Cellular automaton of idealized brain tumor growth dynamics

A. R. Kansal; S. Torquato; G. R. Harsh Iv; E. A. Chiocca; T. S. Deisboeck

doi:10.1016/S0303-2647(99)00089-1

Cellular automaton of idealized brain tumor growth dynamics

A. R. Kansal
, S. Torquato
, G. R. Harsh Iv
, E. A. Chiocca
, T. S. Deisboeck

Research output: Contribution to journal › Article › peer-review

80 Scopus citations

Abstract

A novel cellular automaton model of proliferative brain tumor growth has been developed. This model is able to simulate Gompertzian tumor growth over nearly three orders of magnitude in radius using only four microscopic parameters. The predicted composition and growth rates are in agreement with a test case pooled from the available medical literature. The model incorporates several new features, improving previous models, and also allows ready extension to study other important properties of tumor growth, such as clonal competition. Copyright (C) 2000 Elsevier Science Ireland Ltd.

Original language	English (US)
Pages (from-to)	119-127
Number of pages	9
Journal	BioSystems
Volume	55
Issue number	1-3
DOIs	https://doi.org/10.1016/S0303-2647(99)00089-1
State	Published - Feb 2000

All Science Journal Classification (ASJC) codes

Applied Mathematics
General Biochemistry, Genetics and Molecular Biology
Statistics and Probability
Modeling and Simulation

Keywords

Cellular automaton
Proliferative dynamics
Tumor modeling

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10.1016/S0303-2647(99)00089-1

Cite this

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title = "Cellular automaton of idealized brain tumor growth dynamics",

abstract = "A novel cellular automaton model of proliferative brain tumor growth has been developed. This model is able to simulate Gompertzian tumor growth over nearly three orders of magnitude in radius using only four microscopic parameters. The predicted composition and growth rates are in agreement with a test case pooled from the available medical literature. The model incorporates several new features, improving previous models, and also allows ready extension to study other important properties of tumor growth, such as clonal competition. Copyright (C) 2000 Elsevier Science Ireland Ltd.",

keywords = "Cellular automaton, Proliferative dynamics, Tumor modeling",

author = "Kansal, \{A. R.\} and S. Torquato and \{Harsh Iv\}, \{G. R.\} and Chiocca, \{E. A.\} and Deisboeck, \{T. S.\}",

note = "Funding Information: S.T. gratefully acknowledges the Guggenheim Foundation for his Guggenheim fellowship to conduct this work. This work is also supported in part by grants CA84509 and CA69246 from the National Institutes of Health. Calculations were carried out on an IBM SP2, which was kindly provided by the IBM Corporation (equipment grant to Princeton University for the Harvard-Princeton Tumor Modeling Project). The technical assistance provided by Drs Kirk E. Jordan and Gyan V. Bhanot of the IBM T.J. Watson Labs is gratefully acknowledged. The authors would also like to thank Drs Stuart A. Kauffman of the Santa Fe Institute for Complex Science, Jerome B. Posner of Memorial Sloan Kettering Cancer Center, Michael E. Berens of the Barrows Neurological Institute and Hans-Juergen Reulen of the University of Munich Neurosurgical Department for valuable discussions. Copyright: Copyright 2007 Elsevier B.V., All rights reserved.",

year = "2000",

month = feb,

doi = "10.1016/S0303-2647(99)00089-1",

language = "English (US)",

volume = "55",

pages = "119--127",

journal = "BioSystems",

issn = "0303-2647",

publisher = "Elsevier Ireland Ltd",

number = "1-3",

}

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T1 - Cellular automaton of idealized brain tumor growth dynamics

AU - Kansal, A. R.

AU - Torquato, S.

AU - Harsh Iv, G. R.

AU - Chiocca, E. A.

AU - Deisboeck, T. S.

N1 - Funding Information: S.T. gratefully acknowledges the Guggenheim Foundation for his Guggenheim fellowship to conduct this work. This work is also supported in part by grants CA84509 and CA69246 from the National Institutes of Health. Calculations were carried out on an IBM SP2, which was kindly provided by the IBM Corporation (equipment grant to Princeton University for the Harvard-Princeton Tumor Modeling Project). The technical assistance provided by Drs Kirk E. Jordan and Gyan V. Bhanot of the IBM T.J. Watson Labs is gratefully acknowledged. The authors would also like to thank Drs Stuart A. Kauffman of the Santa Fe Institute for Complex Science, Jerome B. Posner of Memorial Sloan Kettering Cancer Center, Michael E. Berens of the Barrows Neurological Institute and Hans-Juergen Reulen of the University of Munich Neurosurgical Department for valuable discussions. Copyright: Copyright 2007 Elsevier B.V., All rights reserved.

PY - 2000/2

Y1 - 2000/2

N2 - A novel cellular automaton model of proliferative brain tumor growth has been developed. This model is able to simulate Gompertzian tumor growth over nearly three orders of magnitude in radius using only four microscopic parameters. The predicted composition and growth rates are in agreement with a test case pooled from the available medical literature. The model incorporates several new features, improving previous models, and also allows ready extension to study other important properties of tumor growth, such as clonal competition. Copyright (C) 2000 Elsevier Science Ireland Ltd.

AB - A novel cellular automaton model of proliferative brain tumor growth has been developed. This model is able to simulate Gompertzian tumor growth over nearly three orders of magnitude in radius using only four microscopic parameters. The predicted composition and growth rates are in agreement with a test case pooled from the available medical literature. The model incorporates several new features, improving previous models, and also allows ready extension to study other important properties of tumor growth, such as clonal competition. Copyright (C) 2000 Elsevier Science Ireland Ltd.

KW - Cellular automaton

KW - Proliferative dynamics

KW - Tumor modeling

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DO - 10.1016/S0303-2647(99)00089-1

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AN - SCOPUS:0034116087

SN - 0303-2647

VL - 55

SP - 119

EP - 127

JO - BioSystems

JF - BioSystems

IS - 1-3

ER -

Cellular automaton of idealized brain tumor growth dynamics

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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