A synergetic model for the verbal transformation effect

We describe a nonlinear dynamical model based on synergetics for the auditory perceptual illusion known as the verbal transformation effect. The model is an extension of a synergetic model of perceptual oscillations of visual ambiguous figures. The main extension is connected to the number of reported alternative phonemic structures, which is typically much greater than the two or three alternatives usually reported in experiments with visual ambiguous figures. The properties of the model, which are derived using basic psychophysical principles, are presented and evaluated on the basis of the fit to earlier empirical work. It will be shown that there is very good agreement between the empirically observed properties of the verbal transformation effect and the properties detected by the model. Received: 19 February 1997 / Accepted in revised form: 27 May 1997     

Sex ratio under the haystack model

This paper investigates the evolution of the sex ratio under an extension of the haystack model of Maynard Smith (1964). At the beginning of each season a stack is colonized by a number of fertilized females, and their offspring breed there for several generations until new haystacks are available for colonization. We intend this as a model for populations which undergo periodical population explosions and crashes. With mating before dispersal, the number of generations in the stack has little effect on the equilibrium sex ratio, but it has a marked effect with mating after dispersal. This model is then used to investigate the evolutionary stability of the mechanism of sex determination found in the wood lemming which leads to a population sex ratio of three females to one male.     

Complexity, pleiotropy, and the fitness effect of mutations

One of the assumptions underlying many theoretical predictions in evolutionary biology concerns the distribution of the fitness effect of mutations. Approximations to this distribution have been derived using various theoretical approaches, of which Fisher's geometrical model is among the most popular ones. Two key concepts in this model are complexity and pleiotropy. Recent studies have proposed different methods for estimating how complexity varies across species, but their results have been contradictory. Here, we show that contradictory results are to be expected when the assumption of universal pleiotropy is violated. We develop a model in which the two key parameters are the total number of traits and the mean number of traits affected by a single mutation. We derive approximations for the distribution of the fitness effect of mutations when populations are either well-adapted or away from the optimum. We also consider drift load in a well-adapted population and show that it is independent of the distribution of the fitness effect of mutations. We show that mutation accumulation experiments can only measure the effect of the mean number of traits affected by mutations, whereas drift load only provides information about the total number of traits. We discuss the plausibility of the model.     

An infinitesimal model for quantitative trait genomic value prediction

We developed a marker based infinitesimal model for quantitative trait analysis. In contrast to the classical infinitesimal model, we now have new information about the segregation of every individual locus of the entire genome. Under this new model, we propose that the genetic effect of an individual locus is a function of the genome location (a continuous quantity). The overall genetic value of an individual is the weighted integral of the genetic effect function along the genome. Numerical integration is performed to find the integral, which requires partitioning the entire genome into a finite number of bins. Each bin may contain many markers. The integral is approximated by the weighted sum of all the bin effects. We now turn the problem of marker analysis into bin analysis so that the model dimension has decreased from a virtual infinity to a finite number of bins. This new approach can efficiently handle virtually unlimited number of markers without marker selection. The marker based infinitesimal model requires high linkage disequilibrium of all markers within a bin. For populations with low or no linkage disequilibrium, we develop an adaptive infinitesimal model. Both the original and the adaptive models are tested using simulated data as well as beef cattle data. The simulated data analysis shows that there is always an optimal number of bins at which the predictability of the bin model is much greater than the original marker analysis. Result of the beef cattle data analysis indicates that the bin model can increase the predictability from 10% (multiple marker analysis) to 33% (multiple bin analysis). The marker based infinitesimal model paves a way towards the solution of genetic mapping and genomic selection using the whole genome sequence data.     

A structural mixed model for variances in differential gene expression studies

The importance of variance modelling is now widely known for the analysis of microarray data. In particular the power and accuracy of statistical tests for differential gene expressions are highly dependent on variance modelling. The aim of this paper is to use a structural model on the variances, which includes a condition effect and a random gene effect, and to propose a simple estimation procedure for these parameters by working on the empirical variances. The proposed variance model was compared with various methods on both real and simulated data. It proved to be more powerful than the gene-by-gene analysis and more robust to the number of false positives than the homogeneous variance model. It performed well compared with recently proposed approaches such as SAM and VarMixt even for a small number of replicates, and performed similarly to Limma. The main advantage of the structural model is that, thanks to the use of a linear mixed model on the logarithm of the variances, various factors of variation can easily be incorporated in the model, which is not the case for previously proposed empirical Bayes methods. It is also very fast to compute and is adapted to the comparison of more than two conditions.     

