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Kimura [1955, 1956] partially solved the problem of finding the transition density function which describes the behavior of a diffusion model for evolution at one genetic locus with many neutral alleles. We complete the solution using a system of polynomials biorthogonal to polynomials suggested by Appell [1881] as generalizations of Jacobi polynomials.
Despite the relevant role of models of nucleotide substitution in phylogenetics, choosing among different models remains a problem. Several statistical methods for selecting the model that best fits the data at hand have been proposed, but their absolute and relative performance has not yet been characterized. In this study, we compare under various conditions the performance of different hierarchical and dynamic likelihood ratio tests, and of Akaike and Bayesian information methods, for selecting best-fit models of nucleotide substitution. We specifically examine the role of the topology used to estimate the likelihood of the different models and the importance of the order in which hypotheses are tested. We do this by simulating DNA sequences under a known model of nucleotide substitution and recording how often this true model is recovered by the different methods. Our results suggest that model selection is reasonably accurate and indicate that some likelihood ratio test methods perform overall better than the Akaike or Bayesian information criteria. The tree used to estimate the likelihood scores does not influence model selection unless it is a randomly chosen tree. The order in which hypotheses are tested, and the complexity of the initial model in the sequence of tests, influence model selection in some cases. Model fitting in phylogenetics has been suggested for many years, yet many authors still arbitrarily choose their models, often using the default models implemented in standard computer programs for phylogenetic estimation. We show here that a best-fit model can be readily identified. Consequently, given the relevance of models, model fitting should be routine in any phylogenetic analysis that uses models of evolution.
Computer simulation of the population dynamics of the genomic patterns of polygenes, transposable elements (TEs), and origin identity labels (OILs) in the course of negative selection for an additive quantitative trait has been performed. It was demonstrated that active polygene alleles disappear very rapidly, whereas the patterns of TEs and OILs continue their evolution determined by strict selective inbreeding and gene drift. Dendrograms of the patterns of polygenes, TEs, and OILs were constructed for all generations. It was demonstrated that the final consensus pattern of OILs consists of the fragments of the original patterns, which contain neither active polygene alleles nor modifier or marker TEs. Neutral TE copies were present in the final pattern, as should be expected in the case of gene drift. Inbreeding coefficient increased steadily but by generation 100 reached values higher than 0.9. All other parameters and initial conditions being the same, the responses to negative and positive selections were asymmetric.
We describe the need for a Foundational Model of Physiology (FMP) as a reference ontology for "functional bioinformatics". The FMP is intended to support symbolic lookup, logical inference and mathematical analysis by integrating descriptive, qualitative and quantitative functional knowledge. The FMP will serve as a symbolic representation of biological functions initially pertaining to human physiology and ultimately extensible to other species. We describe the evolving architecture of the FMP, which is based on the ontological principles of the BioD biological description language and the Foundational Model of Anatomy (FMA).
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The emergence and spread of antibiotic resistance among bacteria make use of the same strategies and obey the same laws as natural evolution but the process is oriented and accelerated.
The simplest conceivable event that can occur at the gene level can result in the development of efficacious resistance tot antibiotics. The bacterial world behaves as an enormous organism whose cells can exchange their genes very easily. Accordingly, opportunities for the exchange of genetic material in nature are probably unlimited. This knowledge cannot be ignored. It leads to the important conclusion that the antibiotics are societal drugs. A resistance gene which has appeared somewhere in the world can travel far and fast.
Nursery pollinators, and the plants they use as hosts for offspring development, function as exemplary models of coevolutionary mutualism. The two pre-eminent examples--fig wasps and yucca moths--show little variation in the interaction: the primary pollinator is an obligate mutualist. By contrast, nursery pollination of certain Caryophyllaceae, including Silene spp., by two nocturnal moth genera, Hadena and Perizoma, ranges from antagonistic to potentially mutualistic, offering an opportunity to test hypotheses about the factors that promote or discourage the evolution of mutualism. Here, we review nursery pollination and host-plant interactions in over 30 caryophyllaceous plants, based on published studies and a survey of researchers investigating pollination, seed predation, and moth morphology and behavior. We detected little direct evidence of mutualism in these moth-plant interactions, but found traits and patterns in both that are nonetheless consistent with the evolution of mutualism and merit further attention.
A simple model of gene conversion presented earlier has been compared with a more general model under the assumption of no bias. The former is based on a simple concept that, by conversion, a gene (or a segment of a gene) is transferred from one locus to another in a multigene family. Intrachromatid symmetric conversion may result in reciprocal exchange of genes within a multigene family, and the previous theory of identity coefficients needs modification when it occurs. The relationship of various parameters of the two models was made clear in formulating the transition equations of identity coefficients. As an example of extending the analyses, a model of preferential conversion that is relevant to the observed uneven polymorphisms of class I genes in the major histocompatibility complex has been investigated. It differs from the biased conversion, in that directional process is through location of genes on the chromosome. It is shown that the model satisfactorily explains the observed data, even though the effect of preferential conversion is difficult to distinguish from that of natural selection.
