Application of dynamic metabolic flux analysis for process modeling: Robust flux estimation with regularization,confidence bounds,and selection of elementary modes

In macroscopic dynamic models of fermentation processes, elementary modes (EM) derived from metabolic networks are often used to describe the reaction stoichiometry in a simplified manner and to build predictive models by parameterizing kinetic rate equations for the EM. In this procedure, the selection of a set of EM is a key step which is followed by an estimation of their reaction rates and of the associated confidence bounds. In this paper, we present a method for the computation of reaction rates of cellular reactions and EM as well as an algorithm for the selection of EM for process modeling. The method is based on the dynamic metabolic flux analysis (DMFA) proposed by Leighty and Antoniewicz (2011, Metab Eng, 13(6), 745–755) with additional constraints, regularization and analysis of uncertainty. Instead of using estimated uptake or secretion rates, concentration measurements are used directly to avoid an amplification of measurement errors by numerical differentiation. It is shown that the regularized DMFA for EM method is significantly more robust against measurement noise than methods using estimated rates. The confidence intervals for the estimated reaction rates are obtained by bootstrapping. For the selection of a set of EM for a given st oichiometric model, the DMFA for EM method is combined with a multiobjective genetic algorithm. The method is applied to real data from a CHO fed-batch process. From measurements of six fed-batch experiments, 10 EM were identified as the smallest subset of EM based upon which the data can be described sufficiently accurately by a dynamic model. The estimated EM reaction rates and their confidence intervals at different process conditions provide useful information for the kinetic modeling and subsequent process optimization.     

脑电信号的高阶奇异谱分析

奇异谱分析是脑电信号分析的一种新方法,脑电信号的奇异谱可以反映脑电的特征,它有助于研究大脑的动力学行为。奇异谱分析方法是基于二阶统计的方法,反映的是信号时间上和空间上的一种线性相关关系。而脑电信号属于非线性信号,其内在的非线性关系很难通过奇异谱得到真实的反映,从而会丢失某些有用的信息。提出一种新的基于高阶统计的脑电奇异谱分析方法,并将其运用于正常脑电和癫痫患者的脑电分析中。大量的实测信号样本仿真实验结果表明,正常脑电和癫痫脑电的奇异谱有明显的不同。此外,基于高阶统计的奇异谱和基于二阶统计的奇异谱相比更能反映出信号的细节。     

Up hill, down dale: quantitative genetics of curvaceous traits

'Repeated' measurements for a trait and individual, taken along some continuous scale such as time, can be thought of as representing points on a curve, where both means and covariances along the trajectory can change, gradually and continually. Such traits are commonly referred to as 'function-valued' (FV) traits. This review shows that standard quantitative genetic concepts extend readily to FV traits, with individual statistics, such as estimated breeding values and selection response, replaced by corresponding curves, modelled by respective functions. Covariance functions are introduced as the FV equivalent to matrices of covariances. Considering the class of functions represented by a regression on the continuous covariable, FV traits can be analysed within the linear mixed model framework commonly employed in quantitative genetics, giving rise to the so-called random regression model. Estimation of covariance functions, either indirectly from estimated covariances or directly from the data using restricted maximum likelihood or Bayesian analysis, is considered. It is shown that direct estimation of the leading principal components of covariance functions is feasible and advantageous. Extensions to multi-dimensional analyses are discussed.     

Variation,selection and evolution of function-valued traits

We describe an emerging framework for understanding variation, selection and evolution of phenotypic traits that are mathematical functions. We use one specific empirical example – thermal performance curves (TPCs) for growth rates of caterpillars – to demonstrate how models for function-valued traits are natural extensions of more familiar, multivariate models for correlated, quantitative traits. We emphasize three main points. First, because function-valued traits are continuous functions, there are important constraints on their patterns of variation that are not captured by multivariate models. Phenotypic and genetic variation in function-valued traits can be quantified in terms of variance-covariance functions and their associated eigenfunctions: we illustrate how these are estimated as well as their biological interpretations for TPCs. Second, selection on a function-valued trait is itself a function, defined in terms of selection gradient functions. For TPCs, the selection gradient describes how the relationship between an organism's performance and its fitness varies as a function of its temperature. We show how the form of the selection gradient function for TPCs relates to the frequency distribution of environmental states (caterpillar temperatures) during selection. Third, we can predict evolutionary responses of function-valued traits in terms of the genetic variance-covariance and the selection gradient functions. We illustrate how non-linear evolutionary responses of TPCs may occur even when the mean phenotype and the selection gradient are themselves linear functions of temperature. Finally, we discuss some of the methodological and empirical challenges for future studies of the evolution of function-valued traits.     

