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Results for “evolutionary scale model”

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At least 19 recordsLinked to original sources

PubMed2025

ESMpHLA: Evolutionary Scale Model-Based Deep Learning Prediction of HLA Class I Binding Peptides.

The recognition of endogenous peptides by HLA class I plays a crucial role in CD8+ T cell immune responses and human adaptive cell immune. Thus, the prediction of HLA class I-peptide binding affinities is always the core issue for the research of immune recognition and vaccine development. In this study, an evolutionary scale model (ESM) combined with parallel CNN blocks and a cross attention mechanism was used to construct a novel ESMpHLA model for predicting HLA class I binding peptides. Based on the 91,560 binding peptides of 41 HLA-A alleles, 56,731 of 50 HLA-B alleles and 2444 of 10 HLA-C alleles, the ESMpHLA model was successfully established and achieved satisfying prediction performances with the overall accuracy and AUC values of 0.874 and 0.938 for the test dataset. The results indicate that the ESMpHLA model performs well in dealing with different HLA class I 2-field alleles as well as the peptides with different lengths. Then, the generalisation ability of the ESMpHLA model was validated by an independent test dataset compiled from recent IEDB weekly benchmark datasets. The results showed that the ESMpHLA model achieved the highest ROC-AUC and PR-AUC values when compared with the latest BVMHC, CapsNet-MHC, STMHCpan and BVLSTM models. In addition, two ensemble models were also established by integrating the above 5 deep learning models using soft-voting and hard-voting strategies.

Humans↗

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↗

PubMed2005

Evolutionary ecology in silico: Does mathematical modelling help in understanding 'generic' trends?

Motivated by the results of recent laboratory experiments, as well as many earlier field observations, that evolutionary changes can take place in ecosystems over relatively short ecological time scales, several 'unified' mathematical models of evolutionary ecology have been developed over the last few years with the aim of describing the statistical properties of data related to the evolution of ecosystems. Moreover, because of the availability of sufficiently fast computers, it has become possible to carry out detailed computer simulations of these models. For the sake of completeness and to put these recent developments in perspective, we begin with a brief summary of some older models of ecological phenomena and evolutionary processes. However, the main aim of this article is to review critically these 'unified' models, particularly those published in the physics literature, in simple language that makes the new theories accessible to a wider audience.

Aging↗

PubMed2005

The emergence of scaling in sequence-based physical models of protein evolution.

It has recently been discovered that many biological systems, when represented as graphs, exhibit a scale-free topology. One such system is the set of structural relationships among protein domains. The scale-free nature of this and other systems has previously been explained using network growth models that, although motivated by biological processes, do not explicitly consider the underlying physics or biology. In this work we explore a sequence-based model for the evolution protein structures and demonstrate that this model is able to recapitulate the scale-free nature observed in graphs of real protein structures. We find that this model also reproduces other statistical feature of the protein domain graph. This represents, to our knowledge, the first such microscopic, physics-based evolutionary model for a scale-free network of biological importance and as such has strong implications for our understanding of the evolution of protein structures and of other biological networks.

Algorithms↗

PubMed2001

Nonequilibrium diversity dynamics of the Lesser Antillean avifauna.

MacArthur and Wilson's model of island diversity predicts an increase in the number of species until colonization and extinction are balanced at a long-term steady state. We appraise this model on an evolutionary time scale by molecular phylogenetic analysis of the colonization of the Lesser Antilles by small land birds. The pattern of accumulation of species with time, estimated by genetic divergence between island and source lineages, rejects a homogeneous model of colonization and extinction. Rather, our results suggest an abrupt, roughly 10-fold increase in colonization rate or a 90% mass extinction event 0.55 to 0.75 million years ago.

Animals↗

PubMed2025

SimHumanity: Using SLiM 5.0 to run whole-genome simulations of human evolution.

The reconstruction of human evolutionary history has undergone repeated advances, each made possible by methodological innovations. In recent decades, genetic and genomic data played a central role in the reconstruction of major evolutionary events such as the out-of-Africa migration, and genetic simulations of human evolutionary history have come to play a major role in testing more specific hypotheses including proposed patterns of migration and admixture with archaic hominins. Increasing computational power has allowed human evolutionary history to be modeled at ever-larger scales, but simulations that encompass the complete human genome, including sex chromosomes and mitochondrial DNA, have been difficult due to the lack of support for whole-genome models in commonly used evolutionary simulation frameworks. With the recent introduction of SLiM 5 such simulations are now straightforward to construct, allowing the easy simulation of humans at whole-genome scale under different demographic models and evolutionary dynamics. We here present three versions of a reusable, customizable, open-source SLiM 5 model for simulating the molecular evolution of the full human genome. We also show some simple analyses of results from the model, to illustrate its utility. We hope this model, which we have nicknamed "SimHumanity" in jest, will facilitate further progress in the field of human evolutionary simulations.

