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PubMed2004

Generating high-speed dynamic running gaits in a quadruped robot using an evolutionary search.

Over the past several decades, there has been a considerable interest in investigating high-speed dynamic gaits for legged robots. While much research has been published, both in the biomechanics and engineering fields regarding the analysis of these gaits, no single study has adequately characterized the dynamics of high-speed running as can be achieved in a realistic, yet simple, robotic system. The goal of this paper is to find the most energy-efficient, natural, and unconstrained gallop that can be achieved using a simulated quadrupedal robot with articulated legs, asymmetric mass distribution, and compliant legs. For comparison purposes, we also implement the bound and canter. The model used here is planar, although we will show that it captures much of the predominant dynamic characteristics observed in animals. While it is not our goal to prove anything about biological locomotion, the dynamic similarities between the gaits we produce and those found in animals does indicate a similar underlying dynamic mechanism. Thus, we will show that achieving natural, efficient high-speed locomotion is possible even with a fairly simple robotic system. To generate the high-speed gaits, we use an efficient evolutionary algorithm called set-based stochastic optimization. This algorithm finds open-loop control parameters to generate periodic trajectories for the body. Several alternative methods are tested to generate periodic trajectories for the legs. The combined solutions found by the evolutionary search and the periodic-leg methods, over a range of speeds up to 10.0 m/s, reveal "biological" characteristics that are emergent properties of the underlying gaits.

Algorithms↗

PubMed2000

Error thresholds for quasispecies on dynamic fitness landscapes.

In this paper we investigate error thresholds on dynamic fitness landscapes. We show that there exists both a lower and an upper threshold, representing limits to the copying fidelity of simple replicators. The lower bound can be expressed as a correction term to the error threshold present on a static landscape. The upper error threshold is a new limit that only exists on dynamic fitness landscapes. We also show that for long genomes and/or highly dynamic fitness landscapes there exists a lower bound on the selection pressure required for the effective selection of genomes with superior fitness independent of mutation rates, i.e. there are distinct nontrivial limits to evolutionary parameters in dynamic environments.

Biological Evolution↗

PubMed1999

Dynamically heterogenous partitions and phylogenetic inference: an evaluation of analytical strategies with cytochrome b and ND6 gene sequences in cranes.

ki ctes over whether molecular sequence data should be partitioned for phylogenetic analysis often confound two types of heterogeneity among partitions. We distinguish historical heterogeneity (i.e., different partitions have different evolutionary relationships) from dynamic heterogeneity (i.e., different partitions show different patterns of sequence evolution) and explore the impact of the latter on phylogenetic accuracy and precision with a two-gene, mitochondrial data set for cranes. The well-established phylogeny of cranes allows us to contrast tree-based estimates of relevant parameter values with estimates based on pairwise comparisons and to ascertain the effects of incorporating different amounts of process information into phylogenetic estimates. We show that codon positions in the cytochrome b and NADH dehydrogenase subunit 6 genes are dynamically heterogenous under both Poisson and invariable-sites + gamma-rates versions of the F84 model and that heterogeneity includes variation in base composition and transition bias as well as substitution rate. Estimates of transition-bias and relative-rate parameters from pairwise sequence comparisons were comparable to those obtained as tree-based maximum likelihood estimates. Neither rate-category nor mixed-model partitioning strategies resulted in a loss of phylogenetic precision relative to unpartitioned analyses. We suggest that weighted-average distances provide a computationally feasible alternative to direct maximum likelihood estimates of phylogeny for mixed-model analyses of large, dynamically heterogenous data sets.

Algorithms↗

PubMed2003

Evolutionary perspectives on pregnancy: maternal age at menarche and infant birth weight.