Spatial distribution of dispersing animals

Summary A mathematical model for the dispersal of an animal population is presented for a system in which animals are initially released in the central region of a uniform field and migrate randomly, exerting mutually repulsive influences (population pressure) until they eventually become sedentary. The effect of the population pressure, which acts to enhance the dispersal of animals as their density becomes high, is modeled in terms of a nonlinear-diffusion equation. From this model, the density distribution of animals is obtained as a function of time and the initial number of released animals. The analysis of this function shows that the population ultimately reaches a nonzero stationary distribution which is confined to a finite region if both the sedentary effect and the population pressure are present. Our results are in good agreement with the experimental data on ant lions reported by Morisita, and we can also interpret some general features known for the spatial distribution of dispersing insects.     

Insect sociality and its consequential effects on intraspecific competition

Summary The phenomenon of colonialism which characterizes many insect groups is considered in terms of its potential effect on intraspecific competition. Intraspecific competition is assumed to be a function of the number of distinct pair encounters between 2 individuals of differing colony origin. A model is offered which describes the reduction in the number of potential competing encounters as a result of colonialism, and as such, combinatorial formulae are appropriate. For 2-colony and multi-colony systems, there is a proportionately smaller number of potential competing encounters as the size of the colonies becomes more inequitable and greater than 100 individuals in combined total. As a consequence, large inequitabilities in colony sizes are expected for nearest neighbor pairs, and thus a generally large variance in colony size for groups of colonies is also expected. Empirical data from various sources is presented and in good agreement with the predictions generated from the model.     

Assessing exposure-time treatment effect heterogeneity in stepped-wedge cluster randomized trials

A stepped-wedge cluster randomized trial (CRT) is a unidirectional crossover study in which timings of treatment initiation for clusters are randomized. Because the timing of treatment initiation is different for each cluster, an emerging question is whether the treatment effect depends on the exposure time, namely, the time duration since the initiation of treatment. Existing approaches for assessing exposure-time treatment effect heterogeneity either assume a parametric functional form of exposure time or model the exposure time as a categorical variable, in which case the number of parameters increases with the number of exposure-time periods, leading to a potential loss in efficiency. In this article, we propose a new model formulation for assessing treatment effect heterogeneity over exposure time. Rather than a categorical term for each level of exposure time, the proposed model includes a random effect to represent varying treatment effects by exposure time. This allows for pooling information across exposure-time periods and may result in more precise average and exposure-time-specific treatment effect estimates. In addition, we develop an accompanying permutation test for the variance component of the heterogeneous treatment effect parameters. We conduct simulation studies to compare the proposed model and permutation test to alternative methods to elucidate their finite-sample operating characteristics, and to generate practical guidance on model choices for assessing exposure-time treatment effect heterogeneity in stepped-wedge CRTs.     

Integrins mediate a neuronal survival signal for oligodendrocytes.

Target-dependent survival of newly differentiated cells is an important part of neural development. In the case of myelin-forming oligodendrocytes, it matches the number of oligodendrocytes to the available axons [1]. In addition to growth factors, an axonal signal regulates this survival: when axons are transected, oligodendrocytes die and, conversely, when the number of axons is increased by genetic manipulation, oligodendrocyte numbers increase [2] [3]. Newly formed oligodendrocytes that fail to contact axons undergo apoptosis, and co-culture experiments that model axon-glial interactions in vitro reveal a neuronal survival effect not present in neuron-conditioned medium [4] [5], suggesting that the signal is non-diffusible and present on the surface of axons. The nature of these neuronal signals is unknown, as are the mechanisms by which they interact with growth-factor-mediated survival signals. As integrins can regulate survival in other cell types [6] [7] [8], we determined whether integrins are involved in the neuronal survival effect. We found that the laminin receptor alpha6beta1 integrin, which is expressed on oligodendrocytes, enhances the sensitivity of oligodendrocytes to the survival effect of growth factors. On the basis of this interaction between integrin and growth-factor-mediated signalling, we propose a simple model by which signals from axons and other cell types might interact to regulate oligodendrocyte cell numbers.     

Nonlinear modelling of renal vasoaction

The control of blood pressure is a complex mixture of neural, hormonal and intrinsic interactions at the level of the heart, kidney and blood vessels. While experimental approaches to understanding these interactions are useful, it remains difficult to conduct experiments to quantify these interactions as the number of parameters increases. Thus, modelling of such physiological systems can offer considerable assistance. Typical mathematical models which describe the ability of the blood vessels to change their diameter (vasoconstriction) assume linearity of operation. However, due to the interaction of multiple vasocontrictive and vasodilative effectors, there is a significant nonlinear response to the influence of neural factors, particularly at higher levels of nerve activity (often seen in subjects with high blood pressure) which leads to low blood flow rates. This paper proposes a number of nonlinear mathematical models for the relationship between neural influences (sympathetic nerve activity (SNA)) and renal blood flow, using a feedback path to model the predominantly nonlinear effect of local vasoactive modulators such as nitric oxide, which oppose the action of SNA. The model structures are motivated by basic physiological principles, while the model parameters are determined using numerical optimisation techniques using open-loop data collected from rabbits. The models were verified by demonstrating correlation between experimental results and model outputs.     

