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Self-assembly, modularity, and physical complexity

Overview of attention for article published in Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, August 2010
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50 Mendeley
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4 CiteULike
Title
Self-assembly, modularity, and physical complexity
Published in
Physical Review E: Statistical, Nonlinear, and Soft Matter Physics, August 2010
DOI 10.1103/physreve.82.026117
Pubmed ID
Authors

S. E. Ahnert, I. G. Johnston, T. M. A. Fink, J. P. K. Doye, A. A. Louis

Abstract

We present a quantitative measure of physical complexity, based on the amount of information required to build a given physical structure through self-assembly. Our procedure can be adapted to any given geometry, and thus, to any given type of physical structure that can be divided into building blocks. We illustrate our approach using self-assembling polyominoes, and demonstrate the breadth of its potential applications by quantifying the physical complexity of molecules and protein complexes. This measure is particularly well suited for the detection of symmetry and modularity in the underlying structure, and allows for a quantitative definition of structural modularity. Furthermore we use our approach to show that symmetric and modular structures are favored in biological self-assembly, for example in protein complexes. Lastly, we also introduce the notions of joint, mutual and conditional complexity, which provide a useful quantitative measure of the difference between physical structures.

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 3 6%
United States 3 6%
Brazil 1 2%
Serbia 1 2%
Belgium 1 2%
Colombia 1 2%
Unknown 40 80%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 30%
Researcher 15 30%
Lecturer > Senior Lecturer 4 8%
Professor 4 8%
Professor > Associate Professor 4 8%
Other 8 16%
Readers by discipline Count As %
Physics and Astronomy 13 26%
Computer Science 11 22%
Agricultural and Biological Sciences 8 16%
Engineering 4 8%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 12 24%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 April 2015.
All research outputs
#5,295,220
of 6,230,503 outputs
Outputs from Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
#3,959
of 5,431 outputs
Outputs of similar age
#81,921
of 99,484 outputs
Outputs of similar age from Physical Review E: Statistical, Nonlinear, and Soft Matter Physics
#15
of 21 outputs
Altmetric has tracked 6,230,503 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,431 research outputs from this source. They receive a mean Attention Score of 2.0. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 99,484 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 21 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.