SLiM↗

PubMed2004

Scale-free networks versus evolutionary drift.

Recent studies of properties of various biological networks revealed that many of them display scale-free characteristics. Since the theory of scale-free networks is applicable to evolving networks, one can hope that it provides not only a model of a biological network in its current state but also sheds some insight into the evolution of the network. In this work, we investigate the probability distributions and scaling properties underlying some models for biological networks and protein domain evolution. The analysis of evolutionary models for domain similarity networks indicates that models which include evolutionary drift are typically not scale free. Instead they adhere quite closely to the Yule distribution. This finding indicates that the direct applicability of scale-free models in understanding the evolution of biological network may not be as wide as it has been hoped for.

Computational Biology↗

PubMed2002

Dynamics of bacterial phenotype selection in a colonized host.

The population dynamics of Helicobacter pylori during colonization in an infected animal host provide a quantifiable experimental model of in vivo microbial phenotype evolution. Phenotype variability in H. pylori populations can be typed as polymorphic expression of Lewis antigens on their cell surfaces. The high mutational frequency of H. pylori for Lewis expression provides substrate for differential selection by the host. Experimental challenge and successful colonization of mice and gerbils allows tracking of H. pylori phenotype variability from the initial inoculation to the ultimate establishment of a quasispecies. Colonization data provide a quantitative experimental model of phenotype evolution in a relatively large population (>10(4) individuals) over a relatively long evolutionary time scale (>10(3) generations). A mathematical model is developed to interpret the data in terms of the dynamic processes occurring during colonization. The mathematical model distinguishes the roles of selection and mutation; quantifies the effects of initial phenotype diversity, mutational frequency, and selective advantage; and applies generally to phenotype evolution in biological populations.

Animals↗

PubMed2002

Evolutionary speed of species invasions.

Successful invasion may depend of the capacity of a species to adjust genetically to a spatially varying environment. This research modeled a species invasion by examining the interaction between a quantitative genetic trait and population density. It assumed: (I) a quantitative genetic trait describes the adaptation of an individual to its local ecological conditions; (2) populations far from the local optimum grow more slowly than those near the optimum; and (3) the evolution of a trait depends on local population density, because differences in local population densities cause asymmetrical gene flow. This genetics-density interaction determined the propagation speed of populations. Numerical simulations showed that populations spread by advancing as two synchronic traveling waves, one for population density and one for trait adaptation. The form of the density wave was a step front that advances homogenizing populations at their carrying capacity; the adaptation wave was a curve with finite slope that homogenizes populations at full adaptation. The largest speed of population expansion, for a dimensionless analysis, corresponded to an almost homogeneous spatial environment when this model approached an ecological description such as the Fisher-Skellam's model. A large genetic response also favored faster speeds. Evolutionary speeds, in a natural scale, showed a wide range of rates that were also slower compared to models that only consider demographics. This evolutionary speed increased with high heritability, strong stabilizing selection, and high intrinsic growth rate. It decreased for steeper environmental gradients. Also indicated was an optimal dispersal rate over which evolutionary speed declined. This is expected because dispersal moves individuals further, but homogenizes populations genetically, making them maladapted. The evolutionary speed was compared to observed data. Furthermore, a moderate increase in the speed of expansion was predicted for ecological changes related to global warming.

Adaptation, Biological↗

PubMed2006

Scale invariant correlations between genes and SNPs on Human chromosome 1 reveal potential evolutionary mechanisms.

The local density of gene structures and single nucleotide polymorphisms (SNPs) along human chromosomes appears inhomogeneous. In chromosome 1, the density patterns from both these elements are shown here to exhibit similar scale invariant clustering, as well as long-ranged and scale invariant auto- and cross-correlations. The local densities of these elements sites can be accurately represented by the scale invariant exponential dispersion models, a group of stochastic models that act as limiting distributions for a wide range of generalized linear models. The scale invariant Poisson-gamma (PG) distribution is the most applicable of these models, since it describes the above findings and it lends itself to a stochastic mechanism for the accumulation of segmental chromosomal changes. This PG model describes the summation of neutral chromosomal mutations, deletions, rearrangements and recombinations, within chromosomal segments that are distinguished by their evolutionary genealogies. Scale invariance is a necessary property if such a description is to remain valid at different measurement scales. The observed density patterns, and proposed model, presumably represent the convergent summation of multiple stochastic processes within the evolutionary history of the chromosome.

Chromosomes, Human, Pair 1↗

PubMed2005

Modeling gene and genome duplications in eukaryotes.

