scholarly journals Structure Optimization for Large Gene Networks Based on Greedy Strategy

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Francisco Gómez-Vela ◽  
Domingo S. Rodriguez-Baena ◽  
José Luis Vázquez-Noguera
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Structure Optimization ◽  
Biological Indicators ◽  
Biological Information ◽  
Greedy Heuristic ◽  
Biological Processes ◽  
Information Ratio ◽  
Large Gene ◽  
Do So

In the last few years, gene networks have become one of most important tools to model biological processes. Among other utilities, these networks visually show biological relationships between genes. However, due to the large amount of the currently generated genetic data, their size has grown to the point of being unmanageable. To solve this problem, it is possible to use computational approaches, such as heuristics-based methods, to analyze and optimize gene network’s structure by pruning irrelevant relationships. In this paper we present a new method, called GeSOp, to optimize large gene network structures. The method is able to perform a considerably prune of the irrelevant relationships comprising the input network. To do so, the method is based on a greedy heuristic to obtain the most relevant subnetwork. The performance of our method was tested by means of two experiments on gene networks obtained from different organisms. The first experiment shows how GeSOp is able not only to carry out a significant reduction in the size of the network, but also to maintain the biological information ratio. In the second experiment, the ability to improve the biological indicators of the network is checked. Hence, the results presented show that GeSOp is a reliable method to optimize and improve the structure of large gene networks.

scholarly journals PAFway: pairwise associations between functional annotations in biological networks and pathways

2020 ◽  
Vol 36 (19) ◽  
pp. 4963-4964
Author(s):  
Mahiar Mahjoub ◽  
Daphne Ezer
Keyword(s):  
Arabidopsis Thaliana ◽  
Biological Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Specific Function ◽  
Biological Functions ◽  
Functional Annotations ◽  
Large Gene

Abstract Motivation Large gene networks can be dense and difficult to interpret in a biologically meaningful way. Results Here, we introduce PAFway, which estimates pairwise associations between functional annotations in biological networks and pathways. It answers the biological question: do genes that have a specific function tend to regulate genes that have a different specific function? The results can be visualized as a heatmap or a network of biological functions. We apply this package to reveal associations between functional annotations in an Arabidopsis thaliana gene network. Availability and implementation PAFway is submitted to CRAN. Currently available here: https://github.com/ezer/PAFway. Supplementary information Supplementary data are available at Bioinformatics online.

Bayesian Network Approach to Estimate Gene Networks

2011 ◽  
pp. 2281-2305
Author(s):  
Seiya Imoto
Keyword(s):  
Bayesian Networks ◽  
Observational Data ◽  
Microarray Data ◽  
Gene Networks ◽  
Gene Network ◽  
Directed Graphs ◽  
Biological Data ◽  
Biological Information ◽  
Continuous Variables ◽  
Genome Wide Data

In cells, genes interact with each other and this system can be viewed as directed graphs. A gene network is a graphical representation of transcriptional relations between genes and the problem of estimation of gene networks from genome-wide data, such as DNA microarray gene expression data, is one of the important issues in bioinformatics and systems biology. Here, we present a statistical method based on Bayesian networks to estimate gene networks from microarray data and other biological data. Because microarray data are measured as continuous variables and the relationship between genes are usually nonlinear, we combine Bayesian networks and nonparametric regression to handle continuous variables and nonlinear relations. Most parts of gene networks are still unknown, and we need to estimate them from observational data. This problem is equivalent to the structural learning of Bayesian networks, and we solve it from a Bayes approach. The main difficulty of gene network estimation is due to the number of genes involved in the network. Therefore, it leads to model overfitting to the observational data like microarray data. Hence, a combination of various kinds of biological data is a key technique to estimate accurate gene networks. We show a general framework to combine microarray data and other biological information to estimate gene networks.

Bayesian Network Approach to Estimate Gene Networks

2007 ◽  
pp. 269-299
Author(s):  
Seiya Imoto ◽  
Satoru Miyano
Keyword(s):  
Bayesian Networks ◽  
Observational Data ◽  
Microarray Data ◽  
Gene Networks ◽  
Gene Network ◽  
Directed Graphs ◽  
Biological Data ◽  
Biological Information ◽  
Continuous Variables ◽  
Genome Wide Data

In cells, genes interact with each other and this system can be viewed as directed graphs. A gene network is a graphical representation of transcriptional relations between genes and the problem of estimation of gene networks from genome-wide data, such as DNA micro-array gene expression data, is one of the important issues in bioinformatics and systems biology. Here, we present a statistical method based on Bayesian networks to estimate gene networks from microarray data and other biological data. Because microarray data are measured as continuous variables and the relationship between genes are usually nonlinear, we combine Bayesian networks and nonparametric regression to handle continuous variables and nonlinear relations. Most parts of gene networks are still unknown, and we need to estimate them from observational data. This problem is equivalent to the structural learning of Bayesian networks, and we solve it from a Bayes approach. The main difficulty of gene network estimation is due to the number of genes involved in the network. Therefore, it leads to model overfitting to the observational data like microarray data. Hence, a combination of various kinds of biological data is a key technique to estimate accurate gene networks. We show a general framework to combine microarray data and other biological information to estimate gene networks.

scholarly journals Genome-wide analysis and expression profile of the bZIP gene family in poplar

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Kai Zhao ◽  
Song Chen ◽  
Wenjing Yao ◽  
Zihan Cheng ◽  
Boru Zhou ◽  
...  
Keyword(s):  
Salt Stress ◽  
Systems Biology ◽  
Gene Family ◽  
Stress Responses ◽  
Metal Ion ◽  
Gene Networks ◽  
Expression Patterns ◽  
Gene Set Enrichment Analysis ◽  
Specific Gene ◽  
Biological Processes