We present a novel evolutionary analysis of low birth weight (LBW). LBW is a well-known risk factor for increased infant morbidity and mortality. Its causes, however, remain obscure and there is a vital need for new approaches. Life history theory, the most dynamic branch of evolutionary ecology, provides important insights into the potential role of LBW in human reproductive strategies. Life history theory's primary rationale for LBW is the trade-off between current and future reproduction. This trade-off underlies the prediction that under conditions of environmental risk and uncertainty (experienced subjectively as psychosocial stress) it can be evolutionarily adaptive to reproduce at a young age. One component of early reproduction is early menarche. Early reproduction tends to maximise offspring quantity, but parental investment theory's assumption of a quantity-quality trade-off holds that maximizing offspring quantity reduces quality, of which LBW may be the major component. We therefore predict that women who experienced early psychosocial stress and had early menarche are more likely to produce LBW babies. Furthermore, the extension of parent-offspring conflict theory in utero suggests that the fetus will attempt to resist its mother's efforts to reduce its resources, allocating more of what it does receive to the placenta in order to extract more maternal resources to increase its own quality. We propose that LBW babies born to mothers who experience early psychosocial stress and have early menarche are more likely to have a higher placental/fetal weight ratio. We review evidence in support of these hypotheses and discuss the implications for public health.

Age Factors↗

PubMed1980

Density-dependent selection in a random environment: An evolutionary process that can maintain stable population dynamics.

A theoretical analysis of natural selection is presented in which fitnesses depend on population density and randomly varying environmental processes. The theory is based on a general, heuristic analysis of a pair of coupled, nonlinear, stochastic difference equations that describe the joint dynamics of allele frequencies and population size. Four main conclusions emerge from the investigation of a particular class of models: (i) growth rates at low population densities tend to increase; (ii) individual selection, given sufficient genetic flexibility, will mold growth rates at higher densities so that in spite of i, stable deterministic population dynamics are maintained; (iii) "more fit" genotypes cannot be simply characterized-in particular, the mean population size need not be increased; and (iv) genetic polymorphisms can be maintained in both haploid and diploid organisms.

Journal Article↗

PubMed1999

Evolutionary constraint networks in ligand-binding domains: an information-theoretic approach.

Ligand-binding sites in homologous protein domains can diverge greatly during evolution. This poses a particularly interesting problem in those cases where the ligand-binding site is situated in, or close to, the domain core, or where ligand-docking induces dramatic conformational changes. These features are present in many receptors and enzymes; the hormone-binding domain of the nuclear receptors for steroids and retinoids, for example, exhibits both characteristics. It is therefore of great interest to determine how binding sites for diverse ligands evolve in core regions of structurally dynamic domains. Are evolutionary changes locally restricted to the ligand-binding site, or are they distributed throughout the domain? We describe here an information-theoretic approach for the study of covariation between ligand-contacting residues and compensatory mutations that preserve the structural integrity and the conformational dynamics of ligand-binding domains. We apply this method to the analysis of the nuclear receptor ligand-binding domain and show that the ligand-contacting residues in the hormone-binding pocket are evolutionarily linked to an extensive network of covarying positions.

Amino Acid Sequence↗

PubMed2002

New inferences from tree shape: numbers of missing taxa and population growth rates.

The relative positions of branching events in a phylogeny contain information about evolutionary and population dynamic processes. We provide new summary statistics of branching event times and describe how these statistics can be used to infer rates of species diversification from interspecies trees or rates of population growth from intraspecies trees. We also introduce a phylogenetic method for estimating the level of taxon sampling in a clade. Different evolutionary models and different sampling regimes can produce similar patterns of branching events, so it is important to consider explicitly the model assumptions involved when making evolutionary inferences. Results of an analysis of the phylogeny of the mosquito-borne flaviviruses suggest that there could be several thousand currently unidentified viruses in this clade.

Animals↗

PubMed2005

Molecular footprint of drug-selective pressure in a human immunodeficiency virus transmission chain.

