Santa Fe Institute

About: Santa Fe Institute is a nonprofit organization based out in Santa Fe, New Mexico, United States. It is known for research contribution in the topics: Population & Complex network. The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Network motifs in computational graphs: A case study in software architecture

Abstract: Complex networks in both nature and technology have been shown to display characteristic, small subgraphs (so-called motifs) which appear to be related to their underlying functionality. All these networks share a common trait: they manipulate information at different scales in order to perform some kind of computation. Here we analyze a large set of software class diagrams and show that several highly frequent network motifs appear to be a consequence of network heterogeneity and size, thus suggesting a somewhat less relevant role of functionality. However, by using a simple model of network growth by duplication and rewiring, it is shown the rules of graph evolution seem to be largely responsible for the observed motif distribution.

Evolution of Viral Proteins Originated De Novo by Overprinting

Abstract: New protein-coding genes can originate either through modification of existing genes or de novo. Recently, the importance of de novo origination has been recognized in eukaryotes, although eukaryotic genes originated de novo are relatively rare and difficult to identify. In contrast, viruses contain many de novo genes, namely those in which an existing gene has been “overprinted” by a new open reading frame, a process that generates a new protein-coding gene overlapping the ancestral gene. We analyzed the evolution of 12 experimentally validated viral genes that originated de novo and estimated their relative ages. We found that young de novo genes have a different codon usage from the rest of the genome. They evolve rapidly and are under positive or weak purifying selection. Thus, young de novo genes might have strain-specific functions, or no function, and would be difficult to detect using current genome annotation methods that rely on the sequence signature of purifying selection. In contrast to young de novo genes, older de novo genes have a codon usage that is similar to the rest of the genome. They evolve slowly and are under stronger purifying selection. Some of the oldest de novo genes evolve under stronger selection pressure than the ancestral gene they overlap, suggesting an evolutionary tug of war between the ancestral and the de novo gene.

Seeking Quantum Speedup Through Spin Glasses: The Good, the Bad, and the Ugly

Abstract: While manufacturing limitations are imposing constraints on Moore's law, researchers are searching for novel computing architectures based on quantum-mechanical effects. However, it remains to be shown that quantum annealing techniques consistently outperform classical simulated annealing to minimize optimization problems.

Defector-accelerated cooperativeness and punishment in public goods games with mutations.

Abstract: We study the evolution of cooperation in spatial public goods games with four competing strategies: cooperators, defectors, punishing cooperators, and punishing defectors. To explore the robustness of the cooperation-promoting effect of costly punishment, besides the usual strategy adoption dynamics we also apply strategy mutations. As expected, frequent mutations create kind of well-mixed conditions, which support the spreading of defectors. However, when the mutation rate is small, the final stationary state does not significantly differ from the state of the mutation-free model, independently of the values of the punishment fine and cost. Nevertheless, the mutation rate affects the relaxation dynamics. Rare mutations can largely accelerate the spreading of costly punishment. This is due to the fact that the presence of defectors breaks the balance of power between both cooperative strategies, which leads to a different kind of dynamics.