Biological networks have an inherent simplicity: they
are modular with a design that can be separated into
units that perform almost independently. Furthermore,
they show reuse of recurring patterns termed network
motifs. Little is known about the evolutionary origin
of these properties. Current models of biological
evolution typically produce networks that are highly
nonmodular and lack understandable motifs. Here, we
suggest a possible explanation for the origin of
modularity and network motifs in biology. We use
standard evolutionary algorithms to evolve networks. A
key feature in this study is evolution under an
environment (evolutionary goal) that changes in a
modular fashion. That is, we repeatedly switch between
several goals, each made of a different combination of
subgoals. We find that such modularly varying goals
lead to the spontaneous evolution of modular network
structure and network motifs. The resulting networks
rapidly evolve to satisfy each of the different goals.
Such switching between related goals may represent
biological evolution in a changing environment that
requires different combinations of a set of basic
biological functions. The present study may shed light
on the evolutionary forces that promote structural
simplicity in biological networks and offers ways to
improve the evolutionary design of engineered
systems.
Elistist selection, high mutation rate, fitness
parsimony genotype pressure. pop size=1000 or 2000.
crossover. Goal switched every 20 generations. Z-score
Z = (Nreal - Nrand) / sigma. Fixed genome genetic
algorithm. Quantifying Modularity. Evolution with
nonmodular random goals did not yield modular networks.
Modularly varying give evolution of modularity and
motifs. The two functions had shared subproblems --
modularly varying goals MVG. Rapid target function
swapping -> Q=0.54 (ie high modularity). But typically
used 11 NAND rather than 10 NAND evolved with fixed
fitness target. With randomly chosen goals (ie no
common sub goals) evolved networks typically are not
modular. Modular seed rapidly loses modularity.
Fixed? architecture feed forward multi-layer (4 layers)
perceptron? MLP. pop size=600. Feedback (output to
level -1 etc) allowed in NAND circuit. Every 10
generations copies of the 50 best networks from each
island were added to each of the other islands,
replacing eliminated networks. 4x2 binary picture.
Bifan and diamond motifs common but also some
anti-motifs less common in evolved modular ANN than
occurred in random ANN.
Table 5. Modularity measure of several biological
networks E. coli transcription network Neuronal network
of C. elegans (threshold = 5) Signal transduction in
human cells Over the course of many goal changes,
modularly varying goals seem to guide the population
toward a region of network space that contains fitness
peaks for each of the goals in close proximity. This
region seems to correspond to modular
networks.
High-resolution figures, a citation map, links to
PubMed and Google Scholar, etc., can be found at:
www.pnas.org/cgi/content/full/102/39/13773
Supplementary material can be found at:
www.pnas.org/cgi/content/full/0503610102/DC1
This article cites 28 articles, 8 of which you can
access for free at:
www.pnas.org/cgi/content/full/102/39/13773#BIBL This
article has been cited by other articles:
www.pnas.org/cgi/content/full/102/39/13773#otherarticles
%0 Journal Article
%1 Kashtan:2005:PNAS
%A Kashtan, Nadav
%A Alon, Uri
%D 2005
%J Proceedings of the National Academy of Sciences
%K ANN, EHW, GA, MFINDER1.2 NAND, algorithms, demes, genetic parallel programming,
%N 39
%P 13773--13778
%R doi:10.1073/pnas.0503610102
%T Spontaneous evolution of modularity and network
motifs
%U http://www.pnas.org/cgi/reprint/102/39/13773.pdf
%V 102
%X Biological networks have an inherent simplicity: they
are modular with a design that can be separated into
units that perform almost independently. Furthermore,
they show reuse of recurring patterns termed network
motifs. Little is known about the evolutionary origin
of these properties. Current models of biological
evolution typically produce networks that are highly
nonmodular and lack understandable motifs. Here, we
suggest a possible explanation for the origin of
modularity and network motifs in biology. We use
standard evolutionary algorithms to evolve networks. A
key feature in this study is evolution under an
environment (evolutionary goal) that changes in a
modular fashion. That is, we repeatedly switch between
several goals, each made of a different combination of
subgoals. We find that such modularly varying goals
lead to the spontaneous evolution of modular network
structure and network motifs. The resulting networks
rapidly evolve to satisfy each of the different goals.