Recent analysis of complete eukaryotic genome sequences has revealed that gene duplication has been rampant. Moreover, next to a continuous mode of gene duplication, in many eukaryotic organisms the complete genome has been duplicated in their evolutionary past. Such large-scale gene duplication events have been associated with important evolutionary transitions or major leaps in development and adaptive radiations of species. Here, we present an evolutionary model that simulates the duplication dynamics of genes, considering genome-wide duplication events and a continuous mode of gene duplication. Modeling the evolution of the different functional categories of genes assesses the importance of different duplication events for gene families involved in specific functions or processes. By applying our model to the Arabidopsis genome, for which there is compelling evidence for three whole-genome duplications, we show that gene loss is strikingly different for large-scale and small-scale duplication events and highly biased toward certain functional classes. We provide evidence that some categories of genes were almost exclusively expanded through large-scale gene duplication events. In particular, we show that the three whole-genome duplications in Arabidopsis have been directly responsible for >90% of the increase in transcription factors, signal transducers, and developmental genes in the last 350 million years. Our evolutionary model is widely applicable and can be used to evaluate different assumptions regarding small- or large-scale gene duplication events in eukaryotic genomes.

Arabidopsis↗

PubMed2001

Understanding hierarchical protein evolution from first principles.

We propose a model that explains the hierarchical organization of proteins in fold families. The model, which is based on the evolutionary selection of proteins by their native state stability, reproduces patterns of amino acids conserved across protein families. Due to its dynamic nature, the model sheds light on the evolutionary time-scales. By studying the relaxation of the correlation function between consecutive mutations at a given position in proteins, we observe separation of the evolutionary time-scales: at short time intervals families of proteins with similar sequences and structures are formed, while at long time intervals the families of structurally similar proteins that have low sequence similarity are formed. We discuss the evolutionary implications of our model. We provide a "profile" solution to our model and find agreement between predicted patterns of conserved amino acids and those actually observed in nature.

Amino Acid Sequence↗

PubMed2005

Gompertz mortality law and scaling behavior of the Penna model.

The Penna model is a model of evolutionary ageing through mutation accumulation where traditionally time and the age of an organism are treated as discrete variables and an organism's genome is represented by a binary bit string. We reformulate the asexual Penna model and show that a universal scale invariance emerges as we increase the number of discrete genome bits to the limit of a continuum. The continuum model, introduced by Almeida and Thomas [Int. J. Mod. Phys. C 11, 1209 (2000)] can be recovered from the discrete model in the limit of infinite bits coupled with a vanishing mutation rate per bit. Finally, we show that scale invariant properties may lead to the ubiquitous Gompertz law for mortality rates for early ages, which is generally regarded as being empirical.

Aging↗

PubMed2004

An evolutionary model for protein-coding regions with conserved RNA structure.

Here we present a model of nucleotide substitution in protein-coding regions that also encode the formation of conserved RNA structures. In such regions, apparent evolutionary context dependencies exist, both between nucleotides occupying the same codon and between nucleotides forming a base pair in the RNA structure. The overlap of these fundamental dependencies is sufficient to cause "contagious" context dependencies which cascade across many nucleotide sites. Such large-scale dependencies challenge the use of traditional phylogenetic models in evolutionary inference because they explicitly assume evolutionary independence between short nucleotide tuples. In our model we address this by replacing context dependencies within codons by annotation-specific heterogeneity in the substitution process. Through a general procedure, we fragment the alignment into sets of short nucleotide tuples based on both the protein coding and the structural annotation. These individual tuples are assumed to evolve independently, and the different tuple sets are assigned different annotation-specific substitution models shared between their members. This allows us to build a composite model of the substitution process from components of traditional phylogenetic models. We applied this to a data set of full-genome sequences from the hepatitis C virus where five RNA structures are mapped within the coding region. This allowed us to partition the effects of selection on different structural elements and to test various hypotheses concerning the relation of these effects. Of particular interest, we found evidence of a functional role of loop and bulge regions, as these were shown to evolve according to a different and more constrained selective regime than the nonpairing regions outside the RNA structures. Other potential applications of the model include comparative RNA structure prediction in coding regions and RNA virus phylogenetics.

Base Sequence↗

PubMed1993

Why some fitness landscapes are fractal.

Many biological and biochemical measurements, for example the "fitness" of a particular genome, or the binding affinity to a particular substrate, can be treated as a "fitness landscape", an assignment of numerical values to points in sequence space (or some other configuration space). As an alternative to the enormous amount of data required to completely describe such a landscape, we propose a statistical characterization, based on the properties of a random walk through the landscape and, more specifically, its autocorrelation function. Under assumptions roughly satisfied by two classes of simple model landscapes (the N-k model and the p-spin model) and by the landscape of estimated free energies of RNA secondary structures, this autocorrelation function, along with the mean and variance of individual points and the size of the landscape, completely characterize it. Having noted that these and other landscapes of estimated replication and degradation rates all have a well-defined correlation length, we propose a classification of landscapes depending on how the correlation length scales with the diameter of the landscape. The landscapes of some of the kinetic parameters of RNA molecules scale similarly to the model landscapes introduced into evolutionary studies from other fields, such as quadratic spin glasses and the traveling salesman problem, but the correlation length of RNA landscapes are considerably smaller. Nevertheless, both the model and some of the RNA landscapes satisfy a test of self-similarity proposed by Sorkin (1988).

