Biomedical Engineering Reference
In-Depth Information
diameter and surface roughness. At the same time, our simulations
indicated that an increase in the tumor's velocity of spatial expan-
sion, <
v
>, indeed corresponds to an increase in the surface fracti-
cality
d
f
.
In the future, there are several extensions that can be pursued as follow-up
projects. As genomics data become increasingly available, our micro-macro
approach should provide a very helpful starting point for investigating the cru-
cial relationship between the molecular level—e.g., gene expression changes—
and the performance of the tumor system on a macroscopic scale. Our current
version of the model includes two "key" genes only since the precise role of
other potentially critical genes involved in the gene-protein regulatory network
remain largely unknown. If such information becomes available in the future,
our agent-based model can easily be extended to include more genes and pro-
teins involved in subcellular signaling cascades, as we have recently shown for
the case of EGF-R (55). In addition, since we currently assume a monoclonal
population of tumor cells, if a more realistic model is desired one will have to
consider a heterogeneous multiclonal population of tumor cells. In that context
of pursuing a more biologically accurate model, currently underway is extension
of our 2D framework into a 3D version, which will be better suited to simulate
the progression of an in-vivo tumor, and will also have potential clinical applica-
tions related to intraoperative navigation techniques (see this volume, Part IV,
chapter 8, by Heilbrun).
In summary,
multiscale agent-based modeling
is a powerful tool for inves-
tigating tumors as complex dynamic biosystems. This innovative approach has a
high potential to lead to paradigm-shifting insights into tumor biology, which in
turn is a first step toward improving diagnostic tools and therapeutic strategies
and thus, ultimately, patient outcome.
8.
ACKNOWLEDGMENTS
This work has been supported by grants CA 085139 and CA 113004 from
the National Institutes of Health and by the Harvard-MIT (HST) Athinoula A.
Martinos Center for Biomedical Imaging and the Department of Radiology at
Massachusetts General Hospital. Y.M. is the recipient of an NCI-Training Grant
Fellowship from the National Institutes of Health (CA 09502).
9.
NOTE
1. "Permission" refers to
haptotaxis
, i.e., enhanced cell movement along a
solid substrate, which however, is not explicitly modeled here.