We model the evolution of plant mating systems under the joint effects of pollen discounting and pollen limitation, using a dynamic model of inbreeding depression, allowing for partial purging of recessive lethal mutations by selfing. Stable mixed mating systems occur for a wide range of parameter values with pollen discounting alone. However, when typical levels of pollen limitation are combined with pollen discounting, stable selfing rates are always high but less than 1 (0.9<s<1 in most cases); in this situation, complete selfing does not evolve because pollen discounting becomes very large at high selfing rates, so that the automatic advantage of selfing changes to a disadvantage. These results suggest that mixed mating systems with high selfing rates can be maintained by selection, whereas mixed mating systems with low to moderate selfing rates are more likely attributable to unavoidable geitonogamous selfing.
We describe a simple theoretical framework for identifying orthologous sets of genes that deviate from a clock-like model of evolution. The approach used is based on comparing the evolutionary distances within a set of orthologs to a standard intergenomic distance, which was defined as the median of the distribution of the distances between all one-to-one orthologs. Under the clock-like model, the points on a plot of intergenic distances versus intergenomic distances are expected to fit a straight line. A statistical technique to identify significant deviations from the clock-like behavior is described. For several hundred analyzed orthologous sets representing three well-defined bacterial lineages, the alpha-Proteobacteria, the gamma-Proteobacteria, and the Bacillus-Clostridium group, the clock-like null hypothesis could not be rejected for approximately 70% of the sets, whereas the rest showed substantial anomalies. Subsequent detailed phylogenetic analysis of the genes with the strongest deviations indicated that over one-half of these genes probably underwent a distinct form of horizontal gene transfer, xenologous gene displacement, in which a gene is displaced by an ortholog from a different lineage. The remaining deviations from the clock-like model could be explained by lineage-specific acceleration of evolution. The results indicate that although xenologous gene displacement is a major force in bacterial evolution, a significant majority of orthologous gene sets in three major bacterial lineages evolved in accordance with the clock-like model. The approach described here allows rapid detection of deviations from this mode of evolution on the genome scale.
The time evolution of a bistable Ginzburg-Landau model (GL) with a non-Markovian memory term of strength lambda is studied. Due to the nonlinear feedback coupling, the two branches of the stationary solution are not only controlled by the sign of the initial condition P(0), but also by the strength and the sign of lambda. Whereas in case of a positive lambda the stationary solution is ever reduced through the memory, it may be increasing for lambda<0. In that case the system is also able to switch over between both branches of the stationary solution. Such an ability is exclusively achieved for a negative lambda within an interval -u<lambda<lambda(c), where lambda(c) is a critical memory strength and u is the strength of the conventional nonlinear term within the GL. The complete phase diagram is presented in the P(0)-lambda plane analytically and numerically.
Markovian models of protein evolution that relax the assumption of independent change among codons are considered. With this comparatively realistic framework, an evolutionary rate at a site can depend both on the state of the site and on the states of surrounding sites. By allowing a relatively general dependence structure among sites, models of evolution can reflect attributes of tertiary structure. To quantify the impact of protein structure on protein evolution, we analyze protein-coding DNA sequence pairs with an evolutionary model that incorporates effects of solvent accessibility and pairwise interactions among amino acid residues. By explicitly considering the relationship between nonsynonymous substitution rates and protein structure, this approach can lead to refined detection and characterization of positive selection. Analyses of simulated sequence pairs indicate that parameters in this evolutionary model can be well estimated. Analyses of lysozyme c and annexin V sequence pairs yield the biologically reasonable result that amino acid replacement rates are higher when the replacements lead to energetically favorable proteins than when they destabilize the proteins. Although the focus here is evolutionary dependence among codons that is associated with protein structure, the statistical approach is quite general and could be applied to diverse cases of evolutionary dependence where surrogates for sequence fitness can be measured or modeled.
We extend an earlier model of protein evolution on a rugged landscape to the case in which the landscape exhibits a variable degree of correlation (i.e., smoothness). Correlation is introduced by assuming that a protein is composed of a set of independent blocks or domains and that mutation in one block affects the contribution of that block alone to the overall fitness of the protein. We study the statistical structure of such landscapes and apply our theory to the evolution by somatic hypermutation of antibody molecules composed of framework and complementarity-determining regions. We predict the expected number of replacement mutations in each region.
The standard model of carcinogenesis is currently being questioned. The main controversy concerns genetic instability and has links to fundamental questions in evolutionary biology. This paper aims to clarify the underlying conflict between the linear configuration of the standard model and the non-linear dynamics of Darwinian evolution. It addresses the problem of applying the concept of clonal selection to genetically unstable cells and presents an alternative perspective based on the principles of molecular evolution. This model explains genetic instability in terms of competition between genetic strategies and draws lines to basic aspects of evolutionary biology.
Covarion models of molecular evolution allow the rate of evolution of a site to vary through time. There are few simple and effective tests for covarion evolution, and consequently, little is known about the presence of covarion processes in molecular evolution. We describe two new tests for covarion evolution and demonstrate with simulations that they perform well under a wide range of conditions. A survey of covarion evolution in sequenced plastid genomes found evidence of covarion drift in at least 26 out of 57 genes. Covarion evolution is most evident in first and second codon positions of the plastid genes, and there is no evidence of covarion evolution in third codon positions. Therefore, the significant covarion tests are likely due to changes in the selective constraints of amino acids. The frequency of covarion evolution within the plastid genome suggests that covarion processes of evolution were important in generating the observed patterns of sequence variation among plastid genomes.