Animals↗

PubMed2006

Systems biology as a foundation for genome-scale synthetic biology.

As the ambitions of synthetic biology approach genome-scale engineering, comprehensive characterization of cellular systems is required, as well as a means to accurately model cell-scale molecular interactions. These requirements are coincident with the goals of systems biology and, thus, systems biology will become the foundation for genome-scale synthetic biology. Systems biology will form this foundation through its efforts to reconstruct and integrate cellular systems, develop the mathematics, theory and software tools for the accurate modeling of these integrated systems, and through evolutionary mechanisms. As genome-scale synthetic biology is so enabled, it will prove to be a positive feedback driver of systems biology by exposing and forcing researchers to confront those aspects of systems biology which are inadequately understood.

Biology↗

PubMed2001

Birth of scale-free molecular networks and the number of distinct DNA and protein domains per genome.

MOTIVATION: Current growth in the field of genomics has provided a number of exciting approaches to the modeling of evolutionary mechanisms within the genome. Separately, dynamical and statistical analyses of networks such as the World Wide Web and the social interactions existing between humans have shown that these networks can exhibit common fractal properties-including the property of being scale-free. This work attempts to bridge these two fields and demonstrate that the fractal properties of molecular networks are linked to the fractal properties of their underlying genomes. RESULTS: We suggest a stochastic model capable of describing the evolutionary growth of metabolic or signal-transduction networks. This model generates networks that share important statistical properties (so-called scale-free behavior) with real molecular networks. In particular, the frequency of vertices connected to exactly k other vertices follows a power-law distribution. The shape of this distribution remains invariant to changes in network scale: a small subgraph has the same distribution as the complete graph from which it is derived. Furthermore, the model correctly predicts that the frequencies of distinct DNA and protein domains also follow a power-law distribution. Finally, the model leads to a simple equation linking the total number of different DNA and protein domains in a genome with both the total number of genes and the overall network topology. AVAILABILITY: MatLab (MathWorks, Inc.) programs described in this manuscript are available on request from the authors. CONTACT: ar345@columbia.edu.

Biological Evolution↗

PubMed2026

Large-scale genomic analysis places Chinese CC398 as a persistent human-associated MSSA lineage apart from the dominant global LA-MRSA clade.

Staphylococcus aureus clonal complex (CC)398 has emerged as a dominant livestock-associated methicillin-resistant S. aureus (LA-MRSA) lineage worldwide; however, its evolutionary trajectory and regional diversification remain incompletely understood. We developed a core-genome multilocus sequence typing (cgMLST) scheme with hierarchical clustering and applied it to over 30,000 S. aureus genomes, revealing frequent cross-border transmission of CC398. Subsequent time-calibrated phylogenetic analysis placed the most recent common ancestor at 1942 (95% CI: 1939-1945), with the human-to-livestock host jump around 1969 (95% CI: 1968-1972). Chinese CC398 exhibits a distinct trajectory: unlike the LA-MRSA lineages dominating Europe and North America, Chinese isolates are predominantly human-associated methicillin-susceptible S. aureus (HA-MSSA), forming unique East Asia-specific phylogroups (SAP1, SAP2, and AP1-AP3), with distinct resistance and virulence profiles. The LA lineage remains limited in China, with multinational mixed clusters emerging only after 2019. Analysis of global transmission networks revealed a significant correlation between LA-CC398 spread and international trade in fresh swine products, while no such correlation was observed for the human-associated lineage. Beyond the established lineage markers tet(M) and scn, our analysis identified additional differentially distributed genes, including cadC-a chromosomal cadmium resistance regulator-as a novel HA-lineage-enriched gene whose functional role in host adaptation remains to be determined. This study reveals that CC398 followed fundamentally different evolutionary paths in China versus Western countries, challenging a one-size-fits-all model of its dissemination.IMPORTANCEThis study illustrates how large-scale microbial genomics can resolve the evolutionary origins and regional diversification of bacterial pathogens. By applying a novel cgMLST scheme to over 30,000 S. aureus genomes, we show that CC398 followed fundamentally different evolutionary paths in China versus Western countries-challenging the prevailing model of uniform global dissemination-and that livestock-associated MRSA expansion is closely linked to international trade in fresh pork products. These findings highlight the need for integrated surveillance across human, animal, and trade interfaces to anticipate the emergence and spread of zoonotic pathogens.

Staphylococcus aureus↗