Disturbance analysis of nonlinear differential equation models of genetic SUM regulatory networks

Noise disturbances and time delays are frequently met in cellular genetic regulatory systems. This paper is concerned with the disturbance analysis of a class of genetic regulatory networks described by nonlinear differential equation models. The mechanisms of genetic regulatory networks to amplify (attenuate) external disturbance are explored, and a simple measure of the amplification (attenuation) level is developed from a nonlinear robust control point of view. It should be noted that the conditions used to measure the disturbance level are delay-independent or delay-dependent, and are expressed within the framework of linear matrix inequalities, which can be characterized as convex optimization, and computed by the interior-point algorithm easily. Finally, by the proposed method, a numerical example is provided to illustrate how to measure the attenuation of proteins in the presence of external disturbances.     

Modeling and simulation of genetic regulatory systems: a literature review.

In order to understand the functioning of organisms on the molecular level, we need to know which genes are expressed, when and where in the organism, and to which extent. The regulation of gene expression is achieved through genetic regulatory systems structured by networks of interactions between DNA, RNA, proteins, and small molecules. As most genetic regulatory networks of interest involve many components connected through interlocking positive and negative feedback loops, an intuitive understanding of their dynamics is hard to obtain. As a consequence, formal methods and computer tools for the modeling and simulation of genetic regulatory networks will be indispensable. This paper reviews formalisms that have been employed in mathematical biology and bioinformatics to describe genetic regulatory systems, in particular directed graphs, Bayesian networks, Boolean networks and their generalizations, ordinary and partial differential equations, qualitative differential equations, stochastic equations, and rule-based formalisms. In addition, the paper discusses how these formalisms have been used in the simulation of the behavior of actual regulatory systems.     

A Parallel Attractor Finding Algorithm Based on Boolean Satisfiability for Genetic Regulatory Networks

In biological systems, the dynamic analysis method has gained increasing attention in the past decade. The Boolean network is the most common model of a genetic regulatory network. The interactions of activation and inhibition in the genetic regulatory network are modeled as a set of functions of the Boolean network, while the state transitions in the Boolean network reflect the dynamic property of a genetic regulatory network. A difficult problem for state transition analysis is the finding of attractors. In this paper, we modeled the genetic regulatory network as a Boolean network and proposed a solving algorithm to tackle the attractor finding problem. In the proposed algorithm, we partitioned the Boolean network into several blocks consisting of the strongly connected components according to their gradients, and defined the connection between blocks as decision node. Based on the solutions calculated on the decision nodes and using a satisfiability solving algorithm, we identified the attractors in the state transition graph of each block. The proposed algorithm is benchmarked on a variety of genetic regulatory networks. Compared with existing algorithms, it achieved similar performance on small test cases, and outperformed it on larger and more complex ones, which happens to be the trend of the modern genetic regulatory network. Furthermore, while the existing satisfiability-based algorithms cannot be parallelized due to their inherent algorithm design, the proposed algorithm exhibits a good scalability on parallel computing architectures.     

Establishment of apoptotic regulatory network for genetic markers of colorectal cancer and optimal selection of traditional Chinese medicine target

The paper aimed to screen out genetic markers applicable to early diagnosis for colorectal cancer and establish apoptotic regulatory network model for colorectal cancer, and to analyze the current situation of traditional Chinese medicine (TCM) target, thereby providing theoretical evidence for early diagnosis and targeted therapy of colorectal cancer. Taking databases including CNKI, VIP, Wanfang data, Pub Med, and MEDLINE as main sources of literature retrieval, literatures associated with genetic markers that are applied to early diagnosis of colorectal cancer were searched and performed comprehensive and quantitative analysis by Meta analysis, hence screening genetic markers used in early diagnosis of colorectal cancer. KEGG analysis was employed to establish apoptotic regulatory network model based on screened genetic markers, and optimization was conducted on TCM targets. Through Meta analysis, seven genetic markers were screened out, including WWOX, K-ras, COX-2, P53, APC, DCC and PTEN, among which DCC has the highest diagnostic efficiency. Apoptotic regulatory network was built by KEGG analysis. Currently, it was reported that TCM has regulatory function on gene locus in apoptotic regulatory network. The apoptotic regulatory model of colorectal cancer established in this study provides theoretical evidence for early diagnosis and TCM targeted therapy of colorectal cancer in clinic.     

