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Modeling cancer’s ecological and evolutionary dynamics

Bukkuri, Anuraag LU ; Pienta, Kenneth J. ; Hockett, Ian ; Austin, Robert H. ; Hammarlund, Emma U. LU ; Amend, Sarah R. and Brown, Joel S. (2023) In Medical Oncology 40(4).
Abstract

In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be... (More)

In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.

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author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Cancer evolution, Eco-evolutionary dynamics, Evolutionary game theory, Mathematical modeling, Resistance
in
Medical Oncology
volume
40
issue
4
article number
109
publisher
Humana Press
external identifiers
  • pmid:36853375
  • scopus:85149269437
ISSN
1357-0560
DOI
10.1007/s12032-023-01968-0
language
English
LU publication?
yes
id
1bc1caab-6996-4c8d-83d3-6933bf6f0269
date added to LUP
2024-01-12 14:11:19
date last changed
2024-04-27 09:32:51
@article{1bc1caab-6996-4c8d-83d3-6933bf6f0269,
  abstract     = {{<p>In this didactic paper, we present a theoretical modeling framework, called the G-function, that integrates both the ecology and evolution of cancer to understand oncogenesis. The G-function has been used in evolutionary ecology, but has not been widely applied to problems in cancer. Here, we build the G-function framework from fundamental Darwinian principles and discuss how cancer can be seen through the lens of ecology, evolution, and game theory. We begin with a simple model of cancer growth and add on components of cancer cell competition and drug resistance. To aid in exploration of eco-evolutionary modeling with this approach, we also present a user-friendly software tool. By the end of this paper, we hope that readers will be able to construct basic G function models and grasp the usefulness of the framework to understand the games cancer plays in a biologically mechanistic fashion.</p>}},
  author       = {{Bukkuri, Anuraag and Pienta, Kenneth J. and Hockett, Ian and Austin, Robert H. and Hammarlund, Emma U. and Amend, Sarah R. and Brown, Joel S.}},
  issn         = {{1357-0560}},
  keywords     = {{Cancer evolution; Eco-evolutionary dynamics; Evolutionary game theory; Mathematical modeling; Resistance}},
  language     = {{eng}},
  number       = {{4}},
  publisher    = {{Humana Press}},
  series       = {{Medical Oncology}},
  title        = {{Modeling cancer’s ecological and evolutionary dynamics}},
  url          = {{http://dx.doi.org/10.1007/s12032-023-01968-0}},
  doi          = {{10.1007/s12032-023-01968-0}},
  volume       = {{40}},
  year         = {{2023}},
}