Known human immunodeficiency virus (HIV) transmission histories are invaluable models for investigating the evolutionary and transmission dynamics of the virus and to assess the accuracy of phylogenetic reconstructions. Here we have characterized an HIV-1 transmission chain consisting of nine infected patients, almost all of whom were treated with antiviral drugs at later stages of infection. Partial pol and env gp41 regions of the HIV genome were directly sequenced from plasma viral RNA for at least one sample from each patient. Phylogenetic analyses in pol using likelihood methods inferred an evolutionary history not fully compatible with the known transmission history. This could be attributed to parallel evolution of drug resistance mutations resulting in the incorrect clustering of multidrug-resistant virus. On the other hand, a fully compatible phylogenetic tree was reconstructed from the env sequences. We were able to identify and quantify the molecular footprint of drug-selective pressure in pol using maximum likelihood inference under different codon substitution models. An increased fixation rate of mutations in the HIV population of the multidrug-resistant patient was demonstrated using molecular clock modeling. We show that molecular evolutionary analyses, guided by a known transmission history, can reveal the presence of confounding factors like natural selection and caution should be taken when accurate descriptions of HIV evolution are required.

Anti-HIV Agents↗

PubMed2000

Evolutionary trade-offs at two time-scales: competition versus persistence.

The evolution of many natural systems is complicated due to dynamics at a mixture of time-scales. This is especially true when there is a trade-off between large reproductive rates and long-term persistence; such behaviour is frequently observed in disease models. In this paper, a simple partial differential equation model is formulated which describes the evolutionary dynamics of two disease strains in a metapopulation: one strain is a better short-term competitor; the other has greater persistence. By considering the behaviour of means and higher-order moments, analytical expressions for the evolutionary behaviour are produced in the case when the two strains are phenotypically close.

Biological Evolution↗

PubMed2001

Theoretical analysis of mutation-adaptive evolutionary algorithms.

Adaptive evolutionary algorithms require a more sophisticated modeling than their static-parameter counterparts. Taking into account the current population is not enough when implementing parameter-adaptation rules based on success rates (evolution strategies) or on premature convergence (genetic algorithms). Instead of Markov chains, we use random systems with complete connections - accounting for a complete, rather than recent, history of the algorithm's evolution. Under the new paradigm, we analyze the convergence of several mutation-adaptive algorithms: a binary genetic algorithm, the 1/5 success rule evolution strategy, a continuous, respectively a dynamic (1+1) evolutionary algorithm.

Algorithms↗

PubMed2026

Spatiotemporal patterns of Rift Valley fever virus in Africa: a retrospective genomic epidemiology and phylodynamic modelling study.

BACKGROUND: Rift Valley fever virus (RVFV) is a mosquito-borne zoonotic pathogen causing outbreaks in humans and ruminants across Africa and the Arabian Peninsula. Originally restricted to the Great Rift Valley, RVFV has expanded geographically, prompting its classification by WHO as a pathogen of pandemic potential. We investigated the evolutionary and spatial dynamics of RVFV across Africa. METHODS: We used genomic data generated at the International Livestock Research Institute Nairobi genomic laboratory (BioProject PRJNA1106221) and combined with publicly available datasets retrieved from the National Center for Biotechnology (NCBI) GenBank nucleotide database. In retrieving RVFV genome sequences from the NCBI GenBank, we applied the search terms "Rift Valley fever virus segment L AND 6404[SLEN]", "Rift Valley fever virus segment M AND 3885[SLEN]", and "Rift Valley fever virus segment S AND 1520:1690[SLEN]" for L (Large), M (Medium), and S (Small) segments, respectively. For sequences without additional spatiotemporal information, we searched PubMed to extract the associated sequence metadata. We performed molecular clock analysis, phylogenetic inference, phylodynamic modelling (continuous phylogeographic reconstruction), and landscape phylogeography on the three RVFV genome segments (L, M, and S). We aimed to assess evolutionary rates, dispersal patterns, and environmental drivers. Focus was placed on lineage C, the most widely distributed variant. FINDINGS: The global dataset used in this study consisted of large (n=236), medium (n=237), and small (n=247), which were further filtered to exclude potential reassortants and vaccine strains. Genome sequences retrieved from NCBI GenBank database comprised large (n=180), medium (n=184), and small (n=202). The genome sequences from retrospective human and livestock isolates comprised large (n=56), medium (n=53), and small (n=45) collected in Burundi (2018), Kenya (2007, 2018, 2019, 2021, and 2022), and Rwanda (2018 and 2022). Our dataset revealed that RVFV exhibited low overall genetic diversity. Lineage C, however, showed evidence of active evolution, with substitution rates ranging from 3·58 × 10-4 to 9·76 × 10-4 substitutions per site per year. This lineage probably originated in Zimbabwe in the mid-1970s and has since expanded across eastern and southern Africa. Phylogeographic reconstructions revealed rapid spread, with diffusion coefficients exceeding 50 000 km2 per year. INTERPRETATION: Lineage C appears capable of establishing endemic transmission in new regions, with ongoing diversification observed during interepidemic periods. These observations reinforce the value of continuous genomic surveillance, particularly during cryptic transmission phases when adaptive mutations might emerge. Although further evidence is needed, observed trends in climate variability and land-use change point to the potential benefit of targeted surveillance in settings that could be at increased risk, including urban centres and wetlands. FUNDING: This work was supported by the German Federal Ministry for Economic Cooperation and Development, the Rockefeller Foundation, and the Africa Centres for Disease Control and Prevention.

