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Evolutionary algorithms and a fractal inverse problem.

Over the past 30 years, algorithms that model natural evolution have generated robust search methods. These so-called evolutionary algorithms have been successfully applied to a wide range of problems. This paper discusses two types of evolutionary algorithms and their application to a problem in shape representation. Genetic algorithms and evolutionary programming, although both based on evolutionary principles, each place different emphasis on what drives the evolutionary process. While genetic algorithms rely on mimicking specific genotypic transformations, evolutionary programming emphasizes phenotypic adaptation. Results presented show the success of evolutionary programming in solving an example of a fractal inverse problem, but indicate that a genetic algorithm is not as successful. Reasons for this disparity are discussed.

Algorithms↗

Opioid research in amphibians: an alternative pain model yielding insights on the evolution of opioid receptors.

This review summarizes the work from our laboratory investigating mechanisms of opioid analgesia using the Northern grass frog, Rana pipiens. Over the last dozen years, we have accumulated data on the characterization of behavioral effects after opioid administration on radioligand binding by using opioid agonist and antagonist ligands in amphibian brain and spinal cord homogenates, and by cloning and sequencing opioid-like receptor cDNA from amphibian central nervous system (CNS) tissues. The relative analgesic potency of mu, delta, and kappa opioids is highly correlated between frogs and other mammals, including humans. Radioligand binding studies using selective opioid agonists show a similar selectivity profile in amphibians and mammals. In contrast, opioid antagonists that are highly selective for mammalian mu, delta, and kappa opioid receptors were not selective in behavioral and binding studies in amphibians. Three opioid-like receptor cDNAs were cloned and sequenced from amphibian brain tissues and are orthologs to mammalian mu, delta, and kappa opioid receptors. Bioinformatics analysis of the three types of opioid receptor cDNAs from all vertebrate species with full datasets gave a pattern of the molecular evolution of opioid receptors marked by the divergence of mu, delta, and kappa opioid receptor sequences during vertebrate evolution. This divergence in receptor amino acid sequence in later-evolved vertebrates underlies the hypothesis that opioid receptors are more type-selective in mammals than in nonmammalian vertebrates. The apparent order of receptor type evolution is kappa, then delta, and, most recently, the mu opioid receptor. Finally, novel bioinformatics analyses suggest that conserved extracellular receptor domains determine the type selectivity of vertebrate opioid receptors.

Amphibians↗

Ancestral inference on gene trees under selection.

The extent to which natural selection shapes diversity within populations is a key question for population genetics. Thus, there is considerable interest in quantifying the strength of selection. A full likelihood approach for inference about selection at a single site within an otherwise neutral fully linked sequence of sites is described here. A coalescent model of evolution is used to model the ancestry of a sample of DNA sequences which have the selected site segregating. The mutation model, for the selected and neutral sites, is the infinitely many-sites model where there is no back or parallel mutation at sites. A unique perfect phylogeny, a gene tree, can be constructed from the configuration of mutations on the sample sequences under this model of mutation. The approach is general and can be used for any bi-allelic selection scheme. Selection is incorporated through modelling the frequency of the selected and neutral allelic classes stochastically back in time, then using a subdivided population model considering the population frequencies through time as variable population sizes. An importance sampling algorithm is then used to explore over coalescent tree space consistent with the data. The method is applied to a simulated data set and the gene tree presented in Verrelli et al. (2002).

Genetic Variation↗

Flavonoids: a colorful model for the regulation and evolution of biochemical pathways.

For more than a century, the biosynthesis of flavonoid pigments has been a favorite of scientists to study a wide variety of biological processes, such as inheritance and transposition, and has become one of the best-studied pathways in nature. The analysis of pigmentation continues to provide insights into new areas, such as the channeling and intracellular transport of metabolites, regulation of gene expression and RNA interference. Moreover, because pigmentation is studied in a variety of species, it provides unique molecular insights into the evolution of biochemical pathways and regulatory networks.

Biological Evolution↗

Maladaptation and mass effects in a metacommunity: consequences for species coexistence.

