A Penalized Likelihood Approach for Bivariate Conditional Normal Models for Dynamic Co‐expression Analysis

Summary Gene co‐expressions have been widely used in the analysis of microarray gene expression data. However, the co‐expression patterns between two genes can be mediated by cellular states, as reflected by expression of other genes, single nucleotide polymorphisms, and activity of protein kinases. In this article, we introduce a bivariate conditional normal model for identifying the variables that can mediate the co‐expression patterns between two genes. Based on this model, we introduce a likelihood ratio (LR) test and a penalized likelihood procedure for identifying the mediators that affect gene co‐expression patterns. We propose an efficient computational algorithm based on iterative reweighted least squares and cyclic coordinate descent and have shown that when the tuning parameter in the penalized likelihood is appropriately selected, such a procedure has the oracle property in selecting the variables. We present simulation results to compare with existing methods and show that the LR‐based approach can perform similarly or better than the existing method of liquid association and the penalized likelihood procedure can be quite effective in selecting the mediators. We apply the proposed method to yeast gene expression data in order to identify the kinases or single nucleotide polymorphisms that mediate the co‐expression patterns between genes.     

A Monte Carlo simulation of plasmid replication during the bacterial division cycle

Plasmids have cell cycle replication patterns that need to be considered in models of their replication dynamics. To compare current theories for control of plasmid replication with experimental data for timing of plasmid replication with the cell cycle, a Monte Carlo simulation of plasmid replication and partition was developed. High-copy plasmid replication was simulated by incorporating equations previously developed from the known molecular biology of ColE1-type plasmids into the cell-cycle simulation. Two types of molecular mechanisms for low-copy plasmid replication were tested: accumulation of an initiator protein in proportion to cell mass and binding of the plasmid origin to the cell membrane. The low-copy plasmids were partitioned actively, with a specific mechanism to mediate the transfer from mother to daughter cells, whereas the high-copy plasmids were partitioned passively with cell mass.The simulation results and experimental data demonstrate cell-cycle-specific replication for the low-copy F plasmid and cell-cycle-independent replication for the high-copy pBR322, ColBM, and R6K plasmids. The simulation results indicate that synchronous replication at multiple plasmid origins is critical for the cell-cycle-specific pattern observed in rapidly growing cells. Variability in the synchrony of initiation of multiple plasmid origins give rise to a cell-cycle-independent pattern and is offered as a plausible explanation for the controversy surrounding the replication pattern of the low-copy plasmids. A comparison of experimental data and simulation results for the low-copy F plasmid at several growth rates indicates that either initiation mechanism would be sufficient to explain the timing of replication with the cell cycle. The simulation results also demonstrate that, although cell-cycle-specific and cell-cycle independent replication patterns give rise to very different gene-expression patterns during short induction periods in age-selected populations, long-term expression of genes encoded on low-copy and high-copy plasmids in exponentially growing cells have nearly the same patterns. These results may be important for the future use of low-copy plasmids as expression vectors and validate the use of simpler models for high-copy plasmids that do not consider cell-cycle phenomena. (c) 1996 John Wiley & Sons, Inc.     

Multilevel functional clustering analysis

Summary In this article, we investigate clustering methods for multilevel functional data, which consist of repeated random functions observed for a large number of units (e.g., genes) at multiple subunits (e.g., bacteria types). To describe the within- and between variability induced by the hierarchical structure in the data, we take a multilevel functional principal component analysis (MFPCA) approach. We develop and compare a hard clustering method applied to the scores derived from the MFPCA and a soft clustering method using an MFPCA decomposition. In a simulation study, we assess the estimation accuracy of the clustering membership and the cluster patterns under a series of settings: small versus moderate number of time points; various noise levels; and varying number of subunits per unit. We demonstrate the applicability of the clustering analysis to a real data set consisting of expression profiles from genes activated by immunity system cells. Prevalent response patterns are identified by clustering the expression profiles using our multilevel clustering analysis.     

An autologistic model for the genetic analysis of familial binary data.

Regressive logistic models specify the probability distribution of familial binary traits by conditioning each individual's phenotype on those of preceding relatives; therefore, the expression of the joint probability of the familial data necessitates ordering the observations. In the present paper, we propose an autologistic model of this familial dependence structure, which does not require specification of a particular ordering of the phenotypic observations. Genetic effects are introduced into the model in order to perform segregation analysis that is aimed at detecting the role of a major locus in the expression of familial phenotypes. In this model, the conditional probabilities have a logistic form, and large patterns of dependence between relatives can be considered with a simple interpretation of the parameters measuring the relationship between two phenotypes. The model is compared with the regressive logistic approach in terms of odds ratios and by using a simulation study.     