Rift Valley fever virus↗

PubMed2005

Genetic basis and consequences of niche construction: plasticity-induced genetic constraints on the evolution of seed dispersal in Arabidopsis thaliana.

Because seed dispersal influences the environment experienced by seeds, that environment can change as dispersal evolves. The evolutionary potential of dispersal can in turn change as dispersal evolves, if its expression of genetic variation depends on the postdispersal environment. We examined whether seed dispersion patterns have a detectable genetic basis (and therefore evolutionary potential) and determined whether that genetic basis changed depending on one postdispersal environmental factor: conspecific density. We grew replicates of 12 ecotypes of Arabidopsis thaliana at high and low density and measured seed dispersion patterns and maternal traits associated with dispersal under controlled conditions. We found density-dependent ecotypic variation for maternal traits that influence dispersal. Significant genetic variation for postdispersal sibling density was detected only when plants were grown at high density, suggesting that if dispersal evolves to result in lower postdispersal densities, the expression of genetic variation for dispersal would be reduced. This dynamic could lead to a plasticity-induced constraint on the evolution of dispersal. The ability of organisms to alter the environment they experience and the ability of that environment to evolve can alter evolutionary dynamics by augmenting or reducing evolutionary potential and thereby facilitating or constraining evolutionary responses to selection.

Arabidopsis↗

PubMed2005

Empirical analysis of locality, heritability and heuristic bias in evolutionary algorithms: a case study for the multidimensional knapsack problem.

Our main aim is to provide guidelines and practical help for the design of appropriate representations and operators for evolutionary algorithms (EAs). For this purpose, we propose techniques to obtain a better understanding of various effects in the interplay of the representation and the operators. We study six different representations and associated variation operators in the context of a steady-state evolutionary algorithm for the multidimensional knapsack problem. Four of them are indirect decoder-based techniques, and two are direct encodings combined with different initialization, repair, and local improvement strategies. The complex decoders and the local improvement and repair strategies make it practically impossible to completely analyze such EAs in a fully theoretical way. After comparing the general performance of the chosen EA variants for the multidimensional knapsack problem on two benchmark suites, we present a hands-on approach for empirically analyzing important aspects of initialization, mutation, and crossover in an isolated fashion. Static, inexpensive measurements based on randomly created solutions are performed in order to quantify and visualize specific properties with respect to heuristic bias, locality, and heritability. These tests shed light onto the complex behavior of such EAs and point out reasons for good or bad performance. In addition, the proposed measures are also examined during actual EA runs, which gives further insight into dynamic aspects of evolutionary search and verifies the validity of the isolated static measurements. All measurements are described in a general way, allowing for an easy adaption to other representations and problems.

