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Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations
Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and c...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4071513/ https://www.ncbi.nlm.nih.gov/pubmed/25097751 http://dx.doi.org/10.1098/rsfs.2014.0037 |
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author | Liao, David Tlsty, Thea D. |
author_facet | Liao, David Tlsty, Thea D. |
author_sort | Liao, David |
collection | PubMed |
description | Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities. |
format | Online Article Text |
id | pubmed-4071513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-40715132014-08-06 Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations Liao, David Tlsty, Thea D. Interface Focus Articles Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities. The Royal Society 2014-08-06 /pmc/articles/PMC4071513/ /pubmed/25097751 http://dx.doi.org/10.1098/rsfs.2014.0037 Text en http://creativecommons.org/licenses/by/3.0/ © 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Liao, David Tlsty, Thea D. Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations |
title | Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations |
title_full | Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations |
title_fullStr | Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations |
title_full_unstemmed | Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations |
title_short | Evolutionary game theory for physical and biological scientists. I. Training and validating population dynamics equations |
title_sort | evolutionary game theory for physical and biological scientists. i. training and validating population dynamics equations |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4071513/ https://www.ncbi.nlm.nih.gov/pubmed/25097751 http://dx.doi.org/10.1098/rsfs.2014.0037 |
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