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.

Evolving Networks with distance preferences

Abstract: We study evolving networks where new nodes when attached to the network form links with other nodes of preferred distances. A particular case is where always the shortest distances are selected ("make friends with the friends of your present friends"). We present simulation results for network parameters like the first eigenvalue of the graph Laplacian (synchronizability), clustering coefficients, average distances, and degree distributions for different distance preferences and compare them with the parameter values for random and scale-free networks. We find that for the shortest distance rule we obtain a power-law degree distribution as in scale-free networks, while the other parameters are significantly different, especially the clustering coefficient.

Protein robustness promotes evolutionary innovations on large evolutionary time-scales.

Abstract: Recent laboratory experiments suggest that a molecule's ability to evolve neutrally is important for its ability to generate evolutionary innovations. In contrast to laboratory experiments, life unfolds on time-scales of billions of years. Here, we ask whether a molecule's ability to evolve neutrally—a measure of its robustness—facilitates evolutionary innovation also on these large time-scales. To this end, we use protein designability, the number of sequences that can adopt a given protein structure, as an estimate of the structure's ability to evolve neutrally. Based on two complementary measures of functional diversity—catalytic diversity and molecular functional diversity in gene ontology—we show that more robust proteins have a greater capacity to produce functional innovations. Significant associations among structural designability, folding rate and intrinsic disorder also exist, underlining the complex relationship of the structural factors that affect protein evolution.

Evolution of learning in fluctuating environments: when selection favors both social and exploratory individual learning.

Abstract: Cumulative cultural change requires organisms that are capable of both exploratory individual learning and faithful social learning. In our model, an organism's phenotype is initially determined innately (by its genotypic value) or by social learning (copying a phenotype from the parental generation), and then may or may not be modified by individual learning (exploration around the initial phenotype). The environment alternates periodically between two states, each defined as a certain range of phenotypes that can survive. These states may overlap, in which case the same phenotype can survive in both states, or they may not. We find that a joint social and exploratory individual learning strategy-the strategy that supports cumulative culture-is likely to spread when the environmental states do not overlap. In particular, when the environmental states are contiguous and mutation is allowed among the genotypic values, this strategy will spread in either moderately or highly stable environments, depending on the exact nature of the individual learning applied. On the other hand, natural selection often favors a social learning strategy without exploration when the environmental states overlap. We find only partial support for the "consensus" view, which holds that individual learning, social learning, and innate determination of behavior will evolve at short, intermediate, and long environmental periodicities, respectively.

Cancer research meets evolutionary biology

Abstract: There is increasing evidence that Darwin’s theory of evolution by natural selection provides insights into the etiology and treatment of cancer. On a microscopic scale, neoplastic cells meet the conditions for evolution by Darwinian selection: cell reproduction with heritable variability that affects cell survival and replication. This suggests that, like other areas of biological and biomedical research, Darwinian theory can provide a general framework for understanding many aspects of cancer, including problems of great clinical importance. With the availability of raw molecular data increasing rapidly, this theory may provide guidance in translating data into understanding and progress. Several conceptual and analytical tools from evolutionary biology can be applied to cancer biology. Two clinical problems may benefit most from the application of Darwinian theory: neoplastic progression and acquired therapeutic resistance. The Darwinian theory of cancer has especially profound implications for drug development, both in terms of explaining past difficulties, and pointing the way toward new approaches. Because cancer involves complex evolutionary processes, research should incorporate both tractable (simplified) experimental systems, and also longitudinal observational studies of the evolutionary dynamics of cancer in laboratory animals and in human patients. Cancer biology will require new tools to control the evolution of neoplastic cells.

Dating Shuidonggou and the Upper Palaeolithic blade industry in North China

Abstract: Shuidonggou is unique within the Chinese Palaeolithic sequence and its assemblage is reminiscent of Upper Palaeolithic core-and-blade technologies in Mongolia and southern Siberia. Limited chronological controls have prevented evaluation of this technology in both the Chinese and greater Eurasian Palaeolithic. Dating of recently discovered hearths at Locality 2 places Shuidonggou firmly at 29,000–24,000 BP, and suggests the spread of the Eurasian large blade technology was primarily from north to south. The concurrent production of small microblade-like bipolar bladelets at the site may also presage the development of a microlithic industry.