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A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks

Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. Th...

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Autor principal: Uthamacumaran, Abicumaran
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085613/
https://www.ncbi.nlm.nih.gov/pubmed/33982021
http://dx.doi.org/10.1016/j.patter.2021.100226
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author Uthamacumaran, Abicumaran
author_facet Uthamacumaran, Abicumaran
author_sort Uthamacumaran, Abicumaran
collection PubMed
description Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted.
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spelling pubmed-80856132021-05-11 A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks Uthamacumaran, Abicumaran Patterns (N Y) Review Cancers are complex dynamical systems. They remain the leading cause of disease-related pediatric mortality in North America. To overcome this burden, we must decipher the state-space attractor dynamics of gene expression patterns and protein oscillations orchestrated by cancer stemness networks. The review provides an overview of dynamical systems theory to steer cancer research in pattern science. While most of our current tools in network medicine rely on statistical correlation methods, causality inference remains primitively developed. As such, a survey of attractor reconstruction methods and machine algorithms for the detection of causal structures applicable in experimentally derived time series cancer datasets is presented. A toolbox of complex systems approaches are discussed for reconstructing the signaling state space of cancer networks, interpreting causal relationships in their time series gene expression patterns, and assisting clinical decision making in computational oncology. As a proof of concept, the applicability of some algorithms are demonstrated on pediatric brain cancer datasets and the requirement of their time series analysis is highlighted. Elsevier 2021-04-09 /pmc/articles/PMC8085613/ /pubmed/33982021 http://dx.doi.org/10.1016/j.patter.2021.100226 Text en © 2021 The Author https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Review
Uthamacumaran, Abicumaran
A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
title A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
title_full A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
title_fullStr A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
title_full_unstemmed A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
title_short A review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
title_sort review of dynamical systems approaches for the detection of chaotic attractors in cancer networks
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085613/
https://www.ncbi.nlm.nih.gov/pubmed/33982021
http://dx.doi.org/10.1016/j.patter.2021.100226
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