Such switching between related goals may represent
biological evolution in a changing environment that
requires different combinations of a set of basic
biological functions. The present study may shed light
on the evolutionary forces that promote structural
simplicity in biological networks and offers ways to
improve the evolutionary design of engineered
systems.
@article{Kashtan:2005:PNAS,
abstract = {Biological networks have an inherent simplicity: they
are modular with a design that can be separated into
units that perform almost independently. Furthermore,
they show reuse of recurring patterns termed network
motifs. Little is known about the evolutionary origin
of these properties. Current models of biological
evolution typically produce networks that are highly
nonmodular and lack understandable motifs. Here, we
suggest a possible explanation for the origin of
modularity and network motifs in biology. We use
standard evolutionary algorithms to evolve networks. A
key feature in this study is evolution under an
environment (evolutionary goal) that changes in a
modular fashion. That is, we repeatedly switch between
several goals, each made of a different combination of
subgoals. We find that such modularly varying goals
lead to the spontaneous evolution of modular network
structure and network motifs. The resulting networks
rapidly evolve to satisfy each of the different goals.
Such switching between related goals may represent
biological evolution in a changing environment that
requires different combinations of a set of basic
biological functions. The present study may shed light
on the evolutionary forces that promote structural
simplicity in biological networks and offers ways to
improve the evolutionary design of engineered
systems.},
added-at = {2008-06-19T17:35:00.000+0200},
author = {Kashtan, Nadav and Alon, Uri},
biburl = {https://www.bibsonomy.org/bibtex/24883c2a08385a9b3b41f55108494d849/brazovayeye},
doi = {doi:10.1073/pnas.0503610102},
interhash = {7e455889bff51ea7b5df6bab2c166607},
intrahash = {4883c2a08385a9b3b41f55108494d849},
journal = {Proceedings of the National Academy of Sciences},
keywords = {ANN, EHW, GA, MFINDER1.2 NAND, algorithms, demes, genetic parallel programming,},
month = {September 27},
notes = {Elistist selection, high mutation rate, fitness
parsimony genotype pressure. pop size=1000 or 2000.
crossover. Goal switched every 20 generations. Z-score
Z = (Nreal - Nrand) / sigma. Fixed genome genetic
algorithm. Quantifying Modularity. Evolution with
nonmodular random goals did not yield modular networks.
Modularly varying give evolution of modularity and
motifs. The two functions had shared subproblems --
modularly varying goals MVG. Rapid target function
swapping -> Q=0.54 (ie high modularity). But typically
used 11 NAND rather than 10 NAND evolved with fixed
fitness target. With randomly chosen goals (ie no
common sub goals) evolved networks typically are not
modular. Modular seed rapidly loses modularity.
Fixed? architecture feed forward multi-layer (4 layers)
perceptron? MLP. pop size=600. Feedback (output to
level -1 etc) allowed in NAND circuit. Every 10
generations copies of the 50 best networks from each
island were added to each of the other islands,
replacing eliminated networks. 4x2 binary picture.
Bifan and diamond motifs common but also some
anti-motifs less common in evolved modular ANN than
occurred in random ANN.
Table 5. Modularity measure of several biological
networks E. coli transcription network Neuronal network
of C. elegans (threshold = 5) Signal transduction in
human cells Over the course of many goal changes,
modularly varying goals seem to guide the population
toward a region of network space that contains fitness
peaks for each of the goals in close proximity. This
region seems to correspond to modular
networks.
High-resolution figures, a citation map, links to
PubMed and Google Scholar, etc., can be found at:
www.pnas.org/cgi/content/full/102/39/13773
Supplementary material can be found at:
www.pnas.org/cgi/content/full/0503610102/DC1
This article cites 28 articles, 8 of which you can
access for free at:
www.pnas.org/cgi/content/full/102/39/13773#BIBL This
article has been cited by other articles:
www.pnas.org/cgi/content/full/102/39/13773#otherarticles},
number = 39,
pages = {13773--13778},
timestamp = {2008-06-19T17:42:57.000+0200},
title = {Spontaneous evolution of modularity and network
motifs},
url = {http://www.pnas.org/cgi/reprint/102/39/13773.pdf},
volume = 102,
year = 2005
}