Algorithms↗

PubMed1982

Dynamics of non-linear biochemical systems and the evolutionary significance of time hierarchy.

Several non-linear reaction networks are analyzed in order to study the influence of time hierarchy on the dynamics of biochemical systems. The analysis is based on the assumption that the separation of the time constants within metabolic systems is a direct consequence of strong differences of enzyme concentrations. Therefore, as variable system parameters, only the enzyme concentrations are considered. By investigation of the stationary states of various three-component models with feedback-activation bifurcation diagrams within a two-dimensional parameter space, the enzyme simplex, are constructed. The diagrams contain the information about the number of stationary states, their stability properties as well as the type of motion expected at different parameter combinations. The results support the hypothesis that systems with separated time constants generally show a simple dynamic behaviour. Complex motions can be expected mainly for systems without time hierarchy, which are characterized by parameters located within the centre of the enzyme simplex. Quantitative measures for the time hierarchy and the complexity of the dynamics are derived. It is supposed that the separation of time constants is a main feature of the evolution of biochemical systems. The hypothesis is further supported by the consideration of the time hierarchy of the glycolytic pathway.

Biological Evolution↗

PubMed2025

Genome-scale evolution and phylodynamics of swine influenza A viruses in China: a genomic epidemiology study.

BACKGROUND: Pigs are recognised as crucial intermediate hosts for the emergence of influenza viruses of pandemic potential. As the largest pork-producing nation, China hosts a complex ecosystem of swine influenza viruses (SIVs). We aimed to investigate the evolutionary processes, spatiotemporal dynamics, and biological characteristics of SIVs in China. METHODS: From Jan 15, 2016, to Dec 22, 2020, we collected nasal swabs from pigs at eight abattoirs and 16 swine farms in the Guangdong, Henan, and Shandong provinces of China, as part of SIV surveillance. SIVs were detected with RT-PCR. Positive samples underwent viral isolation and genome sequencing. We analysed evolution and spatiotemporal dynamics using the whole genomes of isolated SIVs, as well as genome sequences of SIV isolates from human infections worldwide retrieved from the Global Initiative on Sharing All Influenza Data and GenBank Flu databases up to April 28, 2024. Viral sequences without a sample collection area or date were excluded from the analysis. Viral receptor-binding properties and in-vitro replication of strains isolated in this study were evaluated with a solid-phase binding assay and various cell lines, including Madin-Darby canine kidney cells, porcine alveolar macrophages, primary porcine trachea epithelial cells, human bronchial epithelioid, and human lung adenocarcinoma epithelial (A549) cells. Viral replication and transmission studies were conducted in 33 guinea pigs and 13 pigs. Additionally, we collected serum samples from pig farm workers and members of the general public recruited by the Third Affiliated Hospital of Sun Yat-sen University between Feb 28 and May 11, 2023, to detect specific antibodies against Eurasian avian-like A(H1) and human-like A(H3N2) SIVs using the haemagglutination inhibition assay. FINDINGS: 23 (1·3%) of 1818 nasal swabs collected in abattoirs had SIVs; 22 (0·9%) of 2375 swabs from swine farms had SIVs. Further viral isolation yielded 39 strains of SIV. We identified 534 A(H1N1), 69 A(H1N2), and 92 A(H3N2) SIVs, representing 20 genotypes within the Eurasian avian-like lineage, 14 within the classical swine A(H1) lineage, and 16 within the human-like A(H3N2) lineage. The introduction of the A(H1N1)pdm/09 virus significantly influenced the internal gene pool of SIVs, enhancing genotypic diversity in China. Notably, the Eurasian avian-like A(H1), classical swine A(H1), and human-like A(H3N2) lineages showed human-mediated spread over long distances between provinces, with the Eurasian avian-like A(H1) lineage showing the most prevalent spread pathways. Eurasian avian-like A(H1) SIVs showed a preference for binding to sialic acid α-2,6 glycan receptors, predominantly found in humans, resulting in an increased production of progeny viruses in human airway epithelial cells, as well as effective transmission and infectivity among guinea pigs and pigs. Among 54 eligible serum samples collected from pig farm workers (24 from slaughterhouses and 30 from swine farms), 23 (43%) were seropositive for Eurasian avian-like A(H1) SIVs and 46 (85%) for human-like A(H3N2) SIVs. Among 100 eligible samples from members of the general public, 14 (14%) were seropositive for Eurasian avian-like A(H1) SIVs and 85 (85%) for human-like A(H3N2) SIVs. INTERPRETATION: This study elucidates the evolutionary processes and spatiotemporal patterns of SIVs, highlighting potential risks to public health. These findings are crucial for informing public health interventions that aim to prevent future SIV epidemics in China and other countries worldwide. FUNDING: Scientific Innovation Strategy-Construction of High-Level Academy of Agriculture Science-Distinguished Scholar (R2020PY-JC001).

