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

Roudi, Y; Nirenberg, S; Latham, PE; (2009) Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't. UNSPECIFIED, US. Green open access

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Abstract

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.

Type: Other
Title: Pairwise Maximum Entropy Models for Studying Large Biological Systems: When They Can Work and When They Can't
Open access status: An open access version is available from UCL Discovery
DOI: 10.1371/journal.pcbi.1000380
Language: English
Additional information: © 2009 Roudi et al. 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 credited.
Keywords: RETINAL GANGLION-CELLS, CROSS-CORRELATION, INFORMATION, CAT
UCL classification: UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neuroscience Unit
URI: http://discovery.ucl.ac.uk/id/eprint/114377
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