Spatiotemporal dynamics of two generic predator-prey models

We present the analysis of two reaction-diffusion systems modelling predator-prey interactions, where the predator displays the Holling type II functional response, and in the absence of predators, the prey growth is logistic. The local analysis is based on the application of qualitative theory for ordinary differential equations and dynamical systems, while the global well-posedness depends on invariant sets and differential inequalities. The key result is an L (∞)-stability estimate, which depends on a polynomial growth condition for the kinetics. The existence of an a priori L ( p )-estimate, uniform in time, for all p≥1, implies L (∞)-uniform bounds, given any nonnegative L (∞)-initial data. The applicability of the L (∞)-estimate to general reaction-diffusion systems is discussed, and how the continuous results can be mimicked in the discrete case, leading to stability estimates for a Galerkin finite-element method with piecewise linear continuous basis functions. In order to verify the biological wave phenomena of solutions, numerical results are presented in two-space dimensions, which have interesting ecological implications as they demonstrate that solutions can be 'trapped' in an invariant region of phase space.     

Expert-based versus habitat-suitability models to develop resistance surfaces in landscape genetics

Landscape genetics aims to investigate functional connectivity among wild populations by evaluating the impact of landscape features on gene flow. Genetic distances among populations or individuals are generally better explained by least-cost path (LCP) distances derived from resistance surfaces than by simple Euclidean distances. Resistance surfaces reflect the cost for an organism to move through particular landscape elements. However, determining the effects of landscape types on movements is challenging. Because of a general lack of empirical data on movements, resistance surfaces mostly rely on expert knowledge. Habitat-suitability models potentially provide a more objective method to estimate resistance surfaces than expert opinions, but they have rarely been applied in landscape genetics so far. We compared LCP distances based on expert knowledge with LCP distances derived from habitat-suitability models to evaluate their performance in landscape genetics. We related all LCP distances to genetic distances in linear mixed effect models on an empirical data set of wolves (Canis lupus) from Italy. All LCP distances showed highly significant (P ≤ 0.0001) standardized β coefficients and R ² values, but LCPs from habitat-suitability models generally showed higher values than those resulting from expert knowledge. Moreover, all LCP distances better explained genetic distances than Euclidean distances, irrespective of the approaches used. Considering our results, we encourage researchers in landscape genetics to use resistance surfaces based on habitat suitability which performed better than expert-based LCPs in explaining patterns of gene flow and functional connectivity.     

Fitting Age Related Reference Intervals to Ocular Axial Length in Children Using a Four-Parameter Curve Family

There exist a number of methods to determine age dependent reference intervals. Some of those are based on standard parametric classes of distributions like normal or lognormal and standard parametric classes of age functions like linear or polynomial of some order. Others are based on more flexible distribution classes like Box-Cox transformation of the normal distribution, which allows for skewness. There exist also purely nonparametric methods, where the bounds of the reference intervals are only assumed to be nondecreasing and they are directly estimated by a suitable error function without any distributional assumption. In this paper we propose a flexible four-parameter age function class for the reference interval bounds and a method to estimate those. The four parameters in the class have concrete meanings; starting value at age 0, asymptotic value at increasing age, time scale and shape. The function class satisfies some desirable properties, which are discussed. The estimation of the parameters in the model uses the same type of error function as in the purely nonparametric methods. With our method we also get an estimate of the distributional position of an observation for a new individual given its age. The method is illustrated by an application example, where a 90% reference interval for ocular axis length of children up to age 18 years are determined.     

