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Open Access Hypothesis

A systems biology approach to invasive behavior: comparing cancer metastasis and suburban sprawl development

John J Ryan12*, Benjamin L Dows1, Michael V Kirk1, Xueming Chen3, Jeffrey R Eastman4, Rodney J Dyer12 and Lemont B Kier2

Author Affiliations

1 Department of Biology, Virginia Commonwealth University, Richmond, VA 23284, USA

2 Center for the Study of Biological Complexity, Virginia Commonwealth University, Richmond, VA 23284, USA

3 Urban and Regional Planning Program, L. Douglas Wilder School of Government and Public Affairs, Virginia Commonwealth University, Richmond, VA 23284, USA

4 Department of Community Development, City of Richmond, VA 23220, USA

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BMC Research Notes 2010, 3:36  doi:10.1186/1756-0500-3-36

Published: 10 February 2010

Abstract

Background

Despite constant progress, cancer remains the second leading cause of death in the United States. The ability of tumors to metastasize is central to this dilemma, as many studies demonstrate successful treatment correlating to diagnosis prior to cancer spread. Hence a better understanding of cancer invasiveness and metastasis could provide critical insight.

Presentation of the hypothesis

We hypothesize that a systems biology-based comparison of cancer invasiveness and suburban sprawl will reveal similarities that are instructive.

Testing the hypothesis

We compare the structure and behavior of invasive cancer to suburban sprawl development. While these two systems differ vastly in dimension, they appear to adhere to scale-invariant laws consistent with invasive behavior in general. We demonstrate that cancer and sprawl have striking similarities in their natural history, initiating factors, patterns of invasion, vessel distribution and even methods of causing death.

Implications of the hypothesis

We propose that metastatic cancer and suburban sprawl provide striking analogs in invasive behavior, to the extent that conclusions from one system could be predictive of behavior in the other. We suggest ways in which this model could be used to advance our understanding of cancer biology and treatment.