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Results for “evolutionary scale model”

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PubMed2003

A computational model of symbiotic composition in evolutionary transitions.

Several of the major transitions in evolutionary history, such as the symbiogenic origin of eukaryotes from prokaryotes, share the feature that existing entities became the components of composite entities at a higher-level of organization. This composition of pre-adapted extant entities into a new whole is a fundamentally different source of variation from the gradual accumulation of small random variations, and it has some interesting consequences for issues of evolvability. Intuitively, the pre-adaptation of sets of features in reproductively independent specialists suggests a form of 'divide and conquer' decomposition of the adaptive domain. Moreover, the compositions resulting from one level may become the components for compositions at the next level, thus scaling-up the variation mechanism. In this paper, we explore and develop these concepts using a simple abstract model of symbiotic composition to examine its impact on evolvability. To exemplify the adaptive capacity of the composition model, we employ a scale-invariant fitness landscape exhibiting significant ruggedness at all scales. Whilst innovation by mutation and by conventional evolutionary algorithms becomes increasingly more difficult as evolution continues in this landscape, innovation by composition is not impeded as it discovers and assembles component entities through successive hierarchical levels.

Adaptation, Physiological↗

PubMed1995

Slow coevolution of a viral pathogen and its diploid host.

We study a population exposed to a lethal infectious disease. Host response is carried at one locus with two alleles while the pathogen occurs in two variants. Based on an SI-type epidemic model we derive explicit equations for the dynamics of each genotype. By assuming small variations in both host and disease, we obtain a separation in time scales between epidemic and evolutionary processes. This allows us to describe explicitly the changes in host and disease gene frequencies. The resulting model has a rich behaviour including multiple stable states and oscillations. However, in the oscillatory situation the model is degenerate excluding the possibility of limit cycles. We show that the degeneracy can only be removed by frequency dependent selection in the pathogen, for example by including direct interaction of virus in a free-living stage. The qualitative conclusions extend to an SIR-type epidemic model, where recovery with immunity from the disease is possible.

Biological Evolution↗

PubMed2001

An evolutionary scaling law for the primate visual system and its basis in cortical function.

A hallmark of mammalian brain evolution is the disproportionate increase in neocortical size as compared with subcortical structures. Because primary visual cortex (V1) is the most thoroughly understood cortical region, the visual system provides an excellent model in which to investigate the evolutionary expansion of neocortex. I have compared the numbers of neurons in the visual thalamus (lateral geniculate nucleus; LGN) and area V1 across primate species. Here I find that the number of V1 neurons increases as the 3/2 power of the number of LGN neurons. As a consequence of this scaling law, the human, for example, uses four times as many V1 neurons per LGN neuron (356) to process visual information as does a tarsier (87). I argue that the 3/2 power relationship is a natural consequence of the organization of V1, together with the requirement that spatial resolution in V1 should parallel the maximum resolution provided by the LGN. The additional observation that thalamus/neocortex follows the same evolutionary scaling law as LGN/V1 may suggest that neocortex generally conforms to the same organizational principle as V1.

Animals↗

PubMed2002

Relationship between measures of fitness and time scale in evolution.

The notion of fitness is central in evolutionary biology. We use a simple spatially extended predator-prey or host-pathogen model to show a generic case where the average number of offspring of an individual as a measure of fitness fails to characterize the evolutionary dynamics. Mutants with high initial reproduction ratios have lineages that eventually go extinct due to local overexploitation. We propose general quantitative measures of fitness that reflect the importance of time scale in evolutionary processes.

Animals↗

PubMed1991

A model of onshore-offshore change in faunal diversity.

Onshore-offshore patterns of faunal change occurred at many taxonomic scales during the Paleozoic Era, ranging from replacement of the Cambrian evolutionary fauna by the Paleozoic fauna to the environmental expansion of many orders and classes. A simple mathematical model is constructed to investigate such change. The environmental gradient across the marine shelf-slope is treated as a linear array of discrete habitats, each of which holds a set number of species, as observed in the fossil record. During any interval of time, some portion of the species in each habitat becomes extinct by background processes, with rates of extinction varying among both clades and habitats, as also observed in the record. After extinction, species are replaced from within the habitat and from immediately adjacent habitats, with proportions dependent on surviving species. This model leads to the prediction that extinction-resistant clades will always diversify at the expense of extinction-prone clades. But if extinction intensity is highest in nearshore habitats, extinction-resistant clades will expand preferentially in the onshore direction, build up diversity there, and then diversify outward toward the offshore. Thus, onshore-offshore patterns of diversification may be the expectation for faunal change quite independently of whether or not clades originate onshore. When the model is parameterized for Paleozoic trilobites and brachiopods, numerical solutions exhibit both a pattern of faunal change and a time span for diversification similar to that seen in the fossil record. They also generate structure similar to that seen in global diversification, including logistic patterns of growth, declining origination but constant extinction within clades through time, and declining overall extinction across clades through time.

