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PubMed2003

Human-mouse gene identification by comparative evidence integration and evolutionary analysis.

The identification of genes in the human genome remains a challenge, as the actual predictions appear to disagree tremendously and vary dramatically on the basis of the specific gene-finding methodology used. Because the pattern of conservation in coding regions is expected to be different from intronic or intergenic regions, a comparative computational analysis can lead, in principle, to an improved computational identification of genes in the human genome by using a reference, such as mouse genome. However, this comparative methodology critically depends on three important factors: (1) the selection of the most appropriate reference genome. In particular, it is not clear whether the mouse is at the correct evolutionary distance from the human to provide sufficiently distinctive conservation levels in different genomic regions, (2) the selection of comparative features that provide the most benefit to gene recognition, and (3) the selection of evidence integration architecture that effectively interprets the comparative features. We address the first question by a novel evolutionary analysis that allows us to explicitly correlate the performance of the gene recognition system with the evolutionary distance (time) between the two genomes. Our simulation results indicate that there is a wide range of reference genomes at different evolutionary time points that appear to deliver reasonable comparative prediction of human genes. In particular, the evolutionary time between human and mouse generally falls in the region of good performance; however, better accuracy might be achieved with a reference genome further than mouse. To address the second question, we propose several natural comparative measures of conservation for identifying exons and exon boundaries. Finally, we experiment with Bayesian networks for the integration of comparative and compositional evidence.

Animals↗
PubMed2005

Evolutionary optimization of a hierarchical object recognition model.

A major problem in designing artificial neural networks is the proper choice of the network architecture. Especially for vision networks classifying three-dimensional (3-D) objects this problem is very challenging, as these networks are necessarily large and therefore the search space for defining the needed networks is of a very high dimensionality. This strongly increases the chances of obtaining only suboptimal structures from standard optimization algorithms. We tackle this problem in two ways. First, we use biologically inspired hierarchical vision models to narrow the space of possible architectures and to reduce the dimensionality of the search space. Second, we employ evolutionary optimization techniques to determine optimal features and nonlinearities of the visual hierarchy. Here, we especially focus on higher order complex features in higher hierarchical stages. We compare two different approaches to perform an evolutionary optimization of these features. In the first setting, we directly code the features into the genome. In the second setting, in analogy to an ontogenetical development process, we suggest the new method of an indirect coding of the features via an unsupervised learning process, which is embedded into the evolutionary optimization. In both cases the processing nonlinearities are encoded directly into the genome and are thus subject to optimization. The fitness of the individuals for the evolutionary selection process is computed by measuring the network classification performance on a benchmark image database. Here, we use a nearest-neighbor classification approach, based on the hierarchical feature output. We compare the found solutions with respect to their ability to generalize. We differentiate between a first- and a second-order generalization. The first-order generalization denotes how well the vision system, after evolutionary optimization of the features and nonlinearities using a database A, can classify previously unseen test views of objects from this database A. As second-order generalization, we denote the ability of the vision system to perform classification on a database B using the features and nonlinearities optimized on database A. We show that the direct feature coding approach leads to networks with a better first-order generalization, whereas the second-order generalization is on an equally high level for both direct and indirect coding. We also compare the second-order generalization results with other state-of-the-art recognition systems and show that both approaches lead to optimized recognition systems, which are highly competitive with recent recognition algorithms.

Algorithms↗
PubMed2004

Evolutionary potential of Chamaecrista fasciculata in relation to climate change. II. Genetic architecture of three populations reciprocally planted along an environmental gradient in the great plains.

