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PubMed2005

Theoretical models of selection and mutation on quantitative traits.

Empirical studies of quantitative genetic variation have revealed robust patterns that are observed both across traits and across species. However, these patterns have no compelling explanation, and some of the observations even appear to be mutually incompatible. We review and extend a major class of theoretical models, 'mutation-selection models', that have been proposed to explain quantitative genetic variation. We also briefly review an alternative class of 'balancing selection models'. We consider to what extent the models are compatible with the general observations, and argue that a key issue is understanding and modelling pleiotropy. We discuss some of the thorny issues that arise when formulating models that describe many traits simultaneously.

Evolution, Molecular↗
PubMed1997

Predictions of a recurrent model of orientation selectivity.

Recurrent models of orientation selectivity in the visual cortex postulate that an initially broad tuning given by the pattern of geniculate afferents is substantially sharpened by intracortical feedback. We show that these models can be tested on the basis of their predicted responses to certain visual stimuli, without the need for pharmacological or physiological manipulations. First, we consider a detailed recurrent model proposed by Somers, Nelson and Sur [(1995) Journal of Neuroscience, 15, 5448-5465] and show that it can be simplified to a single equation: a center-surround feedback filter in the orientation domain. Then, we explore the responses of the simplified model to stimuli containing two or more orientations. We find that the model exhibits peculiar responses to stimuli containing two orientations, such as plaids or crosses: if the component orientations differ by less than 45 deg the model cannot distinguish between them; if the orientations differ by more than 45 deg the model overestimates their angle by as much as 30 deg. Moreover, the model cannot signal the presence of three orientations separated by 60 deg (it responds as if there were only two orientations), and the addition of two-dimensional visual noise to an oriented stimulus results in strong spurious responses at the orthogonal orientation. We argue that the effects of attraction and repulsion between orientations and the emergence of responses at off-optimal orientations are common to a wide class of feedback models of orientation selectivity. These models could thus be tested by measuring the visual responses of cortical neurons to stimuli containing multiple orientations.

Feedback↗
PubMed2003

A two-locus gene conversion model with selection and its application to the human RHCE and RHD genes.

A two-locus gene conversion model with selection is developed. Under the joint action of selection, mutation, gene conversion, recombination, and random genetic drift, approximate formulas for the expectations of the moments of allele frequencies and the expected amounts of variation within and between two loci are obtained by a diffusion method assuming relatively strong selection. It is shown that the pattern of allelic variation is mainly determined by the balance between gene conversion and selection, because these two mechanisms act in opposite directions. As an application of the theoretical results, the human RHCE and RHD genes are considered. The very high level of amino acid divergence between the two genes is observed only in a short region around exon 7. It is known that exon 7 encodes amino acids that characterize the difference between the RHCE and RHD antigens. The observed pattern of DNA variation in this region is consistent with the selection model developed in this article, suggesting that strong selection might be working to maintain the RHCE/RHD antigen variation in the two-locus system. The selection intensity is estimated on the basis of the theoretical result.

Alleles↗
PubMed2006

Synthetic computational models of selective attention.

Computational modeling plays an important role to understand the mechanisms of attention. In this framework, synthetic computational models can uniquely contribute to integrate different explanatory levels and neurocognitive findings, with special reference to the integration of attention and awareness processes. Novel combined experimental and computational investigations can lead to important insights, as in the revived domain of neural correlates of attention- and awareness-related meditation states and traits.

Attention↗
PubMed1992

On models of quantitative genetic variability: a stabilizing selection-balance model.

A model of stabilizing selection on a multilocus character is proposed that allows the maintenance of stable allelic polymorphism and linkage disequilibrium. The model is a generalization of Lerner's model of homeostasis in which heterozygotes are less susceptible to environmental variation and hence are superior to homozygotes under phenotypic stabilizing selection. The analysis is carried out for weak selection with a quadratic-deviation model for the stabilizing selection. The stationary state is characterized by unequal allele frequencies, unequal proportions of complementary gametes, and a reduction of the genetic (and phenotypic) variance by the linkage disequilibrium. The model is compared with Mather's polygenic balance theory, with models that include mutation-selection balance, and others that have been proposed to study the role of linkage disequilibrium in quantitative inheritance.

Alleles↗
PubMed2006

ModelTest Server: a web-based tool for the statistical selection of models of nucleotide substitution online.

ModelTest server is a web-based application for the selection of models of nucleotide substitution using the program ModelTest. The server takes as input a text file with likelihood scores for the set of candidate models. Models can be selected with hierarchical likelihood ratio tests, or with the Akaike or Bayesian information criteria. The output includes several statistics for the assessment of model selection uncertainty, for model averaging or to estimate the relative importance of model parameters. The server can be accessed at http://darwin.uvigo.es/software/modeltest_server.html.

Base Composition↗
PubMed1988

The coalescent process in models with selection.