Metacommunity theories predict multispecies coexistence based on the interplay between local species interactions and regional migration. To date, most metacommunity models implicitly assume that evolution can be ignored. Yet empirical studies indicate a substantial potential for contemporary evolution. I evaluate how evolution alters species diversity in a simulated mass-effects (sink-source) metacommunity. Populations inhabiting source habitats became locally adapted, while subordinate competitors became maladapted because of assumed ecological and phenotypic trade-offs between habitats. This maladaptation decreased and leveled relative abundances among subordinate populations. These two effects produced two regions of departure from nonevolutionary predictions. Assuming low proportional migration, maladaptation reduced local species richness via an overall reduction in reproductive rates in sink populations. With intermediate proportional migration, a greater absolute reduction of reproductive rates in intermediate competitors leveled reproductive rates and thereby enhanced local species richness. Although maladaptation is usually viewed as a constraint on species coexistence, simulations suggest that its effects on diversity are manifold and dependent on interpatch migration and community context. Hence, metacommunity predictions often may profit from an evolutionary perspective. Results indicate that modifications of community connectivity, such as might occur during habitat fragmentation, could elicit rapid shifts in communities from regions of high to low biodiversity.

Adaptation, Physiological↗

Effects on NaeI-DNA recognition of the leucine to lysine substitution that transforms restriction endonuclease NaeI to a topoisomerase: a model for restriction endonuclease evolution.

Substituting lysine for leucine at position 43 (L43K) transforms NaeI from restriction endonuclease to topoisomerase and makes NaeI hypersensitive to intercalative anticancer drugs. Here we investigated DNA recognition by Nael-L43K. Using DNA competition and gel retardation assays, NaeI-L43K showed reduced affinity for DNA substrate and the ability to bind both single- and double-stranded DNA with a definite preference for the former. Sedimentation studies showed that under native conditions NaeI-L43K, like NaeI, is a dimer. Introduction of mismatched bases into double-stranded DNA significantly increased that DNA's ability to inhibit NaeI-L43K. Wild-type NaeI showed no detectable binding of either single-stranded DNA or mismatched DNA over the concentration range studied. These results demonstrate that the L43K substitution caused a significant change in recognition specificity by NaeI and imply that NaeI-L43K's topoisomerase activity is related to its ability to bind single-stranded and distorted regions in DNA. A mechanism is proposed for the evolution of the NaeI restriction-modification system from a topoisomerase/ligase by a mutation that abolished religation activity and provided a needed change in DNA recognition.

DNA↗

An innovative model for restraint use at the Philadelphia Veterans Affairs Medical Center.

In this article, Quality Management Specialists at the Philadelphia Veterans Affairs Medical Center describe an innovative model for restraint use designed to ensure patient safety. They discuss the impetus of the model, its evolution, and purposes. The model provides alternatives to restraint use, education to staff, patients, and families, and electronic tools to monitor restraint usage. Descriptive statistics and analyses of outcomes generated from the electronic tools are presented to demonstrate the utility and value of the model. Future directions for the use of the model and data generated through the electronic record are highlighted.

Adult↗

Cnidarians as a model system for understanding evolution and regeneration.

Hydra and Podocolyne are two cnidarian animals which provide complementary advantages for analysing developmental mechanisms possibly reflecting the basic developmental processes shared by most bilaterians. Interestingly, these mechanisms remain accessible all along the life of these animals, which bud and regenerate, whatever their age. The Hydra polyp permits a direct study of the molecular cascades linking amputation to regeneration. Podocoryne displays a complete life cycle, polyp and medusa stages with a fast and inducible sexual cycle and an unparalleled In vitro transdifferentiation potential. In both cases, a large number of evolutionarily conserved molecular markers are available, and analysis of their regulation highlights the molecular mechanisms which underly pattern formation in these two species.

Animals↗

Disk-covering, a fast-converging method for phylogenetic tree reconstruction.

The evolutionary history of a set of species is represented by a phylogenetic tree, which is a rooted, leaf-labeled tree, where internal nodes represent ancestral species and the leaves represent modern day species. Accurate (or even boundedly inaccurate) topology reconstructions of large and divergent trees from realistic length sequences have long been considered one of the major challenges in systematic biology. In this paper, we present a simple method, the Disk-Covering Method (DCM), which boosts the performance of base phylogenetic methods under various Markov models of evolution. We analyze the performance of DCM-boosted distance methods under the Jukes-Cantor Markov model of biomolecular sequence evolution, and prove that for almost all trees, polylogarithmic length sequences suffice for complete accuracy with high probability, while polynomial length sequences always suffice. We also provide an experimental study based upon simulating sequence evolution on model trees. This study confirms substantial reductions in error rates at realistic sequence lengths.

Algorithms↗

Gene finding with a hidden Markov model of genome structure and evolution.

