American journal of bioinformatics and computational biology最新文献_第2页

Identification of Granger Causality between Gene Sets 基因组间格兰杰因果关系的鉴定

American journal of bioinformatics and computational biology Pub Date : 2010-08-01 DOI: 10.1142/S0219720010004860

André Fujita, J. Sato, Kaname Kojima, L. Gomes, Masao Nagasaki, M. Sogayar, S. Miyano

{"title":"Identification of Granger Causality between Gene Sets","authors":"André Fujita, J. Sato, Kaname Kojima, L. Gomes, Masao Nagasaki, M. Sogayar, S. Miyano","doi":"10.1142/S0219720010004860","DOIUrl":"https://doi.org/10.1142/S0219720010004860","url":null,"abstract":"Wiener and Granger have introduced an intuitive concept of causality (Granger causality) between two variables which is based on the idea that an effect never occurs before its cause. Later, Geweke generalized this concept to a multivariate Granger causality, i.e. n variables Granger-cause another variable. Although Granger causality is not \"effective causality\" in the Aristothelic sense, this concept is useful to infer directionality and information flow in observational data. Granger causality is usually identified by using VAR (Vector Autoregressive) models due to their simplicity. In the last few years, several VAR-based models were presented in order to model gene regulatory networks. Here, we generalize the multivariate Granger causality concept in order to identify Granger causalities between sets of gene expressions, i.e. whether a set of n genes Granger-causes another set of m genes, aiming at identifying the flow of information between gene networks (or pathways). The concept of Granger causality for sets of variables is presented. Moreover, a method for its identification with a bootstrap test is proposed. This method is applied in simulated and also in actual biological gene expression data in order to model regulatory networks. This concept may be useful for the understanding of the complete information flow from one network or pathway to the other, mainly in regulatory networks. Linking this concept to graph theory, sink and source can be generalized to node sets. Moreover, hub and centrality for sets of genes can be defined based on total information flow. Another application is in annotation, when the functionality of a set of genes is unknown, but this set is Granger-caused by another set of genes which is well studied. Therefore, this information may be useful to infer or construct some hypothesis about the unknown set of genes.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"40 1","pages":"679-701"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84867955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

The Importance of inhibitors for the Simulation of metabolic Processes: in silico Zn2+ Inhibition of M-Aconitase from Analysis of glycolysis and Krebs Cycle Kinetic Models 抑制剂对代谢过程模拟的重要性:从糖酵解和克雷布斯循环动力学模型的分析看硅Zn2+对m -乌头酸酶的抑制

American journal of bioinformatics and computational biology Pub Date : 2010-08-01 DOI: 10.1142/S0219720010004872

M. Čuperlović-Culf

{"title":"The Importance of inhibitors for the Simulation of metabolic Processes: in silico Zn2+ Inhibition of M-Aconitase from Analysis of glycolysis and Krebs Cycle Kinetic Models","authors":"M. Čuperlović-Culf","doi":"10.1142/S0219720010004872","DOIUrl":"https://doi.org/10.1142/S0219720010004872","url":null,"abstract":"Metal ions have a major effect on the metabolic processes in cells either as inhibitors or as integral components of enzymes. The inhibition of enzymes can take place either through the inhibition of gene expression or through inhibition of protein function. A particularly interesting example of the effect of a metal ion on metabolism is the observed inhibition of Krebs cycle and alteration of energy metabolism by zinc (II) cations. In this particular case metal ion inhibition of enzyme is linked to one of the major functions of prostate cells of accumulation and excretion of citrate. Experimental results have shown that increase in concentration of zinc (II) in prostate cells effectively blocks progression of a part of the Krebs cycle leading to change in the concentration of several metabolites with largest effect in the accumulation of citrate. Based on previously reported experimental results, several distinct mechanisms for zinc (II) inhibition of Krebs cycle were proposed. In order to determine the precise mechanism of inhibition in this system, a mathematical model of glycolysis and Krebs cycle was constructed. Three different types of inhibition were analyzed, including competitive and uncompetitive inhibition as well as inhibition through the alteration of the expression level of m-aconitase. The effects of different inhibition models on metabolite concentrations were investigated as a time course simulation of the system of reactions. Several kinetic parameters in the model were optimized in order to best resemble experimental measurements. The simulation shows that only competitive inhibition leads to an agreement with experimental data.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"23 1","pages":"703-715"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81212709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Inference of Genetic Networks Using Lpms: Assessment of Confidence Values of Regulations 利用Lpms的遗传网络推断:规则置信度的评估