Identifying interventional and pathogenic mechanisms by generative inverse modeling of gene expression profiles.

MOTIVATION: The characterization of genetic mechanisms underlying normal cellular function, cancer development, pathogenesis, and the effect of drug treatment is one of the most challenging topics for cancer research and molecular biology. Existing methods for inferring genetic regulatory networks from genome-wide expression profiles provide important information about gene interactions and regulatory relationships. However, these methods do not provide information about the impact of possible interventions or changes on such regulatory networks to study cause-effect relationships at a systems-biology level. RESULTS: We present a data-driven method called generative inverse modeling, which simulates the effect of local genetic changes on the global cellular state, as reflected by an altered genome-wide expression profile. For each genetic change we define a pathogenic score by calculating to what extent it transforms the simulated expression patterns into patterns measured for pathologically altered tissues. The method can be used to estimate the relevance of genes for disease-specific genetic mechanisms, e.g., as presented here for pathogenesis. Generative inverse modeling is based on a Bayesian probability density estimation from a set of measured gene-expression patterns.     

Regulating the genetic revolution.

The rapid pace of scientific breakthroughs that are flowing from genetic research seem to be matched only by the intensifying perception that some form of social control is required. As exemplified by the call to 'regulatory action' that was elicited by the prospect of human cloning, increased formal government oversight of human genetic research appears to be inevitable. Although there are certainly many valid social and ethical concerns worthy of a regulatory response, legislating genetic policy will not be easy.     

Statistical epistasis is a generic feature of gene regulatory networks

Functional dependencies between genes are a defining characteristic of gene networks underlying quantitative traits. However, recent studies show that the proportion of the genetic variation that can be attributed to statistical epistasis varies from almost zero to very high. It is thus of fundamental as well as instrumental importance to better understand whether different functional dependency patterns among polymorphic genes give rise to distinct statistical interaction patterns or not. Here we address this issue by combining a quantitative genetic model approach with genotype-phenotype models capable of translating allelic variation and regulatory principles into phenotypic variation at the level of gene expression. We show that gene regulatory networks with and without feedback motifs can exhibit a wide range of possible statistical genetic architectures with regard to both type of effect explaining phenotypic variance and number of apparent loci underlying the observed phenotypic effect. Although all motifs are capable of harboring significant interactions, positive feedback gives rise to higher amounts and more types of statistical epistasis. The results also suggest that the inclusion of statistical interaction terms in genetic models will increase the chance to detect additional QTL as well as functional dependencies between genetic loci over a broad range of regulatory regimes. This article illustrates how statistical genetic methods can fruitfully be combined with nonlinear systems dynamics to elucidate biological issues beyond reach of each methodology in isolation.     

The molecular population genetics of regulatory genes

Regulatory loci, which may encode both trans acting proteins as well as cis acting promoter regions, are crucial components of an organism's genetic architecture. Although evolution of these regulatory loci is believed to underlie the evolution of numerous adaptive traits, there is little information on natural variation of these genes. Recent molecular population genetic studies, however, have provided insights into the extent of natural variation at regulatory genes, the evolutionary forces that shape them and the phenotypic effects of molecular regulatory variants. These recent analyses suggest that it may be possible to study the molecular evolutionary ecology of regulatory diversification by examining both the extent and patterning of regulatory gene diversity, the phenotypic effects of molecular variation at these loci and their ecological consequences.     