Algorithms↗

PubMed2002

The evolution of chromosomal sex determination.

There is a great diversity of sex determination mechanisms, with evidence for numerous evolutionary transitions between different systems. For example, environmental sex determination is widespread in lower vertebrates, and genetic sex determination has probably evolved from it several times. This requires the establishment of genes that override environmental cues. Close linkage between male and female determining loci is favoured by selection, and represents the first step towards the evolution of highly differentiated sex chromosomes. Once crossing over between primitive sex chromosomes has been suppressed, the primitive Y (W) chromosome is vulnerable to the operation of forces that lead to a reduction in its effective population size. This reduces the ability of natural selection to maintain the functionality of genes on the proto-Y, so that it gradually degenerates. Primitive sex chromosome systems, and systems of neo-X and neo-Y chromosomes formed by translocations involving autosomes and sex chromosomes, provide an opportunity to test evolutionary models of the degeneration of Y chromosomes and to determine the time-scales involved. Recent data confirm that newly-evolving Y or neo-Y chromosomes experience a sharp reduction in effective population size, and indicate that degeneration can occur over a few million generations.

Animals↗

PubMed2006

Phyloclimatic modeling: combining phylogenetics and bioclimatic modeling.

We investigate the impact of past climates on plant diversification by tracking the "footprint" of climate change on a phylogenetic tree. Diversity within the cosmopolitan carnivorous plant genus Drosera (Droseraceae) is focused within Mediterranean climate regions. We explore whether this diversity is temporally linked to Mediterranean-type climatic shifts of the mid-Miocene and whether climate preferences are conservative over phylogenetic timescales. Phyloclimatic modeling combines environmental niche (bioclimatic) modeling with phylogenetics in order to study evolutionary patterns in relation to climate change. We present the largest and most complete such example to date using Drosera. The bioclimatic models of extant species demonstrate clear phylogenetic patterns; this is particularly evident for the tuberous sundews from southwestern Australia (subgenus Ergaleium). We employ a method for establishing confidence intervals of node ages on a phylogeny using replicates from a Bayesian phylogenetic analysis. This chronogram shows that many clades, including subgenus Ergaleium and section Bryastrum, diversified during the establishment of the Mediterranean-type climate. Ancestral reconstructions of bioclimatic models demonstrate a pattern of preference for this climate type within these groups. Ancestral bioclimatic models are projected into palaeo-climate reconstructions for the time periods indicated by the chronogram. We present two such examples that each generate plausible estimates of ancestral lineage distribution, which are similar to their current distributions. This is the first study to attempt bioclimatic projections on evolutionary time scales. The sundews appear to have diversified in response to local climate development. Some groups are specialized for Mediterranean climates, others show wide-ranging generalism. This demonstrates that Phyloclimatic modeling could be repeated for other plant groups and is fundamental to the understanding of evolutionary responses to climate change.

Acclimatization↗

PubMed2001

Pair approximation for lattice models with multiple interaction scales.

Pair approximation has frequently proved effective for deriving qualitative information about lattice-based stochastic spatial models for population, epidemic and evolutionary dynamics. Pair approximation is a moment closure method in which the mean-field description is supplemented by approximate equations for the frequencies of neighbor-site pairs of each possible type. A limitation of pair approximation relative to moment closure for continuous space models is that all modes of interaction between individuals (e.g., dispersal of offspring, competition, or disease transmission) are assumed to operate over a single spatial scale determined by the size of the interaction neighborhood. In this paper I present a multiscale pair approximation which allows different sized neighborhoods for each type of interaction. To illustrate and test the approximation I consider a spatial single-species logistic model in which offspring are dispersed across a birth neighborhood and established individuals have a death rate depending on the population density in a competition neighborhood, with one of these neighborhoods nested inside the other. Analysis of the steady-state equations yields several qualitative predictions that are confirmed by simulations of the model, and numerical solutions of the dynamic equations provide a close approximation to the transient behavior of the stochastic model on a large lattice. The multiscale pair approximation thus provides a useful intermediate between the standard pair approximation for a single interaction neighborhood, and a complete set of moment equations for more spatially detailed models.

Animals↗

PubMed1997

The ecology of echo.

