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Related Experiment Video

Updated: May 30, 2026

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

Published on: May 8, 2015

Evolutionary dynamics in a simple model of self-assembly.

Iain G Johnston¹, Sebastian E Ahnert, Jonathan P K Doye

¹Rudolf Peierls Centre for Theoretical Physics, University of Oxford, 1 Keble Road, Oxford OX1 3NP, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|July 30, 2011

Summary

This summary is machine-generated.

This study models the evolution of self-assembling polyominoes using a genetic algorithm. It reveals how evolutionary dynamics depend on fitness landscapes and applies findings to protein symmetry.

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Last Updated: May 30, 2026

Self-assembly of Complex Two-dimensional Shapes from Single-stranded DNA Tiles

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Published on: May 8, 2015

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Self-Assembly of Microtubule Tactoids

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Published on: June 23, 2022

Area of Science:

Computational Biology
Evolutionary Computation
Biophysics

Background:

Self-assembly is crucial for biological structures.
Understanding the evolution of self-assembly mechanisms is key.
Polyominoes provide a model for studying self-assembly rules.

Purpose of the Study:

To investigate evolutionary dynamics in a model of robust two-dimensional structure self-assembly.
To explore the relationship between genetic rules (genotype) and assembled structures (phenotype).
To apply the model to understand symmetry preferences in protein structures.

Main Methods:

Developed an idealized model for polyomino self-assembly.
Incorporated a genotype-phenotype map into a genetic algorithm.
Evolved rule sets under selection for target structures.
Analyzed evolutionary dynamics based on population size, mutation rate, and recombination.

Main Results:

The model exhibits complex evolutionary dynamics influenced by fitness landscape parameters.
Characterized model genome space to link dynamics with landscape structure.
Demonstrated how evolutionary pressures shape self-assembly rules.
Observed emergence of dihedral symmetry preference in the model.

Conclusions:

The genetic algorithm effectively models the evolution of self-assembly.
Fitness landscape structure significantly impacts evolutionary trajectories.
The model provides insights into the evolution of structural symmetry in biological systems, specifically protein tetramers.