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.

Genetic and Phenotypic Analyses of Human Immunodeficiency Virus Type 1 Escape from a Small-Molecule CCR5 Inhibitor.

Abstract: We have described previously the generation of an escape variant of human immunodeficiency virus type 1 (HIV-1), under the selection pressure of AD101, a small molecule inhibitor that binds the CCR5 coreceptor (A Trkola, S E Kuhmann, J M Strizki, E Maxwell, T Ketas, T Morgan, P Pugach, S X L Wojcik, J Tagat, A Palani, S Shapiro, J W Clader, S McCombie, G R Reyes, B M Baroudy, and J P Moore, Proc Natl Acad Sci USA 99:395-400, 2002) The escape mutant, CC10119, continued to use CCR5 for entry, but it was at least 20,000-fold more resistant to AD101 than the parental virus, CC1/85 We have now cloned the env genes from the the parental and escape mutant isolates and made chimeric infectious molecular clones that fully recapitulate the phenotypes of the corresponding isolates Sequence analysis of the evolution of the escape mutants suggested that the most relevant changes were likely to be in the V3 loop of the gp120 glycoprotein We therefore made a series of mutant viruses and found that full AD101 resistance was conferred by four amino acid changes in V3 Each change individually caused partial resistance when they were introduced into the V3 loop of a CC1/85 clone, but their impact was dependent on the gp120 context in which they were made We assume that these amino acid changes alter how the HIV-1 Env complex interacts with CCR5 Perhaps unexpectedly, given the complete dependence of the escape mutant on CCR5 for entry, monomeric gp120 proteins expressed from clones of the fully resistant isolate failed to bind to CCR5 on the surface of L12-CCR5 cells under conditions where gp120 proteins from the parental virus and a partially AD101-resistant virus bound strongly Hence, the full impact of the V3 substitutions may only be apparent at the level of the native Env complex

Genetic algorithms and artificial life

Abstract: Genetic algorithms are computational models of evolution that play a central role in many artificial-life models. We review the history and current scope of research on genetic algorithms in artificial life, giving illustrative examples in which the genetic algorithm is used to study how learning and evolution interact, and to model ecosystems, immune system, cognitive systems, and social systems. We also outline a number of open questions and future directions for genetic algorithms in artificial-life research.

Leverage Causes Fat Tails and Clustered Volatility

Abstract: We build a simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic financial strategy, called "value investing," i.e., systematically attempting to buy underpriced assets. When funds do not borrow, the price fluctuations of the asset are approximately normally distributed and uncorrelated across time. This changes when the funds are allowed to leverage, i.e., borrow from a bank, which allows them to purchase more assets than their wealth would otherwise permit. During good times funds that use more leverage have higher profits, increasing their wealth and making them dominant in the market. However, if a downward price fluctuation occurs while one or more funds are fully leveraged, the resulting margin call causes them to sell into an already falling market, amplifying the downward price movement. If the funds hold large positions in the asset this can cause substantial losses. This in turns leads to clustered volatility: Before a crash, when the value funds are dominant, they damp volatility, and after the crash, when they suffer severe losses, volatility is high. This leads to power law tails which are both due to the leverage-induced crashes and due to the clustered volatility induced by the wealth dynamics. This is in contrast to previous explanations of fat tails and clustered volatility, which depended on "irrational behavior," such as trend following. A standard (supposedly more sophisticated) risk control policy in which individual banks base leverage limits on volatility causes leverage to rise during periods of low volatility, and to contract more quickly when volatility gets high, making these extreme fluctuations even worse.