Error-tolerant pooling designs with inhibitors.

Pooling designs are used in clone library screening to efficiently distinguish positive clones from negative clones. Mathematically, a pooling design is just a nonadaptive group testing scheme which has been extensively studied in the literature. In some applications, there is a third category of clones called "inhibitors" whose effect is to neutralize positives. Specifically, the presence of an inhibitor in a pool dictates a negative outcome even though positives are present. Sequential group testing schemes, which can be modified to three-stage schemes, have been proposed for the inhibitor model, but it is unknown whether a pooling design (a one-stage scheme) exists. Another open question raised in the literature is whether the inhibitor model can treat unreliable pool outcomes. In this paper, we answer both open problems by giving a pooling design, as well as a two-stage scheme, for the inhibitor model with unreliable outcomes. The number of pools required by our schemes are quite comparable to the three-stage scheme.     

Effects of anisotropic interactions on the structure of animal groups

This paper proposes an agent-based model which reproduces different structures of animal groups. The shape and structure of the group is the effect of simple interaction rules among individuals: each animal deploys itself depending on the position of a limited number of close group mates. The proposed model is shown to produce clustered formations, as well as lines and V-like formations. The key factors which trigger the onset of different patterns are argued to be the relative strength of attraction and repulsion forces and, most important, the anisotropy in their application.     

The forward rate of binding of surface-tethered reactants: effect of relative motion between two surfaces

The reaction of molecules confined to two dimensions is of interest in cell adhesion, specifically for the reaction between cell surface receptors and substrate-bound ligand. We have developed a model to describe the overall rate of reaction of species that are bound to surfaces under relative motion, such that the Peclet number is order one or greater. The encounter rate between reactive species is calculated from solution of the two-dimensional convection-diffusion equation. The probability that each encounter will lead to binding depends on the intrinsic rate of reaction and the encounter duration. The encounter duration is obtained from the theory of first passage times. We find that the binding rate increases with relative velocity between the two surfaces, then reaches a plateau. This plateau indicates that the increase in the encounter rate is counterbalanced by the decrease in the encounter duration as the relative velocity increases. The binding rate is fully described by two dimensionless parameters, the Peclet number and the Damk?hler number. We use this model to explain data from the cell adhesion literature by incorporating these rate laws into "adhesive dynamics" simulations to model the binding of a cell to a surface under flow. Leukocytes are known to display a "shear threshold effect" when binding selectin-coated surfaces under shear flow, defined as an increase in bind rate with shear; this effect, as calculated here, is due to an increase in collisions between receptor and ligand with increasing shear. The model can be used to explain other published data on the effect of wall shear rate on the binding of cells to surfaces, specifically the mild decrease in binding within a fixed area with increasing shear rate.     

A Pleiotropic Nonadditive Model of Variation in Quantitative Traits

A model of mutation-selection-drift balance incorporating pleiotropic and dominance effects of new mutations on quantitative traits and fitness is investigated and used to predict the amount and nature of genetic variation maintained in segregating populations. The model is based on recent information on the joint distribution of mutant effects on bristle traits and fitness in Drosophila melanogaster from experiments on the accumulation of spontaneous and P element-induced mutations. These experiments suggest a leptokurtic distribution of effects with an intermediate correlation between effects on the trait and fitness. Mutants of large effect tend to be partially recessive while those with smaller effect are on average additive, but apparently with very variable gene action. The model is parameterized with two different sets of information derived from P element insertion and spontaneous mutation data, though the latter are not fully known. They differ in the number of mutations per generation which is assumed to affect the trait. Predictions of the variance maintained for bristle number assuming parameters derived from effects of P element insertions, in which the proportion of mutations with an effect on the trait is small, fit reasonably well with experimental observations. The equilibrium genetic variance is nearly independent of the degree of dominance of new mutations. Heritabilities of between 0.4 and 0.6 are predicted with population sizes from 10(4) to 10(6), and most of the variance for the metric trait in segregating populations is due to a small proportion of mutations (about 1% of the total number) with neutral or nearly neutral effects on fitness and intermediate effects on the trait (0.1-0.5σ(P)). Much of the genetic variance is contributed by recessive or partially recessive mutants, but only a small proportion (about 10%) of the genetic variance is dominance variance. The amount of apparent selection on the trait itself generated by the model is very small. If a model is assumed in which all mutation events have an effect on the quantitative trait, the majority of the genetic variance is contributed by deleterious mutations with tiny effects on the trait. If such a model is assumed for viability, the heritability is about 0.1, independent of the population size.     