Abstract Background The bZIP gene family, which is widely present in plants, participates in varied biological processes including growth and development and stress responses. How do the genes regulate such biological processes? Systems biology is powerful for mechanistic understanding of gene functions. However, such studies have not yet been reported in poplar. Results In this study, we identified 86 poplar bZIP transcription factors and described their conserved domains. According to the results of phylogenetic tree, we divided these members into 12 groups with specific gene structures and motif compositions. The corresponding genes that harbor a large number of segmental duplication events are unevenly distributed on the 17 poplar chromosomes. In addition, we further examined collinearity between these genes and the related genes from six other species. Evidence from transcriptomic data indicated that the bZIP genes in poplar displayed different expression patterns in roots, stems, and leaves. Furthermore, we identified 45 bZIP genes that respond to salt stress in the three tissues. We performed co-expression analysis on the representative genes, followed by gene set enrichment analysis. The results demonstrated that tissue differentially expressed genes, especially the co-expressing genes, are mainly involved in secondary metabolic and secondary metabolite biosynthetic processes. However, salt stress responsive genes and their co-expressing genes mainly participate in the regulation of metal ion transport, and methionine biosynthetic. Conclusions Using comparative genomics and systems biology approaches, we, for the first time, systematically explore the structures and functions of the bZIP gene family in poplar. It appears that the bZIP gene family plays significant roles in regulation of poplar development and growth and salt stress responses through differential gene networks or biological processes. These findings provide the foundation for genetic breeding by engineering target regulators and corresponding gene networks into poplar lines.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

scholarly journals On Robust State Estimation of Gene Networks

2010 ◽  
Vol 2 ◽  
pp. 117959721000200 ◽  
Author(s):  
Chia-Hua Chuang ◽  
Chun-Liang Lin
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Quantitative Analysis ◽  
State Estimation ◽  
Gene Networks ◽  
Gene Network ◽  
Biological Systems ◽  
Uncertain Process ◽  
Network Systems ◽  
Internal States

Gene networks in biological systems are not only nonlinear but also stochastic due to noise corruption. How to accurately estimate the internal states of the noisy gene networks is an attractive issue to researchers. However, the internal states of biological systems are mostly inaccessible by direct measurement. This paper intends to develop a robust extended Kalman filter for state and parameter estimation of a class of gene network systems with uncertain process noises. Quantitative analysis of the estimation performance is conducted and some representative examples are provided for demonstration.

A NEW METHOD OF DETERMINING THRESHOLD OF GENE NETWORK BASED ON PHENOTYPES

2011 ◽  
Vol 19 (04) ◽  
pp. 607-616
Author(s):  
YUANYUAN ZHANG ◽  
SHUDONG WANG ◽  
MEIXI YANG ◽  
DASHUN XU ◽  
DAZHI MENG
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Structural Parameters ◽  
Network Modeling ◽  
Genetic Diseases ◽  
Disease Stage ◽  
External Representation ◽  
Functional Relationships ◽  
Disease Phenotypes ◽  
Significant Difference

With the rapid growth of microarray data, it has become a hot topic to reveal complex behaviors and functions of life system by studying the relationships among genes. In the process of reverse network modeling, the relationships with less relevance are generally not considered by determining a threshold when the relationships among genes are mined. However, there are no effective methods to determine the threshold up to now. It is worthwhile to note that the phenotypes of genetic diseases are generally regarded as external representation of the functions of genes. Therefore, two types of phenotype networks are constructed from gene and disease views, respectively, and through comparing these two types of phenotype networks, the threshold of gene network corresponding to a certain disease can be determined when their similarity reaches to maximum. Because the gene network is determined based on the relationships among phenotypes and phenotypes are external representation of the functions of genes, it is considered that relationships in the gene network may show functional relationships among genes in biological system. In this work, the thresholds 0.47 and 0.48 of gene network are determined based on Parkinson disease phenotypes. Furthermore, the validity of these thresholds is verified by the specificity and susceptibility of phenotype networks. Also, through comparing the structural parameters of gene networks for normal and disease stage at different thresholds, significant difference between the two gene networks at threshold 0.47 or 0.48 is found. The significant difference of structural parameters further verifies the efficiency of this method.

Heterogeneous networks integration for disease–gene prioritization with node kernels

2020 ◽  
Vol 36 (9) ◽  
pp. 2649-2656 ◽  
Author(s):  
Van Dinh Tran ◽  
Alessandro Sperduti ◽  
Rolf Backofen ◽  
Fabrizio Costa
Keyword(s):  
Gene Networks ◽  
Disease Gene ◽  
Supplementary Information ◽  
Edge Density ◽  
Integrated Network ◽  
Edge Type ◽  
Large Gene ◽  
Disease Gene Prioritization ◽  
Gene Associations ◽  
Different Types

Abstract Motivation The identification of disease–gene associations is a task of fundamental importance in human health research. A typical approach consists in first encoding large gene/protein relational datasets as networks due to the natural and intuitive property of graphs for representing objects’ relationships and then utilizing graph-based techniques to prioritize genes for successive low-throughput validation assays. Since different types of interactions between genes yield distinct gene networks, there is the need to integrate different heterogeneous sources to improve the reliability of prioritization systems. Results We propose an approach based on three phases: first, we merge all sources in a single network, then we partition the integrated network according to edge density introducing a notion of edge type to distinguish the parts and finally, we employ a novel node kernel suitable for graphs with typed edges. We show how the