AN EIGENVECTOR METHOD FOR ESTIMATING PHYLOGENETIC INERTIA

We propose a new method to estimate and correct for phylogenetic inertia in comparative data analysis. The method, called phylogenetic eigenvector regression (PVR) starts by performing a principal coordinate analysis on a pairwise phylogenetic distance matrix between species. Traits under analysis are regressed on eigenvectors retained by a broken-stick model in such a way that estimated values express phylogenetic trends in data and residuals express independent evolution of each species. This partitioning is similar to that realized by the spatial autoregressive method, but the method proposed here overcomes the problem of low statistical performance that occurs with autoregressive method when phylogenetic correlation is low or when sample size is too small to detect it. Also, PVR is easier to perform with large samples because it is based on well-known techniques of multivariate and regression analyses. We evaluated the performance of PVR and compared it with the autoregressive method using real datasets and simulations. A detailed worked example using body size evolution of Carnivora mammals indicated that phylogenetic inertia in this trait is elevated and similarly estimated by both methods. In this example, Type I error at α = 0.05 of PVR was equal to 0.048, but an increase in the number of eigenvectors used in the regression increases the error. Also, similarity between PVR and the autoregressive method, defined by correlation between their residuals, decreased by overestimating the number of eigenvalues necessary to express the phylogenetic distance matrix. To evaluate the influence of cladogram topology on the distribution of eigenvalues extracted from the double-centered phylogenetic distance matrix, we analyzed 100 randomly generated cladograms (up to 100 species). Multiple linear regression of log transformed variables indicated that the number of eigenvalues extracted by the broken-stick model can be fully explained by cladogram topology. Therefore, the broken-stick model is an adequate criterion for determining the correct number of eigenvectors to be used by PVR. We also simulated distinct levels of phylogenetic inertia by producing a trend across 10, 25, and 50 species arranged in “comblike” cladograms and then adding random vectors with increased residual variances around this trend. In doing so, we provide an evaluation of the performance of both methods with data generated under different evolutionary models than tested previously. The results showed that both PVR and autoregressive method are efficient in detecting inertia in data when sample size is relatively high (more than 25 species) and when phylogenetic inertia is high. However, PVR is more efficient at smaller sample sizes and when level of phylogenetic inertia is low. These conclusions were also supported by the analysis of 10 real datasets regarding body size evolution in different animal clades. We concluded that PVR can be a useful alternative to an autoregressive method in comparative data analysis.     

A Sparse Representation-Based Algorithm for Pattern Localization in Brain Imaging Data Analysis

Considering the two-class classification problem in brain imaging data analysis, we propose a sparse representation-based multi-variate pattern analysis (MVPA) algorithm to localize brain activation patterns corresponding to different stimulus classes/brain states respectively. Feature selection can be modeled as a sparse representation (or sparse regression) problem. Such technique has been successfully applied to voxel selection in fMRI data analysis. However, single selection based on sparse representation or other methods is prone to obtain a subset of the most informative features rather than all. Herein, our proposed algorithm recursively eliminates informative features selected by a sparse regression method until the decoding accuracy based on the remaining features drops to a threshold close to chance level. In this way, the resultant feature set including all the identified features is expected to involve all the informative features for discrimination. According to the signs of the sparse regression weights, these selected features are separated into two sets corresponding to two stimulus classes/brain states. Next, in order to remove irrelevant/noisy features in the two selected feature sets, we perform a nonparametric permutation test at the individual subject level or the group level. In data analysis, we verified our algorithm with a toy data set and an intrinsic signal optical imaging data set. The results show that our algorithm has accurately localized two class-related patterns. As an application example, we used our algorithm on a functional magnetic resonance imaging (fMRI) data set. Two sets of informative voxels, corresponding to two semantic categories (i.e., “old people” and “young people”), respectively, are obtained in the human brain.     

The Neurospora circadian clock: simple or complex?

The fungus Neurospora crassa is being used by a number of research groups as a model organism to investigate circadian (daily) rhythmicity. In this review we concentrate on recent work relating to the complexity of the circadian system in this organism. We discuss: the advantages of Neurospora as a model system for clock studies; the frequency (frq), white collar-1 and white collar-2 genes and their roles in rhythmicity; the phenomenon of rhythmicity in null frq mutants and its implications for clock mechanisms; the study of output pathways using clock-controlled genes; other rhythms in fungi; mathematical modelling of the Neurospora circadian system; and the application of new technologies to the study of Neurospora rhythmicity. We conclude that there may be many gene products involved in the clock mechanism, there may be multiple interacting oscillators comprising the clock mechanism, there may be feedback from output pathways onto the oscillator(s) and from the oscillator(s) onto input pathways, and there may be several independent clocks coexisting in one organism. Thus even a relatively simple lower eukaryote can be used to address questions about a complex, networked circadian system.     