Echo is a generic ecosystem model in which evolving agents are situated in a resource-limited environment. The Echo model is described, and the behavior of Echo is evaluated on two well-studied measures of ecological diversity: relative species abundance and the species-area scaling relation. In simulation experiments, these measures are used to compare the behavior of Echo with that of a neutral model, in which selection on agent genotypes is random. These simulations show that the evolutionary component of Echo makes a significant contribution to its behavior and that Echo shows good qualitative agreement with naturally occurring species abundance distributions and species-area scaling relations.

Biological Evolution↗

PubMed2005

Modelling the evolution of genetic regulatory networks.

An evolutionary model of genetic regulatory networks is developed, based on a model of network encoding and dynamics called the Artificial Genome (AG). This model derives a number of specific genes and their interactions from a string of (initially random) bases in an idealized manner analogous to that employed by natural DNA. The gene expression dynamics are determined by updating the gene network as if it were a simple Boolean network. The generic behaviour of the AG model is investigated in detail. In particular, we explore the characteristic network topologies generated by the model, their dynamical behaviours, and the typical variance of network connectivities and network structures. These properties are demonstrated to agree with a probabilistic analysis of the model, and the typical network structures generated by the model are shown to lie between those of random networks and scale-free networks in terms of their degree distribution. Evolutionary processes are simulated using a genetic algorithm, with selection acting on a range of properties from gene number and degree of connectivity through periodic behaviour to specific patterns of gene expression. The evolvability of increasingly complex patterns of gene expression is examined in detail. When a degree of redundancy is introduced, the average number of generations required to evolve given targets is reduced, but limits on evolution of complex gene expression patterns remain. In addition, cyclic gene expression patterns with periods that are multiples of shorter expression patterns are shown to be inherently easier to evolve than others. Constraints imposed by the template-matching nature of the AG model generate similar biases towards such expression patterns in networks in initial populations, in addition to the somewhat scale-free nature of these networks. The significance of these results on current understanding of biological evolution is discussed.

Algorithms↗

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

Mutator natural Escherichia coli isolates have an unusual virulence phenotype.

A small percentage of natural Escherichia coli isolates (both commensal and pathogenic) have a mutator phenotype related to defects in methyl-directed mismatch repair (MR) genes. We investigated whether there was a direct link between the mutator phenotype and virulence by (i) studying the relationships between mutation rate and virulence in a mouse model of extraintestinal virulence for 88 commensal and extraintestinal pathogenic E. coli isolates and (ii) comparing the virulence in mice of MR-deficient and MR-proficient strains that were otherwise isogenic. The results provide no support for the hypothesis that the mutator phenotype has a direct role in virulence or is associated with increased virulence. Most of the natural mutator strains studied displayed an unusual virulence phenotype with (i) a lack of correspondence between the number of virulence determinants and pathogenicity in mice and (ii) an intermediate level of virulence. On a large evolutionary scale, the mutator phenotype may help parasites to achieve an intermediate rate of virulence which mathematical models predict to be selected for during long-term parasite-host interactions.

Animals↗

PubMed2004

Structure and evolution of protein interaction networks: a statistical model for link dynamics and gene duplications.

BACKGROUND: The structure of molecular networks derives from dynamical processes on evolutionary time scales. For protein interaction networks, global statistical features of their structure can now be inferred consistently from several large-throughput datasets. Understanding the underlying evolutionary dynamics is crucial for discerning random parts of the network from biologically important properties shaped by natural selection. RESULTS: We present a detailed statistical analysis of the protein interactions in Saccharomyces cerevisiae based on several large-throughput datasets. Protein pairs resulting from gene duplications are used as tracers into the evolutionary past of the network. From this analysis, we infer rate estimates for two key evolutionary processes shaping the network: (i) gene duplications and (ii) gain and loss of interactions through mutations in existing proteins, which are referred to as link dynamics. Importantly, the link dynamics is asymmetric, i.e., the evolutionary steps are mutations in just one of the binding parters. The link turnover is shown to be much faster than gene duplications. Both processes are assembled into an empirically grounded, quantitative model for the evolution of protein interaction networks. CONCLUSIONS: According to this model, the link dynamics is the dominant evolutionary force shaping the statistical structure of the network, while the slower gene duplication dynamics mainly affects its size. Specifically, the model predicts (i) a broad distribution of the connectivities (i.e., the number of binding partners of a protein) and (ii) correlations between the connectivities of interacting proteins, a specific consequence of the asymmetry of the link dynamics. Both features have been observed in the protein interaction network of S. cerevisiae.

