CAUSES OF ROBUSTNESS

Most species maintain abundant genetic variation and experience a range of environmental conditions, yet phenotypic variation is low.  That is, development is robust to changes in genotype and environment.  It has been claimed that this robustness, termed canalization, evolves due to long-term natural selection for optimal phenotypes.  We show that the developmental process, modeled as a network of interacting transcriptional regulators, constrains the genetic system to produce canalization, even without selection toward an optimum. The extent of canalization, measured as the   insensitivity to mutation of a network's equilibrium state, depends on the complexity of the network, such that more highly connected networks evolve to be more canalized. We argue that canalization may be an inevitable consequence of complex developmental-genetic processes, and thus requires no explanation in terms of evolution to suppress phenotypic variation.

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CONSEQUENCES OF ROBUSTNESS

The molecular chaperone Hsp90 has been termed an 'evolutionary capacitor' because it has been shown, in Drosophila and Arabidopsis, to: 1) suppress phenotypic variation under normal conditions and release this variation when functionally compromised, 2) have its function overwhelmed by environmental stress, and 3) exert pleiotropic effects on key developmental processes.  There is considerable debate whether these properties necessarily make Hsp90 a significant and unique facilitator of adaptation. Using numerical simulations of complex gene networks, as well as genome-scale expression data from yeast single-gene deletion strains, we study a novel mechanism that extends the scope of evolutionary capacitance well beyond the action of Hsp90 alone.  We illustrate that most, perhaps all, genes reveal phenotypic variation when functionally compromised. We also show, in the first demonstration of the plausibility of evolutionary capacitance, that the availability of loss-of-function mutations speeds adaptation to a new optimum phenotype.  However, this effect does not require the mutations to be conditional on the environment.  Thus, there may exist a large class of evolutionary capacitors whose effects on phenotypic variation complement the systemic, environment-induced effects of Hsp90.

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ON THE EVOLUTION OF GENE NETWORKS

We aim at understanding how evolution affects interacting genes of gene networks.The relationship between the topology of a biological network and its functional or evolutionary properties has attracted much recent interest.  It has been suggested that most, if not all, biological networks are 'scale free.'  That is, their connections follow power-law distributions, such that there are very few nodes with very many connections and vice versa.  The number of target genes of known transcriptional regulators in the yeast, Saccharomyces cerevisiae, appears to follow such a distribution, as do other networks, such as the yeast network of protein-protein interactions.  These findings have inspired attempts to draw biological inferences from general properties associated with scale-free network topology.  One often cited general property is that, when compromised; highly connected nodes will tend to have a larger effect on network function than sparsely connected nodes.  For example, more highly connected proteins are more likely to be lethal when knocked out.  However, the correlation between lethality and connectivity is relatively weak, and some highly connected proteins can be removed without noticeable phenotypic effect.  Similarly, network topology only weakly predicts the response of gene expression to environmental perturbations.  Evolutionary simulations of gene- regulatory networks suggest that such weak or nonexistent correlations are to be expected, and are likely not due to inadequacy of experimental data.  We argue that 'top-down' inferences of biological properties based on simple measures of network topology are of limited utility, and our simulation results suggesting that much more detailed information about a gene's location in a regulatory network, as well as dynamic gene-expression data, are needed to make more meaningful functional and evolutionary predictions.  Specifically, we find in our simulations that: 1) the relationship between a gene's connectivity and its fitness effect upon knockout depends on its equilibrium expression level; 2) correlation between connectivity and genetic variation is virtually nonexistent, yet upon independent evolution of networks with identical topologies, some nodes exhibit consistently low or high polymorphism; and 3) certain genes show low polymorphism yet high divergence among independent evolutionary runs.  This latter pattern is generally taken as a signature of positive selection, but in our simulations its cause is often neutral co-evolution of regulatory inputs to the same gene.

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EVOLUTION OF SEX DETERMINATION NETWORKS

Recent evidence indicates that an increase in the complexity of interactions has played an important role in gene network evolution. Sex determination mechanisms offer a good model for studying gene network evolution because, among other reasons, they evolve rapidly.  We are using a modelling approach to study network evolution, and are working on a hierarchical model which integrates two previously separate modelling techniques, from population genetics on the one hand, and network dynamics on the other. The theoretical model has been used to investigate the early evolution of sex determination networks. Following from a hypothesis proposed by A.S.Wilkins, we assume that sex determination networks have evolved in a retrograde manner from bottom to top. Starting from a simplest possible ancestral system, based on a single locus, we investigate how more complex systems, involving more loci, could have evolved. We are also investigating evolution between species for which sex determination is (at least partially) understood:  D. melanogaster, medfly, honeybee, domstic housefly, and silkworm.

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THE GENETIC HALLMARK OF HUMAN LONGEVITY

Recent data indicate that life span is strongly inherited in families with exceptional longevity. Despite this evidence for a substantial genetic component, the inherited biological factor(s) that define life span in long-lived humans remain unknown. To take advantage of the completion of the Human Genomic Project, and explosion in new biological data, we utilize multidisciplinary approach to study exceptional longevity in humans. We employ a combination of large-scale genomic studies (SNPs microarray), molecular data analysis on large data sets of genetic variation, and evolutionary modeling tools to probe into new genetic pathways for exceptional longevity in humans. The theory and methodologies developed for the study of evolution as the transition of genetic variation within a population into that of variation between populations in response to natural selection, "differential reproduction", over the course of many generations are applied to the study of changes in the genetic makeup of single population in response to "differential mortality" over the course of one, or two overlapping generation(s).

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ON THE EVOLUTION OF LANGUAGE AMBIGUITY

Among the greatest obstacles to the development of robust natural language technologies is the prevalence of ambiguity. When early toy natural language processing systems were scaled up to try to handle naturally occurring text, almost every sentence was found to be massively ambiguous. Words typically have multiple interpretations, and it is common for sentences to permit multiple alternative phrase structures. The combination of several such ambiguities in a sentence can lead to a combinatorial explosion of possible parses.
The pervasiveness of ambiguity in natural language is paradoxical. The more interpretations an expression has, the more possibility there is that the hearer's interpretation will not match the speaker's intention. Hence, ambiguity should hinder communication. Since languages are primarily media of communication, and since languages evolve, one would expect that ambiguity would eventually diminish over time. But this is evidently not the case, or languages would not be so massively ambiguous.
This project seeks to understand why languages are so ambiguous. Drawing on tools from evolutionary theory and population biology, we are developing and analyzing mathematical models of the evolution of communication with the goal of discovering under what conditions ambiguous language will emerge, spread, and be maintained. We build on earlier work employing an evolutionary approach to the study of language, but unlike most such work, we do not assume a one-to-one pairing of meanings with expressions.
Our study starts with a computational simulation comparing the evolution of a very simple ambiguous language to the evolution of a very simple unambiguous language. Although under most circumstances the unambiguous language dominates at the population level, the ambiguous language emerges when one meaning is used overwhelmingly often. This finding reflects actual word usage in English, as an analysis of WordNet reveals.

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