Literature DB >> 20866886

Self-assembly, modularity, and physical complexity.

S E Ahnert1, 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.

Entities:  

Year:  2010        PMID: 20866886     DOI: 10.1103/PhysRevE.82.026117

Source DB:  PubMed          Journal:  Phys Rev E Stat Nonlin Soft Matter Phys        ISSN: 1539-3755


  14 in total

1.  Phenotypes can be robust and evolvable if mutations have non-local effects on sequence constraints.

Authors:  Marcel Weiß; Sebastian E Ahnert
Journal:  J R Soc Interface       Date:  2018-01       Impact factor: 4.118

Review 2.  Structural properties of genotype-phenotype maps.

Authors:  S E Ahnert
Journal:  J R Soc Interface       Date:  2017-07       Impact factor: 4.118

3.  Probing the Limits of Supramolecular G-Quadruplexes Using Atomistic Molecular Dynamics Simulations.

Authors:  Marilyn García-Arriaga; Maxier Acosta-Santiago; Antony Cruz; José M Rivera-Rivera; Gustavo E López; José M Rivera
Journal:  Inorganica Chim Acta       Date:  2017-09-05       Impact factor: 2.545

4.  Large protein complex interfaces have evolved to promote cotranslational assembly.

Authors:  Mihaly Badonyi; Joseph A Marsh
Journal:  Elife       Date:  2022-07-28       Impact factor: 8.713

5.  Integrated information increases with fitness in the evolution of animats.

Authors:  Jeffrey A Edlund; Nicolas Chaumont; Arend Hintze; Christof Koch; Giulio Tononi; Christoph Adami
Journal:  PLoS Comput Biol       Date:  2011-10-20       Impact factor: 4.475

6.  The arrival of the frequent: how bias in genotype-phenotype maps can steer populations to local optima.

Authors:  Steffen Schaper; Ard A Louis
Journal:  PLoS One       Date:  2014-02-05       Impact factor: 3.240

7.  A tractable genotype-phenotype map modelling the self-assembly of protein quaternary structure.

Authors:  Sam F Greenbury; Iain G Johnston; Ard A Louis; Sebastian E Ahnert
Journal:  J R Soc Interface       Date:  2014-04-09       Impact factor: 4.118

8.  Defining structural and evolutionary modules in proteins: a community detection approach to explore sub-domain architecture.

Authors:  Jose Sergio Hleap; Edward Susko; Christian Blouin
Journal:  BMC Struct Biol       Date:  2013-10-16

9.  The organization of biological sequences into constrained and unconstrained parts determines fundamental properties of genotype-phenotype maps.

Authors:  S F Greenbury; S E Ahnert
Journal:  J R Soc Interface       Date:  2015-12-06       Impact factor: 4.118

10.  Genetic Correlations Greatly Increase Mutational Robustness and Can Both Reduce and Enhance Evolvability.

Authors:  Sam F Greenbury; Steffen Schaper; Sebastian E Ahnert; Ard A Louis
Journal:  PLoS Comput Biol       Date:  2016-03-03       Impact factor: 4.475

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