Climate change will test the evolutionary potential of populations. Information regarding the genetic architecture within and among populations is essential for prediction of evolutionary outcomes. However, little is known about the distribution of genetic variation for relevant traits in natural populations or alteration of genetic architecture in a changing environment. In this study, pedigreed families from three populations of the annual prairie legume Chamaecrista fasciculata were reciprocally transplanted in three environments across a broad latitudinal range in the Great Plains. The underlying premise of this work is that northern populations will in the future experience climates similar to current-day climates further south. Estimates of narrow-sense heritability ranged from 0.053 to 0.481, suggesting the potential for evolutionary change is possible for most traits. In general, the northern population harbored less genetic variation and had lower heritability for traits than the southern population. This population also experienced large reductions in fitness, as measured by estimated lifetime fecundity, when raised in either the intermediate or the southern climate, whereas the difference between the intermediate and southern population was less extreme. For fecundity, the pattern of cross-environment additive genetic correlations was antagonistic to evolutionary change in four of six cases when native and nonnative sites were compared. Six additional antagonistic positive correlations were found for the rate of phenological development and leaf thickness. Overall, the data suggest that if climate changes as predicted, the northern population will face a severe evolutionary challenge in the future because of low heritabilities, cross-environment genetic correlations antagonistic to selection, and demographic instability due to lower seed production in a hotter and drier climate.

Adaptation, Physiological↗
PubMed2006

Evolutionary responses of natives to introduced species: what do introductions tell us about natural communities?

Biological invasions dramatically affect the distribution, abundance and reproduction of many native species. Because of these ecological effects, exotic species can also influence the evolution of natives exposed to novel interactions with invaders. Evolutionary changes in natives in response to selection from exotics are usually overlooked, yet common responses include altered anti-predator defenses, changes in the spectrum of resources and habitats used, and other adaptations that allow native populations to persist in invaded areas. Whether a native population is capable of responding evolutionarily to selection from invaders will depend on the demographic impact of the invader, the genetic architecture and genetic variability of the native population and potentially the history of previous invasions. In some cases, natives will fail to evolve or otherwise adapt, and local or global extinction will result. In other cases, adaptive change in natives may diminish impacts of invaders and potentially promote coexistence between invaders and natives. Here, we review the evidence for evolutionary responses of native species to novel community members. We also discuss how the effects of introduced species may differ from those caused by natural range expansions of native species. Notably, introduced species may come from remote biotas with no previous evolutionary history with the native community. In addition, the rate of addition of introduced species into communities is much greater than all but the most extreme cases of historical biotic exchange. Understanding the evolutionary component of exotic/native species interactions is critical to recognizing the long-term impacts of biological invasions, and to understanding the role of evolutionary processes in the assembly and dynamics of natural communities.

Animals↗
PubMedDate not supplied

Mutationism and the dual causation of evolutionary change.

The rediscovery of Mendel's laws a century ago launched the science that William Bateson called "genetics," and led to a new view of evolution combining selection, particulate inheritance, and the newly characterized phenomenon of "mutation." This "mutationist" view clashed with the earlier view of Darwin, and the later "Modern Synthesis," by allowing discontinuity, and by recognizing mutation (or more properly, mutation-and-altered-development) as a source of creativity, direction, and initiative. By the mid-20th century, the opposing Modern Synthesis view was a prevailing orthodoxy: under its influence, "evolution" was redefined as "shifting gene frequencies," that is, the sorting out of pre-existing variation without new mutations; and the notion that mutation-and-altered-development can exert a predictable influence on the course of evolutionary change was seen as heretical. Nevertheless, mutationist ideas re-surfaced: the notion of mutational determinants of directionality emerged in molecular evolution by 1962, followed in the 1980s by an interest among evolutionary developmental biologists in a shaping or creative role of developmental propensities of variation, and more recently, a recognition by theoretical evolutionary geneticists of the importance of discontinuity and of new mutations in adaptive dynamics. The synthetic challenge presented by these innovations is to integrate mutation-and-altered-development into a new understanding of the dual causation of evolutionary change--a broader and more predictive understanding that already can lay claim to important empirical and theoretical results--and to develop a research program appropriately emphasizing the emergence of variation as a cause of propensities of evolutionary change.

Animals↗
PubMed1997

Genetic factors in reproduction and their evolutionary significance.