MOTIVATION: A growing number of genomes are sequenced. The differences in evolutionary pattern between functional regions can thus be observed genome-wide in a whole set of organisms. The diverse evolutionary pattern of different functional regions can be exploited in the process of genomic annotation. The modelling of evolution by the existing comparative gene finders leaves room for improvement. RESULTS: A probabilistic model of both genome structure and evolution is designed. This type of model is called an Evolutionary Hidden Markov Model (EHMM), being composed of an HMM and a set of region-specific evolutionary models based on a phylogenetic tree. All parameters can be estimated by maximum likelihood, including the phylogenetic tree. It can handle any number of aligned genomes, using their phylogenetic tree to model the evolutionary correlations. The time complexity of all algorithms used for handling the model are linear in alignment length and genome number. The model is applied to the problem of gene finding. The benefit of modelling sequence evolution is demonstrated both in a range of simulations and on a set of orthologous human/mouse gene pairs. AVAILABILITY: Free availability over the Internet on www server: http://www.birc.dk/Software/evogene.

Algorithms↗

Modeling and simulating morphological evolution in an artificial life environment.

This paper presents a computer-based environment designed to study biological evolution considering morphological aspects. It was inspired on cellular automata and evolutionary algorithm principles. Simple rules are used to determine the genotype and phenotype of individuals and their relationships with behavioral aspects in a square matrix environment, where individuals can evolve. Two methods to simulate mutational errors and to introduce variability of mutations are discussed. A series of four simulations show that the model promotes phenotype evolution depending on the distribution of food over the environment; morphology evolved as to favor movement of the individuals towards the portion of the environment in which the food has been distributed or to capture falling food.

Algorithms↗

Could natural selection account for molecular evolution and polymorphism?

A model of molecular evolution is presented that is based on the combined action of natural selection, genetic drift, and mutation. The mathematical description of the model uses strong-selection, weak-mutation limits to approximate the dynamics of multidimensional diffusion processes with one dimensional Markov chains. This approach leads to a great simplification of the dynamics and provides a unified method for describing many different mechanisms of natural selection. In this paper two models are examined, one based on selection in a randomly fluctuating environment, the other on overdominance. Both models exhibit similar dynamics, with a rapid buildup phase that introduces new alleles into the population, followed by a relatively quiescent phase where new alleles may enter and leave the population at a low rate. If occasional extreme environmental changes occur that favor particular alleles, the resulting dynamics turn out to be in remarkable agreement with many of the observations on molecular evolution and polymorphism. Thus the model is at least as successful as the neutral theory in accounting for evolutionary events at the molecular level.

Biological Evolution↗

Recovering the geographic origin of early modern humans by realistic and spatially explicit simulations.

Most genetic and archeological evidence argue in favor of a recent and unique origin of modern humans in sub-Saharan Africa, but no attempt has ever been made at quantifying the likelihood of this model, relative to alternative hypotheses of human evolution. In this paper, we investigate the possibility of using multilocus genetic data to correctly infer the geographic origin of humans, and to distinguish between a unique origin (UO) and a multiregional evolution (ME) model. We introduce here an approach based on realistic simulations of the genetic diversity expected after an expansion process of modern humans into the Old World from different possible areas and their comparison to observed data. We find that the geographic origin of the expansion can be correctly recovered provided that a large number of independent markers are used, and that precise information on past demography and potential places of origins is available. In that case, it is also possible to unambiguously distinguish between a unique origin and a multiregional model of human evolution. Application to a real human data set of 377 STR markers tested in 22 populations points toward a unique but surprising North African origin of modern humans. We show that this result could be due to ascertainment bias in favor of markers selected to be polymorphic in Europeans. A new estimation modeling this bias explicitly reveals that East Africa is the most likely place of origin for modern humans.

Africa↗

Evolution of the biopsychosocial model: prospects and challenges for health psychology.

Although advances have been made in specifying connections between biological, psychological, and social processes, the full potential of the biopsychosocial model for health psychology remains untapped. In this article, 4 areas that need to be addressed to ensure the continued evolution of the biopsychosocial model are identified and a series of recommendations concerning initiatives directed at research, training, practice and intervention, and policy are delineated. These recommendations emphasize the need to better understand and utilize linkages among biological, psychological, social, and macrocultural variables. Activities that facilitate the adoption of a multisystem, multilevel, and multivariate orientation among scientists, practitioners, and policymakers will most effectively lead to the kinds of transdisciplinary contributions envisioned by the biopsychosocial perspective.