Time-course analysis of cyanobacterium transcriptome: detecting oscillatory genes

The microarray technique allows the simultaneous measurements of the expression levels of thousands of mRNAs. By mining these data one can identify the dynamics of the gene expression time series. The detection of genes that are periodically expressed is an important step that allows us to study the regulatory mechanisms associated with the circadian cycle. The problem of finding periodicity in biological time series poses many challenges. Such challenge occurs due to the fact that the observed time series usually exhibit non-idealities, such as noise, short length, outliers and unevenly sampled time points. Consequently, the method for finding periodicity should preferably be robust against such anomalies in the data. In this paper, we propose a general and robust procedure for identifying genes with a periodic signature at a given significance level. This identification method is based on autoregressive models and the information theory. By using simulated data we show that the suggested method is capable of identifying rhythmic profiles even in the presence of noise and when the number of data points is small. By recourse of our analysis, we uncover the circadian rhythmic patterns underlying the gene expression profiles from Cyanobacterium Synechocystis.     

Modeling the emergence of circadian rhythms in a clock neuron network

Circadian rhythms in pacemaker cells persist for weeks in constant darkness, while in other types of cells the molecular oscillations that underlie circadian rhythms damp rapidly under the same conditions. Although much progress has been made in understanding the biochemical and cellular basis of circadian rhythms, the mechanisms leading to damped or self-sustained oscillations remain largely unknown. There exist many mathematical models that reproduce the circadian rhythms in the case of a single cell of the Drosophila fly. However, not much is known about the mechanisms leading to coherent circadian oscillation in clock neuron networks. In this work we have implemented a model for a network of interacting clock neurons to describe the emergence (or damping) of circadian rhythms in Drosophila fly, in the absence of zeitgebers. Our model consists of an array of pacemakers that interact through the modulation of some parameters by a network feedback. The individual pacemakers are described by a well-known biochemical model for circadian oscillation, to which we have added degradation of PER protein by light and multiplicative noise. The network feedback is the PER protein level averaged over the whole network. In particular, we have investigated the effect of modulation of the parameters associated with (i) the control of net entrance of PER into the nucleus and (ii) the non-photic degradation of PER. Our results indicate that the modulation of PER entrance into the nucleus allows the synchronization of clock neurons, leading to coherent circadian oscillations under constant dark condition. On the other hand, the modulation of non-photic degradation cannot reset the phases of individual clocks subjected to intrinsic biochemical noise.     

Feature identification in circadian rhythms of mice strains using in vivo information

The objective of this work was to identify strain-specific characteristics from real-time measurements of circadian rhythms of two inbred mouse strains. In particular, heart rate, temperature, and activity data collected from A/J and C57BL/6J (B6) mice using telemetry are analyzed. The influence of activity on heart rate and temperature is minimized by correlation analysis followed by regression analysis. The correlation analysis is used to determine the length of the activity data filter that results in the best correlation between activity data and heart rate or temperature. After the activity data are filtered, they are used in regression analysis. The temperature and heart rate rhythms obtained as the intercepts of the regression analysis are interpreted as the zero-activity rhythms and consequently are good estimates of the circadian rhythms. The circadian temperature rhythms for the B6 mice follow a smoother cosine-like time waveform, whereas those for the A/J mice follow a more square-wave-like waveform. To quantify the difference between these two temperature rhythms, a feature based on Fourier analysis of the time-series data is used. Detrended fluctuation analysis is used to identify features in the heart rate rhythms. The results of this work show that the features for the circadian temperature and heart rate rhythms can be used as distinguishing characteristics of the A/J and B6 strains. This work provides the foundation for future studies directed at investigating the influence of chromosomal substitutions on the regulation of circadian rhythms in these two strains.     

A simple regression method for mapping quantitative trait loci in line crosses using flanking markers

The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.     

Estimating brain functional connectivity with sparse multivariate autoregression

There is much current interest in identifying the anatomical and functional circuits that are the basis of the brain's computations, with hope that functional neuroimaging techniques will allow the in vivo study of these neural processes through the statistical analysis of the time-series they produce. Ideally, the use of techniques such as multivariate autoregressive (MAR) modelling should allow the identification of effective connectivity by combining graphical modelling methods with the concept of Granger causality. Unfortunately, current time-series methods perform well only for the case that the length of the time-series Nt is much larger than p, the number of brain sites studied, which is exactly the reverse of the situation in neuroimaging for which relatively short time-series are measured over thousands of voxels. Methods are introduced for dealing with this situation by using sparse MAR models. These can be estimated in a two-stage process involving (i) penalized regression and (ii) pruning of unlikely connections by means of the local false discovery rate developed by Efron. Extensive simulations were performed with idealized cortical networks having small world topologies and stable dynamics. These show that the detection efficiency of connections of the proposed procedure is quite high. Application of the method to real data was illustrated by the identification of neural circuitry related to emotional processing as measured by BOLD.     

