Cancer as a system dysfunction
Khalid Saeed Elizabeth Ryder Amity Manning
Professor of Economics Associate Professor of Assistant Professor of
and System Dynamics Biology Biology
Worcester Polytechnic Institute
Worcester, MA, USA
Extended abstract
In this paper, we describe a system dynamics model that views cancer as a dysfunction of the
cellular system analogous to those of human societies. Our experiments with the model replicate
the propagation of the ailment and the impacts of the treatments. It also represents work in
progress and should be viewed as a proposition that can be further developed to understand
cancer and used to create appropriate combinations of interventions for specific situations. Our
model represents the interaction between the various types of cells in an organ or a subsystem of
the body. It depicts the common structure in the simplest possible terms. It defines a generic
system that can be applied to solid as well as blood and bone-related diseases, although we
mainly address it to proliferation of sold cancers and their response to treatments in this paper.
Whether viewed in the context of an afflicted organ or a body subsystem, the interacting cell
populations can be placed in four broad categories: normal cells, pre-cancer cells, cancer cells
and immune system cells. Thus, our model contains four stocks connected by flows shown in
Figure 1. Normal cells and Immune system cells have disciplined growth regimes in that they
tend to grow to their indicated levels — the former goal is determined by the internal intelligence
of the cell, while the later by the surveillance need created by the very existence of the unwanted
cells in the body. The cancer cell stock is initially populated by a transformation of precancerous
cells meant to reflect the accumulation of tumor-promoting mutations that increase with age.
Once transformed, the cancerous cells exhibit unregulated proliferation and thus cancer grows
exponentially. The immune response increases to combat the rising cancer cell population, and it
is ultimately the imbalance of tumor growth vs immune response that yields a proliferation of
cancerous cells.
Model behavior
The model is supplied with an internally consistent set of parameters and initial conditions so
under normal conditions, all cell populations except cancer are maintained in a dynamic
equilibrium or homeostasis. Cancer population is initialized at zero value. The simulation time is
set at 1000 months (83.3 years) that approximates the average life expectancy of a healthy
individual. It should be noted that despite the equilibrium, all cell populations constantly turn
over rather than remaining constant. The in- and out- flows tied to each stock continue while the
stocks remain in a dynamic balance.
When initially populated by the transformation of pre-cancer cells, the cancer cells begin to
grow exponentially. Low and intermediate rates of growth of cancer are contained however by a
concomitant increase in the immune activity that constantly kills the cancer cells and keeps their
population under control. The absolute number of cancer cells is influenced by cancer cell
proliferation, as well as the rate at which normal cells acquire pre-cancerous characteristics and
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then are ultimately transformed into cancer cells. To model rates of proliferation that may differ
between aggressively growing and slow growing tumors, the model allows for modulation of
cancer cell proliferation rates.
cancer growth
fration
immune sys
cancer cell cancer cells )
capacity adequacy
proliferation Killed and injured
fr precancerous cells,
going cancerous
fr rogue cells removed
by immune system
normal cell
precancerous cells
adj time {)
going cancerous
fraction normal
normal cell cells going precancerous
normal cell
desired immune
system cells
immune system
cell ratio
‘normal cell going
precancerous
desired
normal
cells
indicated
precancer
normal cells c
immune sys
capacity adequacy
normal cells
killed by autoimune
isease
indicated immune
system cells
immune system cell
population change,
fraction normal cells,
by autoimune disease
immune
system cells
immune system cel
adjustment time
Figure 1 Key cell population stocks, their connecting flows and rules of conduct in the
cellular society.
Conclusion
We have made a preliminary attempt in this paper to model the development of cancer as an
interaction between normal, immune system, pre-cancer and cancer cell populations. The model
is used to test hypotheses about lifetime risk of cancer and the performance of Cancer treatments,
and shows credible results. It is also simulated for experimental treatment options, which reveals
interesting contingencies. We recognize the importance of the inclusive nourishment commons
of the body embodied in its blood supply and propose extending the model to include this for
future work.
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