Why a simple model of genetic regulatory networks describes the distribution of avalanches in gene expression data

In a previous study it was shown that a simple random Boolean network model, with two input connections per node, can describe with a good approximation (with the exception of the smallest avalanches) the distribution of perturbations in gene expression levels induced by the knock-out of single genes in Saccharomyces cerevisiae. Here we address the reason why such a simple model actually works: we present a theoretical study of the distribution of avalanches and show that, in the case of a Poissonian distribution of outgoing links, their distribution is determined by the value of the Derrida exponent. This explains why the simulations based on the simple model have been effective, in spite of the unrealistic hypothesis about the number of input connections per node. Moreover, we consider here the problem of the choice of an optimal threshold for binarizing continuous data, and we show that tuning its value provides an even better agreement between model and data, valuable also in the important case of the smallest avalanches. Finally, we also discuss the choice of an optimal value of the Derrida parameter in order to match the experimental distributions: our results indicate a value slightly below the critical value 1.     

Inferring models of gene expression dynamics

We study the problem of identifying genetic networks in which expression dynamics are modeled by a differential equation that uses logical rules to specify time derivatives. We make three main contributions. First, we describe computationally efficient procedures for identifying the structure and dynamics of such networks from expression time series. Second, we derive predictions for the expected amount of data needed to identify randomly generated networks. Third, if expression values are available for only some of the genes, we show that the structure of the network for these "visible" genes can be identified and that the size and overall complexity of the network can be estimated. We validate these procedures and predictions using simulation experiments based on randomly generated networks with up to 30,000 genes and 17 distinct regulators per gene and on a network that models floral morphogenesis in Arabidopsis thaliana.     

猪链球菌2型ABC转运蛋白gene0910敲除突变体的构建及活性分析

目的:构建猪链球菌2型(Streptococcus suis type 2)强毒株05ZYH3389K毒力岛上的ABC转运蛋白gene0910敲除突变体,并初步分析其活性,为进一步研究猪链球菌假想毒力因子在致病中的作用提供实验基础。方法:以猪链球菌2型05ZYH33基因组为模板,扩增gene0910两侧各约500bp左右的片段为上下游同源臂,以pSET1质粒为模板,扩增氯霉素抗性基因Cm为中间片段,采用重叠PCR方法搭建三个片段,并克隆到自杀载体pSET4S上,构建基因敲除的载体。电转化05ZYH33感受态细胞,经30℃双交换和40℃质粒丢失,最后点板法筛选出基因敲除突变体△0910。对突变株和野生株的生物学活性及小鼠的致病性进行了初步比较。结果:PCR分析和测序结果均显示gene0910完全被氯霉素抗性基因Cm所替代,基因敲除突变体构建成功。结论:突变株的生物学活性和对小鼠的致病性与野生株相比差异不显著。     

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.     

Dynamic models of gene expression and classification

Powerful new methods, like expression profiles using cDNA arrays, have been used to monitor changes in gene expression levels as a result of a variety of metabolic, xenobiotic or pathogenic challenges. This potentially vast quantity of data enables, in principle, the dissection of the complex genetic networks that control the patterns and rhythms of gene expression in the cell. Here we present a general approach to developing dynamic models for analyzing time series of whole genome expression. In this approach, a self-consistent calculation is performed that involves both linear and non-linear response terms for interrelating gene expression levels. This calculation uses singular value decomposition (SVD) not as a statistical tool but as a means of inverting noisy and near-singular matrices. The linear transition matrix that is determined from this calculation can be used to calculate the underlying network reflected in the data. This suggests a direct method of classifying genes according to their place in the resulting network. In addition to providing a means to model such a large multivariate system this approach can be used to reduce the dimensionality of the problem in a rational and consistent way, and suppress the strong noise amplification effects often encountered with expression profile data. Non-linear and higher-order Markov behavior of the network are also determined in this self-consistent method. In data sets from yeast, we calculate the Markov matrix and the gene classes based on the linear-Markov network. These results compare favorably with previously used methods like cluster analysis. Our dynamic method appears to give a broad and general framework for data analysis and modeling of gene expression arrays. Electronic Publication     

Mutual interaction in network motifs robustly sharpens gene expression in developmental processes

The gene regulatory network of a developmental process contains many mutually repressive interactions between two genes. They are often regulated by or regulate an additional factor, which constitute prominent network motifs, called regulated and regulating mutual loops. Our database analysis on the gene regulatory network for Drosophila melanogaster indicates that those with mutual repression are working specifically for the segmentation process. To clarify their biological roles, we mathematically study the response of the regulated mutual loop with mutual repression to input stimuli. We show that the mutual repression increases the response sensitivity without affecting the threshold input level to activate the target gene expression, as long as the network output is unique for a given input level. This high sensitivity of the motif can contribute to sharpening the spatial domain pattern without changing its position, assuring a robust developmental process. We also study transient dynamics that shows shift of domain boundary, agreeing with experimental observations. Importance of mutual repression is addressed by comparing with other types of regulations.     