A simulation study of the genetic regulatory hierarchy for butterfly eyespot focus determination

The color patterns on the wings of butterflies have been an important model system in evolutionary developmental biology. Two types of models have been used to study these patterns. The first type of model employs computational techniques and generalized mechanisms of pattern formation to make predictions about how color patterns will vary as parameters of the model are changed. These generalized mechanisms include diffusion gradient, reaction-diffusion, lateral inhibition, and threshold responses. The second type of model uses known genetic interactions from Drosophila melanogaster and patterns of candidate gene expression in one of several butterfly species (most often Junonia (Precis) coenia or Bicyclus anynana) to propose specific genetic regulatory hierarchies that appear to be involved in color pattern formation. This study combines these two approaches using computational techniques to test proposed genetic regulatory hierarchies for the determination of butterfly eyespot foci (also known as border ocelli foci). Two computer programs, STELLA 8.1 and Delphi 2.0, were used to simulate the determination of eyespot foci. Both programs revealed weaknesses in a genetic model previously proposed for eyespot focus determination. On the basis of these simulations, we propose two revised models for eyespot focus determination and identify components of the genetic regulatory hierarchy that are particularly sensitive to changes in model parameter values. These components may play a key role in the evolution of butterfly eyespots. Simulations like these may be useful tools for the study of other evolutionary developmental model systems and reveal similar sensitive components of the relevant genetic regulatory hierarchies.     

促进植物高效遗传转化的发育调节因子及其在玉米中的应用进展*

高效遗传转化技术体系的建立对植物功能基因组学研究和作物新品种的培育均具有促进作用,目前,再生效率低下是限制许多植物高效遗传转化体系建立的主要技术屏障之一。随着对植物分生组织和体细胞胚形成过程研究的深入,鉴定到了一些关键调控基因,统称为发育调节因子。发育调节因子应用于植物遗传转化后,可以有效改善植物分生组织诱导和再生能力,为提高遗传转化效率提供了重要机遇。综述了7类发育调节因子在提高植物遗传转化效率中的研究进展,重点介绍了其中3类在促进玉米遗传转化中的应用,最后展望了建立植物高效遗传转化体系的发展方向。     

A pivotal role for p53: balancing aerobic respiration and glycolysis

The genetic basis of increased glycolytic activity observed in cancer cells is likely to be the result of complex interactions of multiple regulatory pathways. Here we review the recent evidence of a simple genetic mechanism by which tumor suppressor p53 regulates mitochondrial respiration with secondary changes in glycolysis that are reminiscent of the Warburg effect. The biological significance of this regulation of the two major pathways of energy generation by p53 remains to be seen.     

降解芳烃微生物的多样性

芳烃是一类生物异源物质,自然微生物群落利用其对环境的适应性,对这类物质由陌生到适应,微生物群落的遗传背景发生了变化,降解芳烃的微生物呈现出多样性,本文系统介绍了降解芳烃微生物的特性,物种资源,环境适应,遗传背景及演变;介绍了各遗传型物种的功能基因数量,表达及调控方式,指明芳烃环境污染的生物修复主要取决于高效工程构造及代谢过程的控制。     

Boolean dynamics of genetic regulatory networks inferred from microarray time series data

MOTIVATION: Methods available for the inference of genetic regulatory networks strive to produce a single network, usually by optimizing some quantity to fit the experimental observations. In this article we investigate the possibility that multiple networks can be inferred, all resulting in similar dynamics. This idea is motivated by theoretical work which suggests that biological networks are robust and adaptable to change, and that the overall behavior of a genetic regulatory network might be captured in terms of dynamical basins of attraction. RESULTS: We have developed and implemented a method for inferring genetic regulatory networks for time series microarray data. Our method first clusters and discretizes the gene expression data using k-means and support vector regression. We then enumerate Boolean activation-inhibition networks to match the discretized data. Finally, the dynamics of the Boolean networks are examined. We have tested our method on two immunology microarray datasets: an IL-2-stimulated T cell response dataset and a LPS-stimulated macrophage response dataset. In both cases, we discovered that many networks matched the data, and that most of these networks had similar dynamics. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.