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...
Saved in:
Published in | PLoS computational biology Vol. 5; no. 5; p. e1000380 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
01.05.2009
Public Library of Science (PLoS) |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | 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. |
---|---|
Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 Analyzed the data: YR PEL. Wrote the paper: YR SN PEL. Conceived and designed the study: YR SN PEL. Performed the experiments/simulations/mathematical derivations: YR PEL. |
ISSN: | 1553-7358 1553-734X 1553-7358 |
DOI: | 10.1371/journal.pcbi.1000380 |