Modelling habitat-suitability using museum collections: an example with three sympatric Apodemus species from the Alps

Aim, Location Although the alpine mouse Apodemus alpicola has been given species status since 1989, no distribution map has ever been constructed for this endemic alpine rodent in Switzerland. Based on redetermined museum material and using the Ecological-Niche Factor Analysis (ENFA), habitat-suitability maps were computed for A. alpicola , and also for the co-occurring A. flavicollis and A. sylvaticus .
Methods In the particular case of habitat suitability models, classical approaches (GLMs, GAMs, discriminant analysis, etc.) generally require presence and absence data. The presence records provided by museums can clearly give useful information about species distribution and ecology and have already been used for knowledge-based mapping. In this paper, we apply the ENFA which requires only presence data, to build a habitat-suitability map of three species of Apodemus on the basis of museum skull collections.
Results Interspecific niche comparisons showed that A. alpicola is very specialized concerning habitat selection, meaning that its habitat differs unequivocally from the average conditions in Switzerland, while both A. flavicollis and A. sylvaticus could be considered as 'generalists' in the study area.
Main conclusions Although an adequate sampling design is the best way to collect ecological data for predictive modelling, this is a time and money consuming process and there are cases where time is simply not available, as for instance with endangered species conservation. On the other hand, museums, herbariums and other similar institutions are treasuring huge presence data sets. By applying the ENFA to such data it is possible to rapidly construct a habitat suitability model. The ENFA method not only provides two key measurements regarding the niche of a species (i.e. marginality and specialization), but also has ecological meaning, and allows the scientist to compare directly the niches of different species.     

Cost-effectiveness of habitat-suitability maps using low-detailed data for elusive bat species

European bat species are strictly protected by law, and the Member States of the European Union are obliged to record species condition and to contribute to their conservation. Habitat-suitability models are an essential aid in assessing the conservation status and distribution of a species. However, model performance depends on the data quality. This study compares habitat-suitability models that were generated from two data sets that differ in the degree of details included. The first model used data that were low in detail but freely available and the second used data that were very detailed but costly. Three hypotheses were addressed: (1) that the model using low-detailed data is sufficient in its performance to aid the assessment of species distribution and infrastructural planning; (2) the visualisation of actual species distribution is more accurate in the high-detailed model; and (3) habitat-suitability maps can depict species distribution better than species occurrence data alone. To develop models, climate, geographic and roosting data of Myotis bechsteinii were used. Models allowed very good spatial predictions of suitable habitats. However, the model using low-detailed data overestimated suitable habitat. The high-detailed model was more able to predict actual species distribution. These findings were supported by field evaluation where M. bechsteinii could only be detected in areas where both models predicted high habitat suitability. This framework is promising as it resulted in spatially explicit habitat-suitability maps and suggests that similar models may be used to improve the understanding of bat distribution and factors endangering other species of bats.     

MANOVA multiple comparisons using the generalized step-down procedure

If the variables in MANOVA problem can be arranged according to the order of their importance, then J. ROY'S (1958) step-down procedure may be more appropriate than the conventional invariant inference techniques. However, it may often be possible only to identify subsets such that variables within subsets are equally important and subsets are of unequal importance. In experimental situations, it is common to have a set of variables of primary interest and another of “addon” variables. The step-down reasoning is extended to such cases and a set of simultaneous confidence bounds based upon the procedure which uses the largest root criterion at each stage are derived. The confidence bounds are on all linear functions of means only that do not involve nuisance parameters, and are therefore suitable for studying the configuration of means. This method yields shorter intervals for contrasts among the means of the variables of primary interest compared with the conventional intervals based upon the largest root. The method is illustrated using BARNARD'S data (1935) on skull characters.     