Global gene expression analysis of bovine somatic cell nuclear transfer blastocysts and cotyledons

Low developmental competence of bovine somatic cell nuclear transfer (SCNT) embryos is a universal problem. Abnormal placentation has been commonly reported in SCNT pregnancies from a number of species. The present study employed Affymetrix bovine expression microarrays to examine global gene expression patterns of SCNT and in vivo produced (AI) blastocysts as well as cotyledons from day‐70 SCNT and AI pregnancies. SCNT and AI embryos and cotyledons were analyzed for differential expression. Also in an attempt to establish a link between abnormal gene expression patterns in early embryos and cotyledons, differentially expressed genes were compared between the two studies. Microarray analysis yielded a list of 28 genes differentially expressed between SCNT and AI blastocysts and 19 differentially expressed cotyledon genes. None of the differentially expressed genes were common to both groups, although major histocompatibility complex I (MHCI) was significant in the embryo data and approached significance in the cotyledon data. This is the first study to report global gene expression patterns in bovine AI and SCNT cotyledons. The embryonic gene expression data reported here adds to a growing body of data that indicates the common occurrence of aberrant gene expression in early SCNT embryos. Mol. Reprod. Dev. 76: 471–482, 2009. © 2008 Wiley‐Liss, Inc.     

eXPatGen: generating dynamic expression patterns for the systematic evaluation of analytical methods

MOTIVATION: Experimental gene expression data sets, such as those generated by microarray or gene chip experiments, typically have significant noise and complicated interconnectivities that make understanding even simple regulatory patterns difficult. Given these complications, characterizing the effectiveness of different analysis techniques to uncover network groups and structures remains a challenge. Generating simulated expression patterns with known biological features of expression complexity, diversity and interconnectivities provides a more controlled means of investigating the appropriateness of different analysis methods. A simulation-based approach can systematically evaluate different gene expression analysis techniques and provide a basis for improved methods in dynamic metabolic network reconstruction. RESULTS: We have developed an on-line simulator, called eXPatGen, to generate dynamic gene expression patterns typical of microarray experiments. eXPatGen provides a quantitative network structure to represent key biological features, including the induction, repression, and cascade regulation of messenger RNA (mRNA). The simulation is modular such that the expression model can be replaced with other representations, depending on the level of biological detail required by the user. Two example gene networks, of 25 and 100 genes respectively, were simulated. Two standard analysis techniques, clustering and PCA analysis, were performed on the resulting expression patterns in order to demonstrate how the simulator might be used to evaluate different analysis methods and provide experimental guidance for biological studies of gene expression. AVAILABILITY: http://www.che.udel.edu/eXPatGen/     

A comprehensive expression analysis of the Arabidopsis proline-rich extensin-like receptor kinase gene family using bioinformatic and experimental approaches

The Arabidopsis proline-rich extensin-like receptor kinase (PERK) family consists of 15 predicted receptor kinases. A comprehensive expression analysis was undertaken to identify overlapping and unique expression patterns within this family relative to their phylogeny. Three different approaches were used to study AtPERK gene family expression, and included analyses of the EST, MPSS and NASCArrays databases as well as experimental RNA blot analyses. Some of the AtPERK members were identified as tissue-specific genes while others were more broadly expressed. While in some cases there was a good association between these different expression patterns and the position of the AtPERK members in the kinase phylogeny, in other cases divergence of expression patterns was seen. The PERK expression data identified by the bioinformatics and experimental approaches were found generally to show similar trends and supported the use of data from large-scale expression studies for obtaining preliminary expression data. Thus, the bioinformatics survey for ESTs and microarrays is a powerful comprehensive approach for obtaining a genome-wide view of genes in a multigene family.     

Segmenting the fly embryo: logical analysis of the role of the segment polarity cross-regulatory module

Initially activated by the pair-rule genes, the expression patterns of the segment polarity genes engrailed and wingless become consolidated through inter-cellular interactions between juxtaposed cells. We delineate a logical model focusing on a dozen molecular components at the core of the regulatory network controlling this process. Our model leads to the following conclusions: (1) the pair-rule signals, which activate engrailed and wingless genes independently of each other, need to be operative until the inter-cellular circuit involving these two genes is functional. This implies that the pair-rule pattern is instrumental both in determining the activation of the genes engrailed and wingless in rows of adjacent cells, and in consolidating these expression patterns; (2) the consolidation of engrailed and wingless expression patterns requires the simultaneous activation of both autocrine and paracrine Wingless-pathways, and the Hedgehog pathway; (3) protein kinase A plays at least two roles through the phosphorylation of Cubitus interruptus, the effector molecule of the Hedgehog signalling pathway and (4) the roles of Sloppy-paired and Naked in the delineation of the engrailed and wingless expression domains are emphasized as being important for segmental boundary formation. Moreover, the application of an original computational method leads to the delineation of a subset of crucial regulatory circuits enabling the coexistence of specific expression states at the cellular level, as well as specific combination of cellular states inter-connected through Wingless and Hedgehog signalling. Finally, the simulation of altered expressions of segment polarity genes leads to results consistent with the published data.     