Stochastic models for inferring genetic regulation from microarray gene expression data

Microarray expression profiles are inherently noisy and many different sources of variation exist in microarray experiments. It is still a significant challenge to develop stochastic models to realize noise in microarray expression profiles, which has profound influence on the reverse engineering of genetic regulation. Using the target genes of the tumour suppressor gene p53 as the test problem, we developed stochastic differential equation models and established the relationship between the noise strength of stochastic models and parameters of an error model for describing the distribution of the microarray measurements. Numerical results indicate that the simulated variance from stochastic models with a stochastic degradation process can be represented by a monomial in terms of the hybridization intensity and the order of the monomial depends on the type of stochastic process. The developed stochastic models with multiple stochastic processes generated simulations whose variance is consistent with the prediction of the error model. This work also established a general method to develop stochastic models from experimental information.     

Cluster-based network model for time-course gene expression data

We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.     

Recovering context-specific gene network modules from expression data: A brief review

With the popularization of microarray experi-ments in biomedical laboratories, how to make context-specific knowledge discovery from expression data becomes a hot topic. While the static "reference networks"for key model organisms are nearly at hand, the endeavors to recover context-specific network modules are still at the beginning. Currently, this is achieved through filtering existing edges of the ensemble reference network or constructing gene networks ab initio. In this paper, we briefly review recent progress in the field and point out some research directions awaiting improved work, includ-ing expression-data-guided revision of reference networks.     

Intrinsic noise, gene regulation and steady-state statistics in a two-gene network

The intrinsic noise in a two-gene network model is analysed. The technique of the Fokker-Planck approximation is used to investigate the statistics of noise when the system state is near a stable equilibrium. This is called also the steady-state statistics. The relative size of noise is measured by the Fano factor that is defined as the ratio of the variance to the mean. Our main result shows that in general, the noise control in a two-gene network might be a very complicated process, but for the repressor-repressor system that is a very important case in investigating the genetic switch, the relative size of noise, i.e. the Fano factor, must be bigger than one for both the repressor proteins.     

Identification of dilated cardiomyopathy signature genes through gene expression and network data integration

Dilated cardiomyopathy (DCM) is a leading cause of heart failure (HF) and cardiac transplantations in Western countries. Single-source gene expression analysis studies have identified potential disease biomarkers and drug targets. However, because of the diversity of experimental settings and relative lack of data, concerns have been raised about the robustness and reproducibility of the predictions. This study presents the identification of robust and reproducible DCM signature genes based on the integration of several independent data sets and functional network information. Gene expression profiles from three public data sets containing DCM and non-DCM samples were integrated and analyzed, which allowed the implementation of clinical diagnostic models. Differentially expressed genes were evaluated in the context of a global protein–protein interaction network, constructed as part of this study. Potential associations with HF were identified by searching the scientific literature. From these analyses, classification models were built and their effectiveness in differentiating between DCM and non-DCM samples was estimated. The main outcome was a set of integrated, potentially novel DCM signature genes, which may be used as reliable disease biomarkers. An empirical demonstration of the power of the integrative classification models against single-source models is also given.     

The nature of 2-locus epistatic interactions in animals: evidence from Sewall Wright's guinea pig data

Summary The nature of epistatic interactions affects covariance between relatives and the expression of heterosis in various crossbred genotypes. The investigation of these interactions for metric traits requires large data sets of a suitable type. Data from Sewall Wright's early work with guinea pigs are used to compare the goodness-of-fit of seven biological models of 2-locus interaction for the six out of eleven traits in which epistatic effects are apparent. The model equivalent to additive x additive epistasis gives the best general fit over traits, with an average transformed R² value significantly greater than that of the next best fitting model (P<0.05). This result is compatible with results from the one other study in this area, using data from mice. It is concluded that, based on results available to date, the additive x additive 2-locus model of epistatic interaction appears most suitable for reduced genetic models.     

