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Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't

One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard b...

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Autores principales: Roudi, Yasser, Nirenberg, Sheila, Latham, Peter E.
Formato: Texto
Lenguaje:English
Publicado: Public Library of Science 2009
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674569/
https://www.ncbi.nlm.nih.gov/pubmed/19424487
http://dx.doi.org/10.1371/journal.pcbi.1000380
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author Roudi, Yasser
Nirenberg, Sheila
Latham, Peter E.
author_facet Roudi, Yasser
Nirenberg, Sheila
Latham, Peter E.
author_sort Roudi, Yasser
collection PubMed
description One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point.
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spelling pubmed-26745692009-05-08 Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't Roudi, Yasser Nirenberg, Sheila Latham, Peter E. PLoS Comput Biol Research Article One of the most critical problems we face in the study of biological systems is building accurate statistical descriptions of them. This problem has been particularly challenging because biological systems typically contain large numbers of interacting elements, which precludes the use of standard brute force approaches. Recently, though, several groups have reported that there may be an alternate strategy. The reports show that reliable statistical models can be built without knowledge of all the interactions in a system; instead, pairwise interactions can suffice. These findings, however, are based on the analysis of small subsystems. Here, we ask whether the observations will generalize to systems of realistic size, that is, whether pairwise models will provide reliable descriptions of true biological systems. Our results show that, in most cases, they will not. The reason is that there is a crossover in the predictive power of pairwise models: If the size of the subsystem is below the crossover point, then the results have no predictive power for large systems. If the size is above the crossover point, then the results may have predictive power. This work thus provides a general framework for determining the extent to which pairwise models can be used to predict the behavior of large biological systems. Applied to neural data, the size of most systems studied so far is below the crossover point. Public Library of Science 2009-05-08 /pmc/articles/PMC2674569/ /pubmed/19424487 http://dx.doi.org/10.1371/journal.pcbi.1000380 Text en Roudi et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Roudi, Yasser
Nirenberg, Sheila
Latham, Peter E.
Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
title Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
title_full Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
title_fullStr Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
title_full_unstemmed Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
title_short Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
title_sort pairwise maximum entropy models for studying large biological systems: when they can work and when they can't
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2674569/
https://www.ncbi.nlm.nih.gov/pubmed/19424487
http://dx.doi.org/10.1371/journal.pcbi.1000380
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