Biological Evolution↗

PubMed2005

A simple physical model for scaling in protein-protein interaction networks.

It has recently been demonstrated that many biological networks exhibit a "scale-free" topology, for which the probability of observing a node with a certain number of edges (k) follows a power law: i.e., p(k) approximately k(-gamma). This observation has been reproduced by evolutionary models. Here we consider the network of protein-protein interactions (PPIs) and demonstrate that two published independent measurements of these interactions produce graphs that are only weakly correlated with one another despite their strikingly similar topology. We then propose a physical model based on the fundamental principle that (de)solvation is a major physical factor in PPIs. This model reproduces not only the scale-free nature of such graphs but also a number of higher-order correlations in these networks. A key support of the model is provided by the discovery of a significant correlation between the number of interactions made by a protein and the fraction of hydrophobic residues on its surface. The model presented in this paper represents a physical model for experimentally determined PPIs that comprehensively reproduces the topological features of interaction networks. These results have profound implications for understanding not only PPIs but also other types of scale-free networks.

Biophysical Phenomena↗

PubMed2002

Psychobiological and evolutionary perspectives on coping and health characteristics following loss: a twin study.

An analysis of coping, grief and health characteristics is reported for a bereaved monozygotic (MZ) and dizygotic (DZ) same-sex twin sample. The data were examined with reference to psychobiological and evolutionary perspectives on behavior. A Coping Scale, included as part of a comprehensive Twin Loss Survey (TLS), assessed coping with daily responsibilities and activities 1-2 months before the co-twin's death, 1-2 months following the co-twin's death and currently. A Grief Intensity Scale obtained judgments of grief 1-2 months following the loss, and currently. Information on physical symptoms was available from the Somatization Scale of the Grief Experience Inventory. Psychobiological and evolutionary perspectives specified hypotheses for two twin groups: one model was specified to reflect bereavement experiences immediately following loss of the co-twin (retrospective twin group); a second model represented present bereavement response (current twin group). Consistent with psychobiological theory, twins' social closeness showed a positive association with grief intensity which, in turn, affected somatic symptoms and coping efficacy in predicted directions. With respect to evolutionary psychological theory, the effect of zygosity on current grief implicated correlates of genetic relatedness as factors in the bereavement process.

Activities of Daily Living↗

PubMed1999

(R)evolutionary considerations in hematopoietic development.

Evolutionary aspects of three characteristics of the mammalian hematopoietic system are considered in the context of both established and recent data. First, the lineage relationships among early members of the hematopoietic hierarchy are reconsidered in a tripartite model proposing lineage segregation based on vascular function, innate immunity, and acquired immunity on an evolutionary time scale. Second, the observation of two stem cell populations that differ in cell cycle status is considered as an evolved mechanism to enhance survival of the species in response to exposure to environmental toxins. Finally, the mobilization of hematopoietic stem cells into the peripheral circulation is proposed to be a mechanism for rapid dissemination of myeloid function during acute bacterial infections. These revolutionary hypotheses challenge some conventional concepts of stem cell biology, and provide an evolutionary context for considering mammalian hematopoiesis.

Animals↗

PubMed2006

Topology of resultant networks shaped by evolutionary pressure.

Understanding the topology of complex systems abstracted to networks is important for unraveling their functional capabilities. Many such networks follow the small-world and scale-free regimes. Several models of artificially growing networks lead to this observed network topology. Most previously proposed models for growing networks, such as rich-get-richer and duplication-divergence, produce realistic network topologies but do not consider the effects of exogenous forces such as optimization for adaptation in shaping network topology. It is likely that such forces have shaped complex systems throughout their evolution. To develop further insights into possible mechanisms that shape networks, a model that uses several previously proposed network growth algorithms was developed to grow networks that adapt under exogenous stress. A decision tree problem was used to generate a complex Boolean function. Growing networks were required to adapt to correctly decode this function using an evolutionary selection process. Under this growth regimen all growing network models are similarly adaptable. The newly added nodes tend to cluster into pathways emanating from few inputs, regardless of the growth algorithm. Distribution of redundant pathways from inputs to the output follow a power-law function with a scaling exponent (approximately 1.3). Similar distribution of redundant pathways was observed from inputs in a cell signaling network and an air traffic control network. A flat distribution of redundant pathways from inputs was observed in growing networks that do not attempt to adapt. This analysis provides initial insights into distribution of pathways in naturally evolving complex systems that have defined input-output relationships.

Algorithms↗

PubMed2003

Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model.

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of alpha-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37 degrees C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.

Adaptation, Physiological↗