From phenotype to gene: Detecting disease-specific gene functional modules via a text-based human disease phenotype network construction

Currently, some efforts have been devoted to the text analysis of disease phenotype data, and their results indicated that similar disease phenotypes arise from functionally related genes. These related genes work together, as a functional module, to perform a desired cellular function. We constructed a text-based human disease phenotype network and detected 82 disease-specific gene functional modules, each corresponding to a different phenotype cluster, by means of graph-based clustering and mapping from disease phenotype to gene. Since genes in such gene functional modules are functionally related and cause clinically similar diseases, they may share common genetic origin of their associated disease phenotypes. We believe the investigation may facilitate the ultimate understanding of the common pathophysiologic basis of associated diseases.     

Association of Hck genetic polymorphisms with gene expression and COPD

Polymorphonuclear leukocytes (PMNs) are major effector cells in the chronic airway inflammation in chronic obstructive pulmonary disease (COPD). PMN degranulation is associated with degradation of extracellular matrix and tissue damage. Hck is an essential molecule in the signaling pathway regulating PMN degranulation. We hypothesized that polymorphisms affect the expression level of Hck, which, in turn, modulates PMN mediator release and tissue damage and influences the development of COPD. Here we systematically investigated genetic tag polymorphisms of the Hck gene, Hck mRNA and protein expression pattern in PMNs, and PMN mediator release (myeloperoxidase) in 60 healthy white subjects, and assessed their association with the use of several genetic models. The association of genetic polymorphisms with COPD-related phenotypes was determined in the lung healthy study cohort (LHS). We identified a novel 15 bp insertion/deletion polymorphism (8,656 L/S) in intron 1 of the Hck gene, which was associated with differential expression of Hck protein and PMN myeloperoxidase release. In the LHS cohort, there was significant interaction between the 8,656 L/S polymorphism and smoking on baseline lung function and 8,656 L/S was associated with bronchodilator response. These data suggest that the insertion/deletion polymorphism could be a functional polymorphism of the Hck gene, may contribute to COPD pathogenesis and modify COPD-related phenotypes.     

Genetic variation in a network of natural and reintroduced populations of Griffon vulture (Gyps fulvus) in Europe

It is generally considered that limiting the loss of genetic diversity in reintroduced populations is essential to optimize the chances of success of population restoration. Indeed, to counter founder effect in a reintroduced population we should maximize the genetic variability within the founding group but also take into account networks of natural populations in the choice of the reintroduction area. However, assessment of relevant reintroduction strategies requires long-term post-release genetic monitoring. In this study, we analyzed genetic data from a network of native and reintroduced Griffon vulture (Gyps fulvus) populations successfully restored in Southern Europe. Using microsatellite markers, we characterized the level of genetic diversity and degree of genetic structure within and among three native colonies, four captive founding groups and one long-term monitored reintroduced population. We also used Bayesian assignment analysis to examine recent genetic connections between the reintroduced population and the other populations. We aimed to assess the level of fragmentation among native populations, the effectiveness of random choice of founders to retain genetic variability of the species, the loss of genetic diversity in the reintroduced population and the effect of gene flow on this founder effect. Our results indicate that genetic diversity was similar in all populations but we detected signs of recent isolation for one native population. The reintroduced population showed a high immigration rate that limited loss of genetic diversity. Genetic investigations performed in native populations and post-released genetic monitoring have direct implications for founder choice and release design.     

Robustness and state-space structure of Boolean gene regulatory models

Robustness to perturbation is an important characteristic of genetic regulatory systems, but the relationship between robustness and model dynamics has not been clearly quantified. We propose a method for quantifying both robustness and dynamics in terms of state-space structures, for Boolean models of genetic regulatory systems. By investigating existing models of the Drosophila melanogaster segment polarity network and the Saccharomyces cerevisiae cell-cycle network, we show that the structure of attractor basins can yield insight into the underlying decision making required of the system, and also the way in which the system maximises its robustness. In particular, gene networks implementing decisions based on a few genes have simple state-space structures, and their attractors are robust by virtue of their simplicity. Gene networks with decisions that involve many interacting genes have correspondingly more complicated state-space structures, and robustness cannot be achieved through the structure of the attractor basins, but is achieved by larger attractor basins that dominate the state space. These different types of robustness are demonstrated by the two models: the D. melanogaster segment polarity network is robust due to simple attractor basins that implement decisions based on spatial signals; the S. cerevisiae cell-cycle network has a complicated state-space structure, and is robust only due to a giant attractor basin that dominates the state space.     

Genetic distance and gene diversity among ten endogamous groups in Chhattisgarh,Central India

A genetic survey has been conducted among the Rawats and Telis, two endogamous caste populations of Chhattisgarh, Central India. Using the gene frequency data for three genetic loci, genetic distance among ten population groups have been calculated. The gene differentiation among these population groups is only about 2 per cent.