- author = "Nadav Kashtan and Uri Alon",
- title = "Spontaneous evolution of modularity and network motifs",
- journal = "Proceedings of the National Academy of Sciences",
- year = "2005",
- volume = "102",
- number = "39",
- pages = "13773--13778",
- month = sep # " 27",
- keywords = "genetic algorithms, genetic programming, EHW, NAND, ANN, demes, parallel GA, MFINDER1.2",
-
URL = "
http://www.pnas.org/cgi/reprint/102/39/13773.pdf",
-
DOI = "
doi:10.1073/pnas.0503610102",
- 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.",
-
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.
May 2022 www pages gone? 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
May 2022 gone Supplementary material can be found at: www.pnas.org/cgi/content/full/0503610102/DC1
",