Pleiotropic Models of Quantitative Variation

It is widely held that each gene typically affects many characters, and that each character is affected by many genes. Moreover, strong stabilizing selection cannot act on an indefinitely large number of independent traits. This makes it likely that heritable variation in any one trait is maintained as a side effect of polymorphisms which have nothing to do with selection on that trait. This paper examines the idea that variation is maintained as the pleiotropic side effect of either deleterious mutation, or balancing selection. If mutation is responsible, it must produce alleles which are only mildly deleterious (s approximately 10(-3)), but nevertheless have significant effects on the trait. Balancing selection can readily maintain high heritabilities; however, selection must be spread over many weakly selected polymorphisms if large responses to artificial selection are to be possible. In both classes of pleiotropic model, extreme phenotypes are less fit, giving the appearance of stabilizing selection on the trait. However, it is shown that this effect is weak (of the same order as the selection on each gene): the strong stabilizing selection which is often observed is likely to be caused by correlations with a limited number of directly selected traits. Possible experiments for distinguishing the alternatives are discussed.     

Analysis of trinomial responses from reproductive and developmental toxicity experiments.

This paper presents a Dirichlet-trinomial distribution for modelling data obtained from reproductive and developmental studies. The common endpoints for the evaluation of reproductive and developmental toxic effects are the number of dead fetuses, the number of malformed fetuses, and the number of normal fetuses for each litter. With current statistical methods for the evaluation of reproductive and developmental effects, the effect on the number of deaths and the effect on the number of malformations are analyzed separately. The Dirichlet-trinomial model provides a procedure for the analysis of multiple endpoints simultaneously. This proposed Dirichlet-trinomial model is a generalization of the beta-binomial model that has been used for handling the litter effect in reproductive and developmental experiments. Likelihood ratio tests for differences in the number of deaths, the number of malformations, and the number of normals among dosed and control groups are derived. The proposed test procedure based on the Dirichlet-trinomial model is compared with that based on the beta-binomial model with an application to a real data set.     

A discrete cell model with adaptive signalling for aggregation of Dictyostelium discoideum.

Dictyostelium discoideum (Dd) is a widely studied model system from which fundamental insights into cell movement, chemotaxis, aggregation and pattern formation can be gained. In this system aggregation results from the chemotactic response by dispersed amoebae to a travelling wave of the chemoattractant cAMP. We have developed a model in which the cells are treated as discrete points in a continuum field of the chemoattractant, and transduction of the extracellular cAMP signal into the intracellular signal is based on the G protein model developed by Tang & Othmer. The model reproduces a number of experimental observations and gives further insight into the aggregation process. We investigate different rules for cell movement the factors that influence stream formation the effect on aggregation of noise in the choice of the direction of movement and when spiral waves of chemoattractant and cell density are likely to occur. Our results give new insight into the origin of spiral waves and suggest that streaming is due to a finite amplitude instability.     

The influence of a positive correlation between clutch size and offspring fitness on the optimal offspring size

Summary The effect is modeled of a positive relationship between clutch size and offspring fitness on the optimal investment in offspring. In species which meet the assumptions of the model, the model predicts a positive correlation between maternal resource level and offspring size. If larger mothers are able to allocate more resources to offspring, then the model would also predict a positive correlation between maternal size and offspring size when the assumptions of the model are met. Thus, this model may help explain both among and within individual variation in offspring size. When offspring are produced in groups and the number of offspring killed per clutch is limited by predator satiation, offspring in larger clutches may experience a higher probability of survival. Such a life style may be found in animals such as sea turtles. Offspring size is positively correlated with maternal size in some members of this group.     

Change in Genetic Variance under Selection in a Self-Fertilizing Population

In this study we show how the genetic variance of a quantitative trait changes in a self-fertilizing population under repeated cycles of truncation selection, with the analysis based on the infinitesimal model in which it is assumed that the trait is determined by an infinite number of unlinked loci without epistasis. The genetic variance is reduced not as a consequence of the genotypic frequency change but due to the build-up of linkage disequilibrium under truncation selection in this model. We assume that the order of the genotypic contribution from each locus is n(-1/2), where n is the number of loci involved, and investigate the change in linkage disequilibrium resulting from selection and self-fertilization using genotypic frequency dynamics in order to analyze the change in the genetic variance. Our analysis gives recurrence relations of genetic variance among the succeeding generations for the three cases of gene action, i.e., purely additive action, pure dominance without additive effect and the presence of both additive effect and dominance, respectively. Numerical examples are also given as a check on the recurrence formulas.