Principal-oscillation-pattern analysis of gene expression

Principal-oscillation-pattern (POP) analysis is a multivariate and systematic technique for identifying the dynamic characteristics of a system from time-series data. In this study, we demonstrate the first application of POP analysis to genome-wide time-series gene-expression data. We use POP analysis to infer oscillation patterns in gene expression. Typically, a genomic system matrix cannot be directly estimated because the number of genes is usually much larger than the number of time points in a genomic study. Thus, we first identify the POPs of the eigen-genomic system that consists of the first few significant eigengenes obtained by singular value decomposition. By using the linear relationship between eigengenes and genes, we then infer the POPs of the genes. Both simulation data and real-world data are used in this study to demonstrate the applicability of POP analysis to genomic data. We show that POP analysis not only compares favorably with experiments and existing computational methods, but that it also provides complementary information relative to other approaches.     

A Method for Numerical Simulation of Single Limb Ground Contact Events: Application to Heel-Toe Running

The objective of this work was to develop a method to simulate single-limb ground contact events, which may be applied to study musculoskeletal injuries associated with such movements. To achieve this objective, a three-dimensional musculoskeletal model was developed consisting of the equations of motion for the musculoskeletal system, and models for the muscle force generation and ground contact elements. An optimization framework and a weighted least-squares objective function were presented that generated muscle stimulation patterns that optimally reproduced subject-specific movement data. Experimental data were collected from a single subject to provide initial conditions for the simulation and tracking data for the optimization. As an example application, a simulation of the stance phase of running was generated. The results showed that the average difference between the simulation and subject's ground reaction force and joint angle data was less than two inter-trial standard deviations. Further, there was good agreement between the model's muscle excitation patterns and experimentally collected electromyography data. These results give confidence in the model to examine musculoskeletal loading during a variety of landing movements and to study the effects of various factors associated with injury. Limitations were examined and areas of improvement for the model were presented.     

A genome-wide study of DNA methylation patterns and gene expression levels in multiple human and chimpanzee tissues

The modification of DNA by methylation is an important epigenetic mechanism that affects the spatial and temporal regulation of gene expression. Methylation patterns have been described in many contexts within and across a range of species. However, the extent to which changes in methylation might underlie inter-species differences in gene regulation, in particular between humans and other primates, has not yet been studied. To this end, we studied DNA methylation patterns in livers, hearts, and kidneys from multiple humans and chimpanzees, using tissue samples for which genome-wide gene expression data were also available. Using the multi-species gene expression and methylation data for 7,723 genes, we were able to study the role of promoter DNA methylation in the evolution of gene regulation across tissues and species. We found that inter-tissue methylation patterns are often conserved between humans and chimpanzees. However, we also found a large number of gene expression differences between species that might be explained, at least in part, by corresponding differences in methylation levels. In particular, we estimate that, in the tissues we studied, inter-species differences in promoter methylation might underlie as much as 12%-18% of differences in gene expression levels between humans and chimpanzees.     

基于CA_Markov模型的伊犁河三角洲景观格局预测

在遥感和水文数据的支持下,结合伊犁河三角洲的区域特点,确定了伊犁河三角洲的景观分类系统.通过景观生态学的定量分析和CA_Markov模型模拟方法,系统分析了1975-2 007年伊犁河三角洲景观格局的时空演变特征,并对2020年景观格局的情景进行了模拟与预测.结果表明：在人为间接干扰和自然环境的双重影响下,1975-2007年伊犁河三角洲的景观格局变化具有一定的周期性和不稳定性;对CA_Markov模型的可利用性进行分析与检验,1990-2007年伊犁河三角洲景观变化符合CA_Markov模型应用的条件;2020年,研究区景观的斑块破碎化严重,连通性趋于弱化,与水密切相关的湖泊、沼泽和漫滩的时空变化存在很大的不确定性;分别基于1990-2000年和2000-2007年概率转移矩阵预测的2020年研究区景观格局存在显著的差异性,不同景观类型的差异程度和表现形式各异,原因在于这两个时段自然条件的非一致性和不稳定性以及各景观类型的转移方式和方向不同.