A statistical model of the human core-temperature circadian rhythm

We formulate a statistical model of the human core-temperature circadian rhythm in which the circadian signal is modeled as a van der Pol oscillator, the thermoregulatory response is represented as a first-order autoregressive process, and the evoked effect of activity is modeled with a function specific for each circadian protocol. The new model directly links differential equation-based simulation models and harmonic regression analysis methods and permits statistical analysis of both static and dynamical properties of the circadian pacemaker from experimental data. We estimate the model parameters by using numerically efficient maximum likelihood algorithms and analyze human core-temperature data from forced desynchrony, free-run, and constant-routine protocols. By representing explicitly the dynamical effects of ambient light input to the human circadian pacemaker, the new model can estimate with high precision the correct intrinsic period of this oscillator ( approximately 24 h) from both free-run and forced desynchrony studies. Although the van der Pol model approximates well the dynamical features of the circadian pacemaker, the optimal dynamical model of the human biological clock may have a harmonic structure different from that of the van der Pol oscillator.     

Comparisons of the variability of three markers of the human circadian pacemaker

A circadian pacemaker within the central nervous system regulates the approximately 24-h physiologic rhythms in sleep cycles, hormone secretion, and other physiologic functions. Because the pacemaker cannot be examined directly in humans, markers of pacemaker function must be used to study the pacemaker and its response to environmental stimuli. Core body temperature (CBT), plasma cortisol, and plasma melatonin are three marker variables frequently used to estimate the phase of the human pacemaker. Measurements of circadian phase using markers can contain variability due to the circadian pacemaker itself, the intrinsic variability of the marker relative to the pacemaker, the method of analysis of the marker, and the marker assay. For this report, we compared the mathematical variability of a number of methods of identifying circadian phase from CBT, plasma cortisol, and plasma melatonin data collected in a protocol in which pacemaker variability was minimized using low light levels and regular timing of both the light pattern and the rest/activity schedule. We hoped to assess the relative variabilities of the different physiological markers and the analysis methods. Methods were based on the crossing of an absolute threshold, on the crossing of a relative threshold, or on fitting a curve to all data points. All methods of calculating circadian phase from plasma melatonin data were less variable than those calculated using CBT or cortisol data. The standard deviation for the phase estimates using CBT data was 0.78 h, using cortisol data was 0.65 h, and for the eight analysis methods using melatonin data was 0.23 to 0.35 h. While the variability for these markers might be different for other subject populations and/or less stringent study conditions, assessment of the intrinsic variability of the different calculations of circadian phase can be applied to allow inference of the statistical significance of phase and phase shift calculations, as well as estimation of sample size or statistical power for the number of subjects within an experimental protocol.     

The Per2 negative feedback loop sets the period in the mammalian circadian clock mechanism

Processes that repeat in time, such as the cell cycle, the circadian rhythm, and seasonal variations, are prevalent in biology. Mathematical models can represent our knowledge of the underlying mechanisms, and numerical methods can then facilitate analysis, which forms the foundation for a more integrated understanding as well as for design and intervention. Here, the intracellular molecular network responsible for the mammalian circadian clock system was studied. A new formulation of detailed sensitivity analysis is introduced and applied to elucidate the influence of individual rate processes, represented through their parameters, on network functional characteristics. One of four negative feedback loops in the model, the Per2 loop, was uniquely identified as most responsible for setting the period of oscillation; none of the other feedback loops were found to play as substantial a role. The analysis further suggested that the activity of the kinases CK1δ and CK1 were well placed within the network such that they could be instrumental in implementing short-term adjustments to the period in the circadian clock system. The numerical results reported here are supported by previously published experimental data.     

The circadian clock of cyanobacteria

A circadian clock, with physiological characteristics similar to those of eukaryotes, functions in the photosynthetic prokaryote, cyanobacteria. The molecular mechanism of this clock has been efficiently dissected using a luciferase reporter gene that reports the status of the clock. A circadian clock gene cluster, kaiABC, has been cloned via rhythm mutants of cyanobacterium, Synechococcus, and many clock mutations mapped to the three kai genes. Although kai genes do not share any homology with clock genes so far identified in eukaryotes, analysis of their expression suggests that a negative feedback control of kaiC expression by KaiC generates the circadian oscillation and that KaiA functions as a positive factor to sustain this oscillation. BioEssays 22:10-15, 2000.     

Reciprocal interactions between circadian clocks and aging

Virtually, all biological processes in the body are modulated by an internal circadian clock which optimizes physiological and behavioral performance according to the changing demands of the external 24-h world. This circadian clock undergoes a number of age-related changes, at both the physiological and molecular levels. While these changes have been considered to be part of the normal aging process, there is increasing evidence that disruptions to the circadian system can substantially impact upon aging and these impacts will have clear health implications. Here we review the current data of how both the physiological and core molecular clocks change with age and how feedback from external cues may modulate the aging of the circadian system.     

Oscillation of human period 1 (hPER1) reporter gene activity in human neuroblastoma cells in vivo

The mammalian period (Per) genes, which are components of the circadian clock, are mainly regulated via an autoregulatory feedback loop. Here we provide evidence that human Per1 (hPER1) reporter gene activity shows circadian rhythmicity in a human neuroblastoma, but not in a astrocytoma or a hepatoma cell line. Medium change and various pharmacological stimuli differentially induce this behavior. This circadian oscillation was strongly dampened and could be followed over maximally three cycles. It was even possible to phase-shift the course of this oscillation by repeated application of stimuli.