Animals↗

PubMed2002

Why are proteins so robust to site mutations?

There have been repeated observations that proteins are surprisingly robust to site mutations, enduring significant numbers of substitutions with little change in structure, stability, or function. These results are almost paradoxical in light of what is known about random heteropolymers and the sensitivity of their properties to seemingly trivial mutations. To address this discrepancy, the preservation of biological protein properties in the presence of mutation has been interpreted as indicating the independence of selective pressure on such properties. Such results also lead to the prediction that de novo protein design should be relatively easy, in contrast to what is observed. Here, we use a computational model with lattice proteins to demonstrate how this robustness can result from population dynamics during the evolutionary process. As a result, sequence plasticity may be a characteristic of evolutionarily derived proteins and not necessarily a property of designed proteins. This suggests that this robustness must be re-interpreted in evolutionary terms, and has consequences for our understanding of both in vivo and in vitro protein evolution.

Computer Simulation↗

PubMed1992

Evolutionarily stable sets in symmetric extensive two-person games.

Evolutionarily stable (ES) sets are characterized for evolutionary games in extensive form. It is shown that, for the normal form of games involving informational symmetries or repeated play, the standard approach of determining evolutionarily stable strategies (ESSs) often fails to predict the evolutionary outcome. The dynamic stability of ES sets is proved in both the pure strategy and mixed strategy models. ES sets are shown to also generalize the notion of direct ESSs (an earlier attempt to apply ESS theory to extensive games). The theory is illustrated by three examples of biological games in extensive form.

Animals↗

PubMed1994

Learning synergy in a multilevel neuronal architecture.

An artificial worlds model of the brain has been developed that integrates memory, intraneuronal dynamics and multilevel evolutionary learning. The model includes two major subsystems. The first is a memory-manipulation scheme, called the reference neuron system, that serves to orchestrate a repertoire of neurons with different input-output capabilities. Signals impinging on these neurons are integrated by a cytoskeletal structure that is simulated as a cellular automation. The second subsystem is an evolutionary learning scheme, called the selection circuits system, that serves to train the neurons in the repertoire by varying the cytoskeletal proteins that control signal flow or readouts. The integrated system comprises two layers of cytoskeletally controlled neurons and two layers of reference neurons. Evolution can occur at the level of readout enzymes in neurons, at the level of proteins that control the flow of signals in the cytoskeleton and at the level of reference neurons that orchestrate the repertoire. The integrated system controls the motion of a modeled organism that is embedded in an artificial environment consisting of barriers, food and a target. The organism effectively learns to use patterns of barriers in its local environment to find the target, using food as a reward. Experiments with the model show that the integrated system enjoys significant computational synergies that make it more powerful than the component systems standing alone, that interactions between different levels at which variation can occur exert significant control over the tempo of evolution, that the synergies between different components and levels becomes more important as the environment becomes more complex and that mutation strategies that significantly slow down the rate of learning significantly decrease the degrading effects of environmental noise on performance.

Biological Evolution↗