Statistical properties of the process describing the genealogical history of a random sample of genes are obtained for a class of population genetics models with selection. For models with selection, in contrast to models without selection, the distribution of this process, the coalescent process, depends on the distribution of the frequencies of alleles in the ancestral generations. If the ancestral frequency process can be approximated by a diffusion, then the mean and the variance of the number of segregating sites due to selectively neutral mutations in random samples can be numerically calculated. The calculations are greatly simplified if the frequencies of the alleles are tightly regulated. If the mutation rates between alleles maintained by balancing selection are low, then the number of selectively neutral segregating sites in a random sample of genes is expected to substantially exceed the number predicted under a neutral model.

Genealogy and Heraldry↗
PubMed1993

Dynamic selection of models for a ventilator-management advisor.

A ventilator-management advisor (VMA) is a computer program that monitors patients who are treated with a mechanical ventilator. A VMA implements a patient-specific physiologic model to interpret patient data and to predict the effects of alternative control settings for the ventilator. Because a VMA evaluates its physiologic model repeatedly during each cycle of data interpretation, highly complex models may require more computation time than is available in this time-critical application. On the other hand, less complex models may be inaccurate if they are unable to represent a patient's physiologic abnormalities. For each patient, a VMA should select a model that balances the tradeoff of prediction accuracy and computation-time complexity. I present a method to select models that are at an appropriate level of detail for time-constrained decision tasks. The method is based on a local search in a graph of models (GoM) for a model that maximizes the tradeoff of computation-time complexity and prediction accuracy. For each model under consideration, a belief network computes a probability of model adequacy given the qualitative prior information, and the goodness of fit of the model to the data provides a measure of the conditional probability of adequacy given the quantitative observations. I apply this method to the problem of model selection for a VMA. I describe an implementation of a graph of physiologic models that range in complexity from VentPlan, a simple model with 3 compartments, to VentSim, a multicompartment model with detailed airway, circulation and mechanical ventilator components.(ABSTRACT TRUNCATED AT 250 WORDS)

Computer Simulation↗
PubMed1994

Branch nucleophile selection in pre-mRNA splicing: evidence for the bulged duplex model.

Selection of the nucleophile for the first step of nuclear pre-mRNA splicing was probed by site-specific incorporation into splicing substrates of nucleotides modified at the 2' position. The differing abilities of ribose, 2'-deoxyribose, and arabinose nucleotides to base-pair within an RNA.RNA duplex and to contribute a nucleophilic 2'-OH group were exploited to analyze the paired/unpaired disposition of the branch site nucleotide. The results provide direct evidence for a bulged duplex model in which either of two adjacent purines within the consensus branch site sequence may shift into a bulged position and contribute the 2'-OH group for the first step of splicing. Furthermore, the presence of a consensus branch site that cannot present a reactive nucleophile suppresses splicing, including the use of cryptic branch sites elsewhere. We conclude that the branch site region base-pairing with U2 snRNA determines the first step nucleophile and persists at the time of the first transesterification reaction.

Arabinose↗
PubMed2006

Adenosine receptor modelling. A1/A2a selectivity.

Three-dimensional models of the A(1) and A(2a) adenosine receptors (AR) were constructed by means of a homology procedure, using bovine rhodopsin as a template. In order to validate the two models, a docking analysis of selective agonists was carried out. The study shows that A(1)/A(2a) selectivity is mainly influenced by the different ability of the two receptors to give lipophilic interactions, instead of giving different H bonds. The binding site cavity of the A(1)AR is smaller than that of the A(2a)AR, and for this reason, less bulky ligands like CPA are able to give close interactions with the A(1)AR, unlike larger ligands such as CGS-21680. The different dimensions of the binding site cavity could be due to the presence of three residues of proline, which cause a different rearrangement of the TM, thus modifying the side chain disposition inside the inter-helix channel.

Adenosine↗
PubMed2005

All systems go: launching cell simulation fueled by integrated experimental biology data.

Biological simulation serves to unify the basic elements of systems biology, namely, model selection, experimentation and model refinement. To select biochemical models for simulation, metabolome analysis can be performed using capillary electrophoresis or liquid chromatography coupled with mass spectrometry. In this manner, selected models can be elaborated with temporal/spatial gene and protein expression data obtained from model organisms such as Escherichia coli. The E. coli single gene deletion mutant library (KO collection) and His-tag/GFP-fusion single open reading frame clone expression library (ASKA) are powerful resources for this task. The integration of parallel experimental datasets into dynamic simulation tools forms the remaining challenge for the systematic analysis and elucidation of biological networks and holds promise for biotechnological applications.

Cell Physiological Phenomena↗
PubMed1994

A selective, non-ischemic, non-pharmacological left ventricular failure animal model.

Selective left ventricular failure was induced in 13 acute anesthetized, closed chest dogs ranging in weight from 18-26 kg. Failure was induced by passing a single, high intensity pulse of current from a defibrillator connected to a left ventricular catheter electrode and a left chest electrode. The intensity of the myocardial damaging shock was related to the predicted current required for transventricular defibrillation, based on heart weight. Thermodilution cardiac output, left ventricular pressure, impedance stroke volume, the cardiac electrogram, and lead II ECG were recorded, along with the pressure impedance (volume) loop, which is a measure of stroke work. It was found that the cardiac output decreased with increasing current intensity. Immediately following the high current shock, cardiac output, and stroke work decreased. In some animals, with a moderate intensity shock, there was a transient increase in cardiac output, followed by a decrease. In the five animals that were monitored continuously for 4 hours, the average percent reduction in cardiac output at this time was 42.5% for an average current overdose ratio of 5.39. The energy setting on the defibrillator to obtain this range of reduction in cardiac output was 175-350 joules. The method described herein is easily applied to the closed chest animal and will allow evaluation of the pumping capabilities of cardiac augmentation techniques, such as dynamic cardio-myoplasty and the skeletal muscle ventricle.