Estimation of genetic parameters for partial egg production periods by means of random regression models

We estimated genetic parameters for egg production in different periods by means of random regression models, aiming at selection based on partial egg production from a generation of layers. The production was evaluated for each individual by recording the number of eggs produced from 20 to 70 weeks of age, with partial records taken every three weeks for a total of 17 periods. The covariance functions were estimated with a random regression model by the restricted maximum likelihood method. A model composed of third-order polynomials for the additive effect, ninth-order polynomials for the permanent environment, and a residual variance structure with five distinct classes, was found to be most suitable for adjusting the egg production data for laying hens. The heritability estimates varied from 0.04 to 0.14. The genetic correlations were all positive, varying from 0.10 to 0.99. Selection applied in partial egg production periods will result in greater genetic profit for the adjacent periods. However, as the distance in time between periods increases, selection becomes less efficient. Selection based on the second period (23 to 25 weeks of age), where greater heritability was estimated, would note benefit the final egg-laying cycle periods.     

Wind power prediction based on neural network with optimization of adaptive multi-group salp swarm algorithm

Salp swarm algorithm (SSA) is a swarm intelligence algorithm inspired by the swarm behavior of salps in oceans. In this paper, a adaptive multi-group salp swarm algorithm (AMSSA) with three new communication strategies is presented. Adaptive multi-group mechanism is to evenly divide the initial population into several subgroups, and then exchange information among subgroups after each adaptive iteration. Communication strategy is also an important part of adaptive multi-group mechanism. This paper proposes three new communication strategies and focuses on promoting the performance of SSA. These measures significantly improve the cooperative ability of SSA, accelerate convergence speed, and avoid easily falling into local optimum. And the benchmark functions confirm that AMSSA is better than the original SSA in exploration and exploitation. In addition, AMSSA is combined with prediction of wind power based on back propagation (AMSSA-BP) neural network. The simulation results show that the AMSSA-BP neural network prediction model can achieve a better prediction effect of wind power.

     

UNIFICATION OF REGRESSION‐BASED METHODS FOR THE ANALYSIS OF NATURAL SELECTION

Regression analyses are central to characterization of the form and strength of natural selection in nature. Two common analyses that are currently used to characterize selection are (1) least squares–based approximation of the individual relative fitness surface for the purpose of obtaining quantitatively useful selection gradients, and (2) spline‐based estimation of (absolute) fitness functions to obtain flexible inference of the shape of functions by which fitness and phenotype are related. These two sets of methodologies are often implemented in parallel to provide complementary inferences of the form of natural selection. We unify these two analyses, providing a method whereby selection gradients can be obtained for a given observed distribution of phenotype and characterization of a function relating phenotype to fitness. The method allows quantitatively useful selection gradients to be obtained from analyses of selection that adequately model nonnormal distributions of fitness, and provides unification of the two previously separate regression‐based fitness analyses. We demonstrate the method by calculating directional and quadratic selection gradients associated with a smooth regression‐based generalized additive model of the relationship between neonatal survival and the phenotypic traits of gestation length and birth mass in humans.     

Unit-treatment interaction and its practical consequences

Most statistical characterizations of a treatment effect focus on the average effect of the treatment over an entire population. However, average effects may provide inadequate information, sometimes misleading information, when a substantial unit-treatment interaction is present in the population. It is even possible that a nonnegligible proportion of the individuals in the population experience an unfavorable treatment effect even though the treatment might appear to be beneficial when considering population averages. This paper examines the extent to which information about unit-treatment interaction can be extracted using observed data from a two-treatment completely randomized experiment. A method for utilizing the information from an available covariate is proposed. Although unit-treatment interaction is a nonidentifiable quantity, we show that mathematical bounds for it can be estimated from observed data. These bounds lead to estimated bounds for the probability of an unfavorable treatment effect. Maximum likelihood estimators of the bounds and their corresponding large-sample distributions are given. The use of the estimated bounds is illustrated in a clinical trials data example.     