PROBLEM: The reproductive process is a major driving force in human evolution. An evolutionary perspective was brought to bear on some aspects of reproduction and its aberrations, and, conversely, some of the insights of modern reproductive genetics were used to investigate problems in evolution. METHOD: The data used were obtained from the literature in evolution, anthropology, archeology, linguistics, and genetics. RESULTS: The evolutionary line leading to modern humans diverged from that leading to the chimpanzees approximately 5-7 million years ago (Mya). Archaic Homo sapiens emerged ca. 0.3 Mya, and modern Homo sapiens and the development of language ca. 0.1 Mya; thus, modern humans occupy approximately 2% of the evolutionary history of the hominid line. During all of this time, the ancestors of modern humans were migratory hunter-gatherers. It was only during the Neolithic transition ca. 0.01 Mya (approximately 0.2% of hominid evolutionary history) that agriculture was developed, and with it a settled lifestyle that allowed a more stable existence and the development of a different reproductive pattern. Various estimates indicate that the human population increased from 0.05 million at the time of the emergence of modern Homo sapiens to 6,000 million at the present time (120,000-fold increase). CONCLUSIONS: These evolutionary considerations were used to explore three areas: (1) the extinction of the Neanderthals, who coexisted for ca. 65,000 years with modern humans; (2) the relatively low and stable rate of human conceptions (20-35% of ova fertilized naturally or fertilized in vitro); and (3) the long postnatal period required for the full maturation of the immune response. From these considerations, a broad view of the human reproductive process was obtained that may provide some insight into the rationale for the development of effective reproductive technologies.

Animals↗
PubMed2005

Evolutionary mechanisms acting on proteinase inhibitor variability.

The interaction of proteinase inhibitors produced, in most cases, by host organisms and the invasive proteinases of pathogens or parasites or the dietary proteinases of predators, results in an evolutionary 'arms race' of rapid and ongoing change in both interacting proteins. The importance of these interactions in pathogenicity and predation is indicated by the high level and diversity of observable evolutionary activity that has been found. At the initial level of evolutionary change, recruitment of other functional protein-folding families has occurred, with the more recent evolution of one class of proteinase inhibitor from another, using the same mechanism and proteinase contact residues. The combination of different inhibitor domains into a single molecule is also observed. The basis from which variation is possible is shown by the high rate of retention of gene duplication events and by the associated process of inhibitory domain multiplication. At this level of reorganization, mutually exclusive splicing is also observed. Finally, the major mechanism by which variation is achieved rapidly is hypervariation of contact residues, an almost ubiquitous feature of proteinase inhibitors. The diversity of evolutionary mechanisms in a single class of proteins is unlikely to be common, because few systems are under similar pressure to create variation. Proteinase inhibitors are therefore a potential model system in which to study basic evolutionary process such as functional diversification.

Amino Acid Sequence↗
PubMed2026

Predictive evolutionary genomics: principles, validation, and practice.

Climate change and habitat loss are driving rapid evolutionary responses in populations world-wide, which creates an urgent need for evolutionary forecasting in conservation and agriculture. Such forecasting can be categorized into three time scales: trait-based models that use multivariate quantitative genetic equations to project correlated phenotypic responses up to c. 20 generations, allele-based analyses that model allele frequency dynamics up to 100 generations, and composite adaptation scores that aggregate many small effects to yield predictions across longer horizons. However, these approaches have remained largely disconnected. Here, we present a Bayesian framework that integrates these three complementary approaches for evolutionary prediction. Our framework combines genomic, phenotypic, and environmental data to yield probabilistic predictions with explicit uncertainty. We show how predictive evolutionary forecasts can be validated with experimental evolution, field experimentation, historical specimens, and reciprocal transplants. These validated forecasts can help advance conservation and agricultural programmes by helping predict which populations are at risk of future extinction, optimizing breeding programmes for future climates, and planning ecosystem management under environmental change. By supporting a shift towards more predictive approaches in evolutionary biology, this framework may help improve our ability to manage biodiversity and food security in a changing world.

Genomics↗
PubMed2001

Evolutionary relationships among parvoviruses: virus-host coevolution among autonomous primate parvoviruses and links between adeno-associated and avian parvoviruses.

