Modeling cancer’s ecological and evolutionary dynamics
(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
- Bukkuri, Anuraag LU ; Pienta, Kenneth J. ; Hockett, Ian ; Austin, Robert H. ; Hammarlund, Emma U. LU ; Amend, Sarah R. and Brown, Joel S.
- organization
- publishing date
- 2023-04
- 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}}, }