Stephen C. Pratt

Genome-Wide Detection of Polymorphisms at Nucleotide Resolution with a Single DNA Microarray

A central challenge of genomics is to detect, simply and inexpensively, all differences in sequence among the genomes of individual members of a species. We devised a system to detect all single-nucleotide differences between genomes with the use of data from a single hybridization to a whole-genome DNA microarray. This allowed us to detect a variety of spontaneous single–base pair substitutions, insertions, and deletions, and most (>90%) of the ∼30,000 known single-nucleotide polymorphisms between two Saccharomyces cerevisiae strains. We applied this approach to elucidate the genetic basis of phenotypic variants and to identify the small number of single–base pair changes accumulated during experimental evolution of yeast.

Population genomic analysis of outcrossing and recombination in yeast

The budding yeast Saccharomyces cerevisiae has been used by humans for millennia to make wine, beer and bread. More recently, it became a key model organism for studies of eukaryotic biology and for genomic analysis. However, relatively little is known about the natural lifestyle and population genetics of yeast. One major question is whether genetically diverse yeast strains mate and recombine in the wild. We developed a method to infer the evolutionary history of a species from genome sequences of multiple individuals and applied it to whole-genome sequence data from three strains of Saccharomyces cerevisiae and the sister species Saccharomyces paradoxus. We observed a pattern of sequence variation among yeast strains in which ancestral recombination events lead to a mosaic of segments with shared genealogy. Based on sequence divergence and the inferred median size of shared segments (∼2,000 bp), we estimated that although any two strains have undergone approximately 16 million cell divisions since their last common ancestor, only 314 outcrossing events have occurred during this time (roughly one every 50,000 divisions). Local correlations in polymorphism rates indicate that linkage disequilibrium in yeast should extend over kilobases. Our results provide the initial foundation for population studies of association between genotype and phenotype in S. cerevisiae.

Exact Multipoint Quantitative-Trait Linkage Analysis in Pedigrees by Variance Components

Methods based on variance components are powerful tools for linkage analysis of quantitative traits, because they allow simultaneous consideration of all pedigree members. The central idea is to identify loci making a significant contribution to the population variance of a trait, by use of allele-sharing probabilities derived from genotyped marker loci. The technique is only as powerful as the methods used to infer these probabilities, but, to date, no implementation has made full use of the inheritance information in mapping data. Here we present a new implementation that uses an exact multipoint algorithm to extract the full probability distribution of allele sharing at every point in a mapped region. At each locus in the region, the program fits a model that partitions total phenotypic variance into components due to environmental factors, a major gene at the locus, and other unlinked genes. Numerical methods are used to derive maximum-likelihood estimates of the variance components, under the assumption of multivariate normality. A likelihood-ratio test is then applied to detect any significant effect of the hypothesized major gene. Simulations show the method to have greater power than does traditional sib-pair analysis. The method is freely available in a new release of the software package GENEHUNTER.

A tunable algorithm for collective decision-making

Complex biological systems are increasingly understood in terms of the algorithms that guide the behavior of system components and the information pathways that link them. Much attention has been given to robust algorithms, or those that allow a system to maintain its functions in the face of internal or external perturbations. At the same time, environmental variation imposes a complementary need for algorithm versatility, or the ability to alter system function adaptively as external circumstances change. An important goal of systems biology is thus the identification of biological algorithms that can meet multiple challenges rather than being narrowly specified to particular problems. Here we show that emigrating colonies of the ant Temnothorax curvispinosus tune the parameters of a single decision algorithm to respond adaptively to two distinct problems: rapid abandonment of their old nest in a crisis and deliberative selection of the best available new home when their old nest is still intact. The algorithm uses a stepwise commitment scheme and a quorum rule to integrate information gathered by numerous individual ants visiting several candidate homes. By varying the rates at which they search for and accept these candidates, the ants yield a colony-level response that adaptively emphasizes either speed or accuracy. We propose such general but tunable algorithms as a design feature of complex systems, each algorithm providing elegant solutions to a wide range of problems.