The current classification of parvoviruses is based on virus host range and helper virus dependence, while little data on evolutionary relationships among viruses are available. We identified and analyzed 472 sequences of parvoviruses, among which there were (virtually) full-length genomes of all 41 viruses currently recognized as individual species within the family Parvoviridae. Our phylogenetic analysis of full-length genomes as well as open reading frames distinguished three evolutionary groups of parvoviruses from vertebrates: (i) the human helper-dependent adeno-associated virus (AAV) serotypes 1 to 6 and the autonomous avian parvoviruses; (ii) the bovine, chipmunk, and autonomous primate parvoviruses, including human viruses B19 and V9; and (iii) the parvoviruses from rodents (except for chipmunks), carnivores, and pigs. Each of these three evolutionary groups could be further subdivided, reflecting both virus-host coevolution and multiple cross-species transmissions in the evolutionary history of parvoviruses. No parvoviruses from invertebrates clustered with vertebrate parvoviruses. Our analysis provided evidence for negative selection among parvoviruses, the independent evolution of their genes, and recombination among parvoviruses from rodents. The topology of the phylogenetic tree of autonomous human and simian parvoviruses matched exactly the topology of the primate family tree, as based on the analysis of primate mitochondrial DNA. Viruses belonging to the AAV group were not evolutionarily linked to other primate parvoviruses but were linked to the parvoviruses of birds. The two lineages of human parvoviruses may have resulted from independent ancient zoonotic infections. Our results provide an argument for reclassification of Parvovirinae based on evolutionary relationships among viruses.

Animals↗
PubMed2002

Evolutionary history of Cucumber mosaic virus deduced by phylogenetic analyses.

Cucumber mosaic virus (CMV) is an RNA plant virus with a tripartite genome and an extremely broad host range. Previous evolutionary analyses with the coat protein (CP) and 5' nontranslated region (NTR) of RNA 3 suggested subdivision of the virus into three groups, subgroups IA, IB, and II. In this study 15 strains of CMV whose nucleotide sequences have been determined were used for a complete phylogenetic analysis of the virus. The trees estimated for open reading frames (ORFs) located on the different RNAs were not congruent and did not completely support the subgrouping indicated by the CP ORF, indicating that different RNAs had independent evolutionary histories. This is consistent with a reassortment mechanism playing an important role in the evolution of the virus. The evolutionary trees of the 1a and 3a ORFs were more compact and displayed more branching than did those of the 2a and CP ORFs. This may reflect more rigid host-interactive constraints exerted on the 1a and 3a ORFs. In addition, analysis of the 3' NTR that is conserved among all RNAs indicated that evolutionary constraints on this region are specific to the RNA component rather than the virus isolate. This indicates that functions other than replication are encoded in the 3' NTR. Reassortment may have led to the genetic diversity found among CMV strains and contributed to its enormous evolutionary success.

3' Untranslated Regions↗
PubMed2002

Pathogen population genetics, evolutionary potential, and durable resistance.

We hypothesize that the evolutionary potential of a pathogen population is reflected in its population genetic structure. Pathogen populations with a high evolutionary potential are more likely to overcome genetic resistance than pathogen populations with a low evolutionary potential. We propose a flexible framework to predict the evolutionary potential of pathogen populations based on analysis of their genetic structure. According to this framework, pathogens that pose the greatest risk of breaking down resistance genes have a mixed reproduction system, a high potential for genotype flow, large effective population sizes, and high mutation rates. The lowest risk pathogens are those with strict asexual reproduction, low potential for gene flow, small effective population sizes, and low mutation rates. We present examples of high-risk and low-risk pathogens. We propose general guidelines for a rational approach to breed durable resistance according to the evolutionary potential of the pathogen.

Bacteria↗
PubMed2000

A simple model of unbounded evolutionary versatility as a largest-scale trend in organismal evolution.

