As mentioned
above, experimental simulations such as Dr. Raj’s Network
Diffusion prediction model and Glass Brain imaging method
proved to be successful examples of computer simulations,
the results of which can have lasting influence on the state of
Alzheimer’s research and those affected by Alzheimer’s
Disease. From this experiment, it is clear to see that
simulations, when conducted effectively and ethically, can
have a major impact on society, especially as models and
simulations become more ubiquitous and widely used in
professional spheres, medical or otherwise.
First, it is critical to understand the ethical implications of
using computer models and simulations, for innovation of any
kind is null without proof that it is safe, reliable, and a genuine
improvement to society. To assure that simulations achieve an
acceptable standard of credibility, Dr. Tuncer I. Ören, Dr.
Maurice S. Elzas, Dr. Iva Smit, and Dr. Louis G. Birta
collaborated to create the Code of Professional Ethics for
Simulationists, a proposed code of ethics with guidelines
regarding “Personal development and the profession,
professional competence, trustworthiness, property rights and
due credit, and compliance” with the guidelines of the
proposed code [12]. Among these guidelines are the
stipulations that simulationists will “Provide full disclosure of
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Janet Canady
reputations of neither the experimenters responsible nor the
concept of simulations are tarnished, and to ensure that human
subjects of the experiment are at lowest possible risk of harm.
comes time to produce a final project, the most cost and
energy effective prototype is created. This has both
environmental and economic benefits, as less money is spent
because less resources are wasted on faulty experiments [14].
While simulations prove to be incredibly useful to the
scientific process, as well as to the wellbeing of people
affected by Alzheimer’s or any targetable affliction,
simulationists still have a long process ahead of them to
ensure their projects are reliable and sustainable. Projects like
Raj’s Network Diffusion model still have minor issues to be
fixed and improvements to be made, however its effectiveness
will only continue to increase [5]. Should more and more
experimenters decide to implement simulations into their
scientific process, society could very well see vast
improvements in the biomedical sphere as a whole.
SOCIETAL IMPACT OF COMPTUER
SIMULATIONS
As computer simulations and models are a relatively
recent technology, brought with the rise of medicallyimplemented computer technology, their credibility to
provide reliable information is still under scrutiny. Effective
simulations can prove to have major impacts on society, from
cases like the Network Diffusion prediction model for
estimating Alzheimer’s spread, or even non-medical
simulations that provide useful information. However, these
impacts should not be considered without first determining
that the simulation is conducted and overseen through an
ethical lense, and that the results of a simulation are accurate
and trustworthy for eventual implementation into society
where it will be utilized daily. The future of computer
simulations and models relies heavily on the ethical
implications of their use.
The Network Diffusion experiment is a prime example of
computer simulations and models benefiting the medical
world and the lives of Alzheimer’s patients. The success of
this experiment can pave the way for effective simulations for
a plethora of other conditions, can shift treatment methods to
a much more custom and individualized approach, and
provides a much more convenient and inexpensive method of
analyzing diseases as they progress [5].
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SUSTAINABILITY AND THE FUTURE OF
BIOMEDICAL SIMULATION
In the case of Alzheimer’s Disease treatment, computer
simulations have demonstrated their ability to predict patterns
of atrophy development, and in doing so provided
experimenters and clinicians with a plethora of new potential
theories for specific and individualized treatment. It can be
readily assumed that simulations such as the Network
Diffusion prediction model set a precedent for models and
simulations to be used in the future, not only for Alzheimer’s
research, but throughout the entirety of the medical sphere.
With the power of models and simulations, scientists harness
the ability to replicate lifelike conditions or even test new
vaccinations and treatment regimens without the costs and
risk that comes with using a living test subject, or without
wasting resources on an experiment that may prove fruitless.
It is critical that new technology is sustainable and
environmentally friendly, and computer models allow for that
verification to be made. Models both eliminate the need for
physical experiments and allow for more resource-conscious
plans to be made and tested within the model, so when it
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Janet Canady
[9] “Biomarker-Based Prediction of Progression in MCI:
Comparison of AD Signature and Hippocampal Volume with
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ACKNOWLEDGEMENTS
We would like to acknowledge our grader Renee
Prymus and our peer advisor Michelle Riffits for help with
writing this paper. Janet would also like to thank her research
professor, Dr. Shilpa Sant for heightening her passion for
bioengineering.
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