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 & Context (language use). The organization has 558 authors who have published 4558 publications receiving 396015 citations. The organization is also known as: SFI.

Identification and characterization of transmitted and early founder virus envelopes in primary HIV-1 infection

Abstract: The precise identification of the HIV-1 envelope glycoprotein (Env) responsible for productive clinical infection could be instrumental in elucidating the molecular basis of HIV-1 transmission and in designing effective vaccines. Here, we developed a mathematical model of random viral evolution and, together with phylogenetic tree construction, used it to analyze 3,449 complete env sequences derived by single genome amplification from 102 subjects with acute HIV-1 (clade B) infection. Viral env genes evolving from individual transmitted or founder viruses generally exhibited a Poisson distribution of mutations and star-like phylogeny, which coalesced to an inferred consensus sequence at or near the estimated time of virus transmission. Overall, 78 of 102 subjects had evidence of productive clinical infection by a single virus, and 24 others had evidence of productive clinical infection by a minimum of two to five viruses. Phenotypic analysis of transmitted or early founder Envs revealed a consistent pattern of CCR5 dependence, masking of coreceptor binding regions, and equivalent or modestly enhanced resistance to the fusion inhibitor T1249 and broadly neutralizing antibodies compared with Envs from chronically infected subjects. Low multiplicity infection and limited viral evolution preceding peak viremia suggest a finite window of potential vulnerability of HIV-1 to vaccine-elicited immune responses, although phenotypic properties of transmitted Envs pose a formidable defense.

Clustering and preferential attachment in growing networks.

Abstract: We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of a pair of scientists collaborating increases with the number of other collaborators they have in common, and that the probability of a particular scientist acquiring new collaborators increases with the number of his or her past collaborators. These results provide experimental evidence in favor of previously conjectured mechanisms for clustering and power-law degree distributions in networks.

Empirical Analysis of an Evolving Social Network

Abstract: Social networks evolve over time, driven by the shared activities and affiliations of their members, by similarity of individuals' attributes, and by the closure of short network cycles. We analyzed a dynamic social network comprising 43,553 students, faculty, and staff at a large university, in which interactions between individuals are inferred from time-stamped e-mail headers recorded over one academic year and are matched with affiliations and attributes. We found that network evolution is dominated by a combination of effects arising from network topology itself and the organizational structure in which the network is embedded. In the absence of global perturbations, average network properties appear to approach an equilibrium state, whereas individual properties are unstable.

Anticipating Critical Transitions

Abstract: Tipping points in complex systems may imply risks of unwanted collapse, but also opportunities for positive change. Our capacity to navigate such risks and opportunities can be boosted by combining emerging insights from two unconnected fields of research. One line of work is revealing fundamental architectural features that may cause ecological networks, financial markets, and other complex systems to have tipping points. Another field of research is uncovering generic empirical indicators of the proximity to such critical thresholds. Although sudden shifts in complex systems will inevitably continue to surprise us, work at the crossroads of these emerging fields offers new approaches for anticipating critical transitions.

The Fourth Dimension of Life: Fractal Geometry and Allometric Scaling of Organisms

Abstract: Fractal-like networks effectively endow life with an additional fourth spatial dimension. This is the origin of quarter-power scaling that is so pervasive in biology. Organisms have evolved hierarchical branching networks that terminate in size-invariant units, such as capillaries, leaves, mitochondria, and oxidase molecules. Natural selection has tended to maximize both metabolic capacity, by maximizing the scaling of exchange surface areas, and internal efficiency, by minimizing the scaling of transport distances and times. These design principles are independent of detailed dynamics and explicit models and should apply to virtually all organisms.