The idea that there are any large-scale trends in the evolution of biological organisms is highly controversial. It is commonly believed, for example, that there is a large-scale trend in evolution towards increasing complexity, but empirical and theoretical arguments undermine this belief. Natural selection results in organisms that are well adapted to their local environments, but it is not clear how local adaptation can produce a global trend. In this paper, I present a simple computational model, in which local adaptation to a randomly changing environment results in a global trend towards increasing evolutionary versatility. In this model, for evolutionary versatility to increase without bound, the environment must be highly dynamic. The model also shows that unbounded evolutionary versatility implies an accelerating evolutionary pace. I believe that unbounded increase in evolutionary versatility is a large-scale trend in evolution. I discuss some of the testable predictions about organismal evolution that are suggested by the model.

Biological Evolution↗
PubMed1999

Visualizing evolutionary activity of genotypes.

We introduce a method for visualizing evolutionary activity of genotypes. Following a proposal of Bedau and Packard [11], we define a genotype's evolutionary activity in terms of the history of its concentration in the evolving population. To visualize this evolutionary activity we graph the distribution of evolutionary activity in the population of genotypes as a function of time. Adaptively significant genotypes trace a salient line or "wave" in these graphs. The quality of these waves indicates a variety of neutral variation, and random genetic drift. We apply this method in an evolutionary model of self-replicating assembly language programs competing for room in a two-dimensional space. Comparison with fitness graphs and with a nonadaptive analogue of this model shows how this method highlights adaptively significant events.

Biological Evolution↗
PubMed1998

Initialization strategies and diversity in evolutionary timetabling.

This document seeks to provide a scientific basis by which different initialization algorithms for evolutionary timetabling may be compared. Seeding the initial population may be used to improve initial quality and provide a better starting point for the evolutionary algorithm. This must be tempered against the consideration that if the seeding algorithm produces very similar solutions, then the loss of genetic diversity may well lead to a worse final solution. Diversity, we hope, provides a good indication of how good the final solution will be, although only by running the evolutionary algorithm will the exact result be found. We will investigate the effects of heuristic seeding by taking quality and diversity measures of populations generated by heuristic initialization methods on both random and real-life data, as well as assessing the long-term performance of an evolutionary algorithm (found to work well on the timetabling problem) when using heuristic initialization. This will show how the use of heuristic initialization strategies can substantially improve the performance of evolutionary algorithms for the timetabling problem.

Algorithms↗
PubMed1999

A statistical study of a class of cellular evolutionary algorithms.

Parallel evolutionary algorithms, over the past few years, have proven empirically worthwhile, but there seems to be a lack of understanding of their workings. In this paper we concentrate on cellular (fine-grained) models, our objectives being: (1) to introduce a suite of statistical measures, both at the genotypic and phenotypic levels, which are useful for analyzing the workings of cellular evolutionary algorithms; and (2) to demonstrate the application and utility of these measures on a specific example-the cellular programming evolutionary algorithm. The latter is used to evolve solutions to three distinct (hard) problems in the cellular-automata domain: density, synchronization, and random number generation. Applying our statistical measures, we are able to identify a number of trends common to all three problems (which may represent intrinsic properties of the algorithm itself), as well as a host of problem-specific features. We find that the evolutionary algorithm tends to undergo a number of phases which we are able to quantitatively delimit. The results obtained lead us to believe that the measures presented herein may prove useful in the general case of analyzing fine-grained evolutionary algorithms.

Algorithms↗
PubMed2006

Evolutionary testing using an extended Chaining Approach.

Fitness functions derived from certain types of white-box test goals can be inadequate for evolutionary software test data generation (Evolutionary Testing), due to a lack of search guidance to the required test data. Often this is because the fitness function does not take into account data dependencies within the program under test, and the fact that certain program statements may need to have been executed prior to the target structure in order for it to be feasible. This paper proposes a solution to this problem by hybridizing Evolutionary Testing with an extended Chaining Approach. The Chaining Approach is a method which identifies statements on which the target structure is data dependent, and incrementally develops chains of dependencies in an event sequence. By incorporating this facility into Evolutionary Testing, and by performing a test data search for each generated event sequence, the search can be directed into potentially promising, unexplored areas of the test object's input domain. Results presented in the paper show that test data can be found for a number of test goals with this hybrid approach that could not be found by using the original Evolutionary Testing approach alone. One such test goal is drawn from code found in the publicly available libpng library.