Animals↗
PubMedDate not supplied

[A screening model for selecting atypical anxiolytics].

A model has been developed for screening atypical tranquilizers, which is based on the intrinsic property of mice and rats to avoid the brightly lit part of a chamber and on the effect of "optic precipice". The model enables one not only to select anxiolytic agents, but to differentiate them as atypical and classical tranquilizers.

Animals↗
PubMed2002

The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.

BACKGROUND: The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets. RESULTS: The gene selection model presented herein is based on the observation that: (1) variance of gene expression is a function of absolute expression; (2) one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3) this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SDwithin) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function. CONCLUSION: The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.

Animals↗
PubMed2006

Selecting the best home range model: an information-theoretic approach.

Choosing an appropriate home range model is important for describing space use by animals and understanding the ecological processes affecting animal movement. Traditional approaches for choosing among home range models have not resulted in general, consistent, and unambiguous criteria that can be applied to individual data sets. We present a new application of information-theoretic model selection that overcomes many of the limitations of traditional approaches, as follows. (1) It alleviates the need to know the true home range to assess home range models, thus allowing performance to be evaluated with data on individual animals. (2) The best model can be chosen from a set of candidate models with the proper balance between fit and complexity. (3) If candidate home range models are based on underlying ecological processes, researchers can use the selected model not only to describe the home range, but also to infer the importance of various ecological processes affecting animal movements within the home range.

Animal Migration↗
PubMed2006

Likelihood methods for detecting temporal shifts in diversification rates.

Maximum likelihood is a potentially powerful approach for investigating the tempo of diversification using molecular phylogenetic data. Likelihood methods distinguish between rate-constant and rate-variable models of diversification by fitting birth-death models to phylogenetic data. Because model selection in this context is a test of the null hypothesis that diversification rates have been constant over time, strategies for selecting best-fit models must minimize Type I error rates while retaining power to detect rate variation when it is present. Here I examine model selection, parameter estimation, and power to reject the null hypothesis using likelihood models based on the birth-death process. The Akaike information criterion (AIC) has often been used to select among diversification models; however, I find that selecting models based on the lowest AIC score leads to a dramatic inflation of the Type I error rate. When appropriately corrected to reduce Type I error rates, the birth-death likelihood approach performs as well or better than the widely used gamma statistic, at least when diversification rates have shifted abruptly over time. Analyses of datasets simulated under a range of rate-variable diversification scenarios indicate that the birth-death likelihood method has much greater power to detect variation in diversification rates when extinction is present. Furthermore, this method appears to be the only approach available that can distinguish between a temporal increase in diversification rates and a rate-constant model with nonzero extinction. I illustrate use of the method by analyzing a published phylogeny for Australian agamid lizards.

Animals↗
PubMed2001

Adjustment for selection bias in cohort studies: an application of a probit model with selectivity to life course epidemiology.

Sample attrition is potentially a source of bias in cohort studies. The outcome may not be observed in a considerable proportion of the subjects. This article proposes the application of a probit model with sample selection to handle the problem. Two equations are simultaneously estimated and their error terms allowed to correlate: one regressing an observed outcome on a set of baseline variables, another regressing the probability of the outcome being observed upon a set of (perhaps the same) baseline variables. The method was applied to a study of a birth cohort, half of whose members were interviewed again at age 26. Baseline variables were observed for all the subjects included. The focus was on the association between birth weight and mental health in adults. The probit model with sample selection revealed a stronger and more significant (P = 0.037) relation between birth weight and mental health than an ordinary probit regression model (P = 0.170). Interpretation and practical considerations are discussed.

Adult↗
PubMed2005

Selection of microalgal growth model for describing specific growth rate-light response using extended information criterion.

The effects of light intensity and carbon dioxide (CO2) concentration on the growth of the highly CO2-tolerant green alga Chlorococcum littorale were studied in batch cultures. Four mathematical representations were compared for the specific growth rate-light response curve: a rectangular hyperbolic function, Steele's exponential function, a Poisson function and a hyperbolic tangent function. The hyperbolic tangent function, which is commonly used for representing the photosynthesis-light relationship, gave the best fit as evaluated by the extended information criterion (EIC). EIC proved to be applicable as a criterion to this kind of nonlinear model selection problem. Carbon dioxide, the sole carbon source for photoautotrophic growth of this alga, inhibited the growth rate at concentrations higher than pCO2 of 0.02. A substrate inhibition model was successfully used to simulate the relationship between the specific growth rate and CO2 response.

Carbon Dioxide↗