Foundations of gregariousness: a dispersal polymorphism among the planktonic larvae of a marine invertebrate

Theory predicts that selection should favor genotypes that can vary their tendency to disperse in habitats that are spatially or temporally variable or those that remain near their carrying capacity. Although many marine habitats appear to fit these criteria, confirmed examples of dispersal polymorphism among marine invertebrates are exceedingly rare. Competent larvae of the gregarious tubeworm, Hydroides dianthus, settle specifically in response to living conspecific worms, but a small proportion of each spawn settle nonspecifically on uninhabited substrata concurrently with their gregarious siblings. Here, using a parental half-sib analysis, we show that the proportion of a spawn settling in response to uninhabited biofilm is highly heritable. When estimated as a continuous trait based on a one-way ANOVA, heritability is estimated to be 0.83 +/- 0.31. When founder production was analyzed as a threshold trait, heritability was estimated to be 0.68 +/- 0.10 based on the breeding design experiment and 0.65 +/- 0.09 based on the artificial selection experiments. Realized heritability based on the selection experiments was considerably lower, however (0.17 per generation and 0.02 cumulative). Artificial selection was ineffectual at sequentially increasing the proportion of founder larvae among inbred family lines, but after three generations of selection, the proportion of larvae settling in response to biofilm was significantly higher among inbred lines than among the field-collected parents. The obligate planktonic larval stage common among so many marine invertebrates is thought to preclude the evolution of dispersal polymorphisms in these animals. Theoretical expectations of variable dispersal may instead be realized through individual behavioral differences resulting in differential transport or settlement preference, but this possibility remains largely unexplored among marine invertebrates.     

The fossil record and estimating divergence times between lineages: Maximum divergene times and the importance of reliable phylogenies

Summary Bounded estimates on divergence times between lineaes are crucial to the calculation of absolute rates of molecular evolution. Upper (minimum) bounds on divergence times are easily estimated based on earliest fossil finds. Lower (maximum) bounds are more difficult to estimate; the age of putative ancestors may be used, though in practice it is virtually impossible to distinguish ancestors from primitive sister groups, which do not, of logical necessit, consitute lower bounds on divergence times. Two relatively new approaches to estimating lower bounds directly assess the incompleteness of the fossil record. The first uses taphonomic control groups to distinguish real absences from nonpreservation, while the second, and probably more powerful, uses the quality of the fossil recored to estimate confidence intervals on the bases of stratigraphic ranges. For some groups, especially vertebrates, the inclusion or exclusion of problematic fossils can dramaticaly affect estimated lower bounds on divergence times, often swamping the uncertainties due to the incompleteness of the fossil record and/or corelation and dating errors. When datable paleogeographic events reflect ancient divisions of faunas, a lower bound on the divergence time of speices within a fauna can be established based on the geologic, rather than fossil, record. The fossil records of hominids, eutherianmammals, echinoids, and geese are used as examples.This article was presented at the C.S.E.O.L. Conferrence on DNA-DNA Hybridization and Evolution, Lake Arrowhead, California, May 11–14, 1989     

An Improved SSA-Based Technique for EMG Removal from ECG

Background

The electrocardiogram (ECG) signals provide important information about the heart electrical activities in medical and diagnostic applications. This signal may be contaminated by different types of noises. One of the noise types which has a considerable overlap with the ECG signals in frequency domain is electromyogram (EMG). Among the exciting approaches for de-noising the ECG signals, those based on singular spectrum analysis (SSA) are popular.

Long-term impacts of genome-enabled selection

The objective was to evaluate the effects of directional selection based on estimated genomic breeding values (GEBVs) for a quantitative trait. Selection affects GEBV prediction accuracy as well as genetic architecture via changes in allelic frequencies and linkage disequilibrium (LD), and the resulting changes are different from those in the absence of selection. How marker density affects long-term GEBV accuracy and selection response needs to be understood as well. Simulations were used to characterize the impact of selection based on GEBVs over generations. Single-nucleotide polymorphism (SNP) marker effects were estimated with the Bayesian Lasso method in the base generation, and these estimates were used to calculate the GEBVs in subsequent generations. GEBV accuracy decreased over generations of selection, and it was lower than under random selection, where a decay took place as well. In the long term, selection response tended to reach a plateau, but, at higher marker density, both the magnitude and duration of the response were larger. Selection changed quantitative trait loci (QTL) allele frequencies and generated new but unfavorable LD for prediction. Family effects had a considerable contribution to GEBV accuracy in early generations of selection.