A modelling framework for understanding social insect foraging

Abstract. The foraging of an insect society is a complex process involving large numbers of individuals collecting food from many different sources. Differential equation models have shown how quite simple communication mechanisms can produce complex and functional group-level foraging patterns. In this paper we review previous differential equation models for pheromone trails, honey bee dances and other methods of communication used during foraging. We develop a general framework for modelling social insect foraging systems that incorporates each of the previous models. This framework identifies the different behaviours that insects undertake while foraging, along with generalised rate functions that determine how the insects switch between behaviours. We describe how to tailor our framework to specific insect societies, by incorporating the details of specific behavioural mechanisms into appropriate expressions for rates of discovery of, recruitment to, and retirement from food sources. Our framework thus provides an experimental tool for improved understanding of the foraging behaviour of particular species, as well as a system for meaningful comparisons of foraging behaviour across species. We end this article by linking our framework to inclusive fitness theory. We demonstrate how understanding of the proximate mechanisms involved in social insect foraging ultimately furthers understanding, not only of how insect societies function, but also of how these mechanisms are used to optimise colony fitness and survival.

An agent-based model of collective nest choice by the ant Temnothorax albipennis

Colonies of the ant Temnothorax (formerly Leptothorax) albipennis can collectively choose the best of several nest sites, even when many of the active ants who organize the move visit only one site ...

Bio-Inspired Group Behaviors for the Deployment of a Swarm of Robots to Multiple Destinations

We present a methodology for characterizing and synthesizing swarm behaviors using both a macroscopic model that represents a swarm as a continuum and a microscopic model that represents individual robots. We develop a systematic approach for synthesizing behaviors at the macroscopic level that can be realized on individual robots at the microscopic level. Our methodology is inspired by a dynamical model of ant house hunting [1], a decentralized process in which a colony attempts to emigrate to the best site among several alternatives. The model is hybrid because the colony switches between different sets of behaviors, or modes, during this process. At the macroscopic level, we are able to synthesize controllers that result in the deployment of a robotic swarm in a predefined ratio between distinct sites. We then derive hybrid controllers for individual robots using only local interactions and no communication that respect the specifications of the global continuous behavior. Our simulations demonstrate that our synthesis procedure yields a correct microscopic model from the macroscopic description with guarantees on performance at both levels

Ant colonies outperform individuals when a sensory discrimination task is difficult but not when it is easy.

“Collective intelligence” and “wisdom of crowds” refer to situations in which groups achieve more accurate perception and better decisions than solitary agents. Whether groups outperform individuals should depend on the kind of task and its difficulty, but the nature of this relationship remains unknown. Here we show that colonies of Temnothorax ants outperform individuals for a difficult perception task but that individuals do better than groups when the task is easy. Subjects were required to choose the better of two nest sites as the quality difference was varied. For small differences, colonies were more likely than isolated ants to choose the better site, but this relationship was reversed for large differences. We explain these results using a mathematical model, which shows that positive feedback between group members effectively integrates information and sharpens the discrimination of fine differences. When the task is easier the same positive feedback can lock the colony into a suboptimal choice. These results suggest the conditions under which crowds do or do not become wise.

Groups have a larger cognitive capacity than individuals

Increasing the number of options can paradoxically lead to worse decisions, a phenomenon known as cognitive overload [1]. This happens when an individual decision-maker attempts to digest information exceeding its processing capacity. Highly integrated groups, such as social insect colonies, make consensus decisions that combine the efforts of many members, suggesting that these groups can overcome individual limitations [2-4]. Here we report that an ant colony choosing a new nest site is less vulnerable to cognitive overload than an isolated ant making this decision on her own. We traced this improvement to differences in individual behavior. In whole colonies, each ant assesses only a small subset of available sites, and the colony combines their efforts to thoroughly explore all options. An isolated ant, on the other hand, must personally assess a larger number of sites to approach the same level of option coverage. By sharing the burden of assessment, the colony avoids overtaxing the abilities of its members.

Efficiency and regulation of recruitment during colony emigration by the ant Temnothorax curvispinosus

Recruitment helps insect societies by bringing individuals to places where work needs to be done, but it also imposes energetic and opportunity costs. The net effect depends both on recruitment efficiency and on the ease with which insects can find work sites on their own. This study examined both of these factors for colony emigration by the ant Temnothorax curvispinosus. Emigrations were organized by a corps of active ants who transported the rest of the colony. These active ants either found new sites independently or followed tandem runs led by successful scouts. Although most tandem runs broke apart before reaching their target, even lost followers found the new site faster than did unguided searchers. When the new site was near the old nest, tandem runs were rare and summoned only a small proportion of the transporter corps. When the new site was instead distant and inconspicuous, tandem runs were common and brought roughly one third of the transporters. This pattern likely results from the quorum rule used by individual scouts to decide when to switch from tandem runs to transports. By monitoring how many nestmates have already found the nest, the ants ensure that the costs of recruitment are born only when necessary.