Evolution of oil droplets in a chemorobotic platform. Gutierrez, J. M. P., Hinkley, T., Taylor, J. W., Yanev, K., & Cronin, L. Nature Communications, December, 2014.
Evolution of oil droplets in a chemorobotic platform [link]Paper  doi  abstract   bibtex   
Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution.
@article{gutierrez_evolution_2014,
	title = {Evolution of oil droplets in a chemorobotic platform},
	volume = {5},
	copyright = {© 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
	url = {http://www.nature.com/ncomms/2014/141208/ncomms6571/full/ncomms6571.html},
	doi = {10.1038/ncomms6571},
	abstract = {Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype–phenotype correlations found in biological evolution.},
	language = {en},
	urldate = {2015-02-04TZ},
	journal = {Nature Communications},
	author = {Gutierrez, Juan Manuel Parrilla and Hinkley, Trevor and Taylor, James Ward and Yanev, Kliment and Cronin, Leroy},
	month = dec,
	year = {2014},
	keywords = {Chemical Sciences, Physical Chemistry, materials science, nanotechnology}
}

Downloads: 0