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.

Advantage of rare HLA supertype in HIV disease progression.

Abstract: The highly polymorphic human leukocyte antigen (HLA) class I molecules help to determine the specificity and repertoire of the immune response. The great diversity of these antigen-binding molecules confers differential advantages in responding to pathogens, but presents a major obstacle to distinguishing HLA allele-specific effects. HLA class I supertypes provide a functional classification for the many different HLA alleles that overlap in their peptide-binding specificities. We analyzed the association of these discrete HLA supertypes with HIV disease progression rates in a population of HIV-infected men. We found that HLA supertypes alone and in combination conferred a strong differential advantage in responding to HIV infection, independent of the contribution of single HLA alleles that associate with progression of the disease. The correlation of the frequency of the HLA supertypes with viral load suggests that HIV adapts to the most frequent alleles in the population, providing a selective advantage for those individuals who express rare alleles.

Distributed biological computation with multicellular engineered networks

Abstract: Ongoing efforts within synthetic and systems biology have been directed towards the building of artificial computational devices using engineered biological units as basic building blocks. Such efforts, inspired in the standard design of electronic circuits, are limited by the difficulties arising from wiring the basic computational units (logic gates) through the appropriate connections, each one to be implemented by a different molecule. Here, we show that there is a logically different form of implementing complex Boolean logic computations that reduces wiring constraints thanks to a redundant distribution of the desired output among engineered cells. A practical implementation is presented using a library of engineered yeast cells, which can be combined in multiple ways. Each construct defines a logic function and combining cells and their connections allow building more complex synthetic devices. As a proof of principle, we have implemented many logic functions by using just a few engineered cells. Of note, small modifications and combination of those cells allowed for implementing more complex circuits such as a multiplexer or a 1-bit adder with carry, showing the great potential for re-utilization of small parts of the circuit. Our results support the approach of using cellular consortia as an efficient way of engineering complex tasks not easily solvable using single-cell implementations.

Evolutionary rescue: an emerging focus at the intersection between ecology and evolution

Abstract: There is concern that the rate of environmental change is now exceeding the capacity of many populations to adapt. Mitigation of biodiversity loss requires science that integrates both ecological and evolutionary responses of populations and communities to rapid environmental change, and can identify the conditions that allow the recovery of declining populations. This special issue focuses on evolutionary rescue (ER), the idea that evolution might occur sufficiently fast to arrest population decline and allow population recovery before extinction ensues. ER emphasizes a shift to a perspective on evolutionary dynamics that focuses on short time-scales, genetic variants of large effects and absolute rather than relative fitness. The contributions in this issue reflect the state of field; the articles address the latest conceptual developments, and report novel theoretical and experimental results. The examples in this issue demonstrate that this burgeoning area of research can inform problems of direct practical concern, such as the conservation of biodiversity, adaptation to climate change and the emergence of infectious disease. The continued development of research on ER will be necessary if we are to understand the extent to which anthropogenic global change will reduce the Earth's biodiversity.

The Predictive Power of Zero Intelligence in Financial Markets

Abstract: Standard models in economics stress the role of intelligent agents who maximize utility. However, there may be situations where, for some purposes, constraints imposed by market institutions dominate intelligent agent behavior. We use data from the London Stock Exchange to test a simple model in which zero intelligence agents place orders to trade at random. The model treats the statistical mechanics of order placement, price formation, and the accumulation of revealed supply and demand within the context of the continuous double auction, and yields simple laws relating order arrival rates to statistical properties of the market. We test the validity of these laws in explaining the cross-sectional variation for eleven stocks. The model explains 96% of the variance of the bid-ask spread, and 76% of the variance of the price diffusion rate, with only one free parameter. We also study the market impact function, describing the response of quoted prices to the arrival of new orders. The non-dimensional coordinates dictated by the model approximately collapse data from different stocks onto a single curve. This work is important from a practical point of view because it demonstrates the existence of simple laws relating prices to order flows, and in a broader context, because it suggests that there are circumstances where institutions are more important than strategic considerations.

Default and Punishment in General Equilibrium

Abstract: We extend the standard model of general equilibrium with incomplete markets to allow for default and punishment by thinking of assets as pools. The equilibrating variables include expected delivery rates, along with the usual prices of assets and commodities. By reinterpreting the variables, our model encompasses a broad range of adverse selection and signalling phenomena in a perfectly competitive, general equilibrium framework. Perfect competition eliminates the need for lenders to compute how the size of their loan or the price they quote might affect default rates. It also makes for a simple equilibrium refinement, which we propose in order to rule out irrational pessimism about deliveries of untraded assets. We show that refined equilibrium always exists in our model, and that default, in conjunction with refinement, opens the door to a theory of endogenous assets. The market chooses the promises, default penalties, and quantity constraints of actively traded assets.