Algorithms↗
PubMed2003

Algorithms for computing parsimonious evolutionary scenarios for genome evolution, the last universal common ancestor and dominance of horizontal gene transfer in the evolution of prokaryotes.

BACKGROUND: Comparative analysis of sequenced genomes reveals numerous instances of apparent horizontal gene transfer (HGT), at least in prokaryotes, and indicates that lineage-specific gene loss might have been even more common in evolution. This complicates the notion of a species tree, which needs to be re-interpreted as a prevailing evolutionary trend, rather than the full depiction of evolution, and makes reconstruction of ancestral genomes a non-trivial task. RESULTS: We addressed the problem of constructing parsimonious scenarios for individual sets of orthologous genes given a species tree. The orthologous sets were taken from the database of Clusters of Orthologous Groups of proteins (COGs). We show that the phyletic patterns (patterns of presence-absence in completely sequenced genomes) of almost 90% of the COGs are inconsistent with the hypothetical species tree. Algorithms were developed to reconcile the phyletic patterns with the species tree by postulating gene loss, COG emergence and HGT (the latter two classes of events were collectively treated as gene gains). We prove that each of these algorithms produces a parsimonious evolutionary scenario, which can be represented as mapping of loss and gain events on the species tree. The distribution of the evolutionary events among the tree nodes substantially depends on the underlying assumptions of the reconciliation algorithm, e.g. whether or not independent gene gains (gain after loss after gain) are permitted. Biological considerations suggest that, on average, gene loss might be a more likely event than gene gain. Therefore different gain penalties were used and the resulting series of reconstructed gene sets for the last universal common ancestor (LUCA) of the extant life forms were analysed. The number of genes in the reconstructed LUCA gene sets grows as the gain penalty increases. However, qualitative examination of the LUCA versions reconstructed with different gain penalties indicates that, even with a gain penalty of 1 (equal weights assigned to a gain and a loss), the set of 572 genes assigned to LUCA might be nearly sufficient to sustain a functioning organism. Under this gain penalty value, the numbers of horizontal gene transfer and gene loss events are nearly identical. This result holds true for two alternative topologies of the species tree and even under random shuffling of the tree. Therefore, the results seem to be compatible with approximately equal likelihoods of HGT and gene loss in the evolution of prokaryotes. CONCLUSIONS: The notion that gene loss and HGT are major aspects of prokaryotic evolution was supported by quantitative analysis of the mapping of the phyletic patterns of COGs onto a hypothetical species tree. Algorithms were developed for constructing parsimonious evolutionary scenarios, which include gene loss and gain events, for orthologous gene sets, given a species tree. This analysis shows, contrary to expectations, that the number of predicted HGT events that occurred during the evolution of prokaryotes might be approximately the same as the number of gene losses. The approach to the reconstruction of evolutionary scenarios employed here is conservative with regard to the detection of HGT because only patterns of gene presence-absence in sequenced genomes are taken into account. In reality, horizontal transfer might have contributed to the evolution of many other genes also, which makes it a dominant force in prokaryotic evolution.

Algorithms↗
PubMed2004

Evolutionary rate depends on number of protein-protein interactions independently of gene expression level.

BACKGROUND: Whether or not a protein's number of physical interactions with other proteins plays a role in determining its rate of evolution has been a contentious issue. A recent analysis suggested that the observed correlation between number of interactions and evolutionary rate may be due to experimental biases in high-throughput protein interaction data sets. DISCUSSION: The number of interactions per protein, as measured by some protein interaction data sets, shows no correlation with evolutionary rate. Other data sets, however, do reveal a relationship. Furthermore, even when experimental biases of these data sets are taken into account, a real correlation between number of interactions and evolutionary rate appears to exist. SUMMARY: A strong and significant correlation between a protein's number of interactions and evolutionary rate is apparent for interaction data from some studies. The extremely low agreement between different protein interaction data sets indicates that interaction data are still of low coverage and/or quality. These limitations may explain why some data sets reveal no correlation with evolutionary rates.

Evolution, Molecular↗