Behavioral Medicine↗

Phase transition in a spatial Lotka-Volterra model.

Spatial evolution is investigated in a simulated system of nine competing and mutating bacterium strains, which mimics the biochemical war among bacteria capable of producing two different bacteriocins (toxins) at most. Random sequential dynamics on a square lattice is governed by very symmetrical transition rules for neighborhood invasions of sensitive strains by killers, killers by resistants, and resistants by sensitives. The community of the nine possible toxicity/resistance types undergoes a critical phase transition as the uniform transmutation rates between the types decreases below a critical value P(c) above that all the nine types of strains coexist with equal frequencies. Passing the critical mutation rate from above, the system collapses into one of three topologically identical (degenerated) states, each consisting of three strain types. Of the three possible final states each accrues with equal probability and all three maintain themselves in a self-organizing polydomain structure via cyclic invasions. Our Monte Carlo simulations support that this symmetry-breaking transition belongs to the universality class of the three-state Potts model.

Bacterial Physiological Phenomena↗

[Stabilizing selection and computer models of the joint evolution of patterns of polygenes, transposable elements, and origin identity labels].

A computer model of the populations dynamics of the patterns of polygenes, transposable elements (TEs), and origin identity labels (OILs) in the course of stabilizing selection for an additive quantitative trait (with the target value being 0.4 of the maximum) was analyzed. It was demonstrated that the final plateaus of the trait value and the frequencies of the active values of polygenes are reached rapidly, namely, within five to seven generations (the effective selection period). The inbreeding coefficient during this period also grows rapidly and then gradually increases eventually reaching approximately 0.7. The inbreeding coefficient reaches plateau (at approximately 1.0) only in generations 300-350, which suggests the effect of gene drift. Dendrograms of the patterns of polygenes, TEs, and OILs were constructed for all generations. By generation 100 of selection, the final patterns of TEs and OILs were not formed completely. Fixations and losses, especially those of the OIL pattern, were delayed. In general, however, the population heterogeneity with respect to the patterns studied does not stabilize. This heterogeneity decreases the case of stabilizing selection, although more slowly than in the cases of positive and negative selections.

Animals↗

The effect of epidemics on genetic evolution.

Mathematical models of a vector-borne infectious disease acting on a host population consisting of three genotypes which differ in susceptibility to, recovery from, and death due to the disease are presented and analyzed. Singular perturbation techniques are used to obtain a single differential equation describing the slow time evolution of gene frequencies.

Biological Evolution↗

The catalytic power of pyruvate decarboxylase. A stochastic model for the molecular evolution of enzymes.

Pyruvate decarboxylase (PDC) catalyzes the decarboxylation of pyruvate anion by a factor of around 10(12), compared with the non-enzymic decarboxylation by thiamine, under standard state conditions of 1 mM pyruvate and thiamine diphosphate (TDP), pH 6.2. Free-energy diagrams constructed on the basis of earlier measurements for the enzymic and non-enzymic reactions give some information on catalysis by PDC. PDC stabilizes the reactant state preceding TDP addition to pyruvate by 76 kJ mol-1 and the transition state for the addition by 83 kJ mol-1. PDC stabilizes the reactant state preceding decarboxylation (presumably alpha-lactyl-TDP) by 27 kJ mol-1 and the transition state for decarboxylation by 68 kJ mol-1. In addition, the free-energy diagrams reveal a leveling of reactant-state free energies in the enzymic reaction compared with the non-enzymic reaction, in that the former are nearly equal to each other. The enzyme-bound transition-state energies are similarly leveled. The energetic leveling of reactant states has been noted by Albery, Knowles and their coworkers in many enzymic reactions and termed 'matched internal thermodynamics.' They showed that the result would arise naturally (and inevitably) in the 'evolution to perfection' of enzymes, when the evolutionary process was treated by a deterministic model. The critical assumption of this model was the validity of a Marcus-type or Brønsted-type linear free-energy relationship between rate and equilibrium constants for reactions occurring wholly within enzyme complexes. Here a completely stochastic simulation of molecular evolution, with no deterministic assumptions, is shown to reproduce both 'matched internal thermodynamics' and the 'matched internal kinetics' or leveling of transition-state energies noted here. The Albery-Knowles result is thus more general than might have been supposed.

Biological Evolution↗