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.

Metabolic-network-driven analysis of bacterial ecological strategies

Abstract: Background: The growth-rate of an organism is an important phenotypic trait, directly affecting its ability to survive in a given environment. Here we present the first large scale computational study of the association between ecological strategies and growth rate across 113 bacterial species, occupying a variety of metabolic habitats. Genomic data are used to reconstruct the species' metabolic networks and habitable metabolic environments. These reconstructions are then used to investigate the typical ecological strategies taken by organisms in terms of two basic speciesspecific measures: metabolic variability - the ability of a species to survive in a variety of different environments; and co-habitation score vector - the distribution of other species that co-inhabit each environment. Results: We find that growth rate is significantly correlated with metabolic variability and the level of co-habitation (that is, competition) encountered by an organism. Most bacterial organisms adopt one of two main ecological strategies: a specialized niche with little co-habitation, associated with a typically slow rate of growth; or ecological diversity with intense co-habitation, associated with a typically fast rate of growth. Conclusions: The pattern observed suggests a universal principle where metabolic flexibility is associated with a need to grow fast, possibly in the face of competition. This new ability to produce a quantitative description of the growth rate-metabolism-community relationship lays a computational foundation for the study of a variety of aspects of the communal metabolic life.

Identification of clusters of investors from their real trading activity in a financial market

Abstract: We use statistically validated networks, a recently introduced method of validating links in a bipartite system, to identify clusters of investors trading in a financial market. Specifically, we investigate a special database allowing us to track the trading activity of individual investors of Nokia stock. We find that many statistically detected clusters of investors show a very high degree of synchronization in time when they decide to trade and in the trading action taken. We investigate the composition of these clusters and find that several of them show an over-expression of specific categories of investors.

Tranching, CDS, and Asset Prices: How Financial Innovation Can Cause Bubbles and Crashes

Abstract: We show how the timing of financial innovation might have contributed to the mortgage bubble and then to the crash of 2007–2009. We show why tranching and leverage first raised asset prices and why CDS lowered them afterward. This may seem puzzling, since it implies that creating a derivative tranche in the securitization whose payoffs are identical to the CDS will raise the underlying asset price, while the CDS outside the securitization lowers it. The resolution of the puzzle is that the CDS lowers the value of the underlying asset since it is equivalent to tranching cash. (JEL E32, E44, G01, G12, G13, G21).

Do variations in substitution rates and male mutation bias correlate with life-history traits? A study of 32 mammalian genomes

Abstract: Life-history traits vary substantially across species, and have been demonstrated to affect substitution rates. We compute genome-wide, branch-specific estimates of male mutation bias (the ratio of male-to-female mutation rates) across 32 mammalian genomes and study how these vary with life-history traits (generation time, metabolic rate, and sperm competition). We also investigate the influence of life-history traits on substitution rates at unconstrained sites across a wide phylogenetic range. We observe that increased generation time is the strongest predictor of variation in both substitution rates (for which it is a negative predictor) and male mutation bias (for which it is a positive predictor). Although less significant, we also observe that estimates of metabolic rate, reflecting replication-independent DNA damage and repair mechanisms, correlate negatively with autosomal substitution rates, and positively with male mutation bias. Finally, in contrast to expectations, we find no significant correlation between sperm competition and either autosomal substitution rates or male mutation bias. Our results support the important but frequently opposite effects of some, but not all, life-history traits on substitution rates.

The regulatory network of E. coli metabolism as a Boolean dynamical system exhibits both homeostasis and flexibility of response

Abstract: Elucidating the architecture and dynamics of large scale genetic regulatory networks of cells is an important goal in systems biology. We study the system level dynamical properties of the genetic network of Escherichia coli that regulates its metabolism, and show how its design leads to biologically useful cellular properties. Our study uses the database (Covert et al., Nature 2004) containing 583 genes and 96 external metabolites which describes not only the network connections but also the Boolean rule at each gene node that controls the switching on or off of the gene as a function of its inputs.