American journal of bioinformatics and computational biology Pub Date : 2010-08-01 DOI: 10.1142/S0219720010004859

Shuhei Kimura, Yuichi Shiraishi, Mariko Okada

{"title":"Inference of Genetic Networks Using Lpms: Assessment of Confidence Values of Regulations","authors":"Shuhei Kimura, Yuichi Shiraishi, Mariko Okada","doi":"10.1142/S0219720010004859","DOIUrl":"https://doi.org/10.1142/S0219720010004859","url":null,"abstract":"When we apply inference methods based on a set of differential equations into actual genetic network inference problems, we often end up with a large number of false-positive regulations. However, as we must check the inferred regulations through biochemical experiments, fewer false-positive regulations are preferable. In order to reduce the number of regulations checked, this study proposes a new method that assigns confidence values to all of the regulations contained in the target network. For this purpose, we combine a residual bootstrap method with the existing method, i.e. the inference method using linear programming machines (LPMs). Through numerical experiments on an artificial genetic network inference problem, we confirmed that most of the regulations with high confidence values are actually present in the target networks. We then used the proposed method to analyze the bacterial SOS DNA repair system, and succeeded in assigning reasonable confidence values to its regulations. Although this study combined the bootstrap method with the inference method using the LPMs, the proposed bootstrap approach could be combined with any method that has an ability to infer a genetic network from time-series of gene expression levels.","PeriodicalId":90783,"journal":{"name":"American journal of bioinformatics and computational biology","volume":"29 1","pages":"661-677"},"PeriodicalIF":0.0,"publicationDate":"2010-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87481594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

A Non-Parametric Bayesian Approach for Predicting RNA Secondary Structures 预测RNA二级结构的非参数贝叶斯方法

American journal of bioinformatics and computational biology Pub Date : 2009-09-12 DOI: 10.1007/978-3-642-04241-6_24

Kengo Sato, Michiaki Hamada, Toutai Mituyama, K. Asai, Y. Sakakibara

引用次数: 12

An Overview of the Algorithmic Session of MCCMB'05 mcmb '05算法部分概述

American journal of bioinformatics and computational biology Pub Date : 2006-04-01 DOI: 10.1142/S0219720006002107

G. Kucherov, A. Mironov, M. Roytberg

引用次数: 0

Erratum: "In Silico Phenotypic Screening Method of Mutants Based on Statistical Modeling of Genetically Mixed Samples" 勘误:“基于遗传混合样本统计建模的突变体的计算机表型筛选方法”

American journal of bioinformatics and computational biology Pub Date : 2006-01-01 DOI: 10.1142/S0219720006001783

E. Kaminuma, N. Heida, T. Yoshizumi, M. Nakazawa, M. Matsui, Tetsuro Toyoda

引用次数: 0

Modeling and Characterization of Multi-charge Mass Spectra for Peptide Sequencing 多肽测序的多电荷质谱建模与表征

American journal of bioinformatics and computational biology Pub Date : 2005-12-01 DOI: 10.1142/9781860947292_0014

K. F. Chong, K. Ning, H. Leong, P. Pevzner

引用次数: 6

Guest Editor's Introduction: Advanced Techniques for Bioinformatics 客座编辑介绍:生物信息学的先进技术

American journal of bioinformatics and computational biology Pub Date : 2005-02-01 DOI: 10.1142/S021972000500093X

Y. Chen

引用次数: 0

Erratum: Theoretically Modeling Microarray with the Chemical Equilibrium and Thermodynamics 勘误:用化学平衡和热力学理论建模微阵列

American journal of bioinformatics and computational biology Pub Date : 2005-01-01 DOI: 10.1142/S0219720005001284

Mei Xu

引用次数: 0