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Why model?

Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations m...

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Autor principal: Wolkenhauer, Olaf
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904180/
https://www.ncbi.nlm.nih.gov/pubmed/24478728
http://dx.doi.org/10.3389/fphys.2014.00021
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author Wolkenhauer, Olaf
author_facet Wolkenhauer, Olaf
author_sort Wolkenhauer, Olaf
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description Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations may however not be satisfied if the number of patient groups with similar characteristics is going to be very large. I therefore doubt that mining sequence data will give us an understanding of why and when therapies work. For understanding the mechanisms underlying diseases, an alternative approach is to model small networks in quantitative mechanistic detail, to elucidate the role of gene and proteins in dynamically changing the functioning of cells. Here an obvious critique is that these models consider too few components, compared to what might be relevant for any particular cell function. I show here that mining approaches and dynamical systems theory are two ends of a spectrum of methodologies to choose from. Drawing upon personal experience in numerous interdisciplinary collaborations, I provide guidance on how to model by discussing the question “Why model?”
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spelling pubmed-39041802014-01-29 Why model? Wolkenhauer, Olaf Front Physiol Physiology Next generation sequencing technologies are bringing about a renaissance of mining approaches. A comprehensive picture of the genetic landscape of an individual patient will be useful, for example, to identify groups of patients that do or do not respond to certain therapies. The high expectations may however not be satisfied if the number of patient groups with similar characteristics is going to be very large. I therefore doubt that mining sequence data will give us an understanding of why and when therapies work. For understanding the mechanisms underlying diseases, an alternative approach is to model small networks in quantitative mechanistic detail, to elucidate the role of gene and proteins in dynamically changing the functioning of cells. Here an obvious critique is that these models consider too few components, compared to what might be relevant for any particular cell function. I show here that mining approaches and dynamical systems theory are two ends of a spectrum of methodologies to choose from. Drawing upon personal experience in numerous interdisciplinary collaborations, I provide guidance on how to model by discussing the question “Why model?” Frontiers Media S.A. 2014-01-28 /pmc/articles/PMC3904180/ /pubmed/24478728 http://dx.doi.org/10.3389/fphys.2014.00021 Text en Copyright © 2014 Wolkenhauer. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Wolkenhauer, Olaf
Why model?
title Why model?
title_full Why model?
title_fullStr Why model?
title_full_unstemmed Why model?
title_short Why model?
title_sort why model?
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3904180/
https://www.ncbi.nlm.nih.gov/pubmed/24478728
http://dx.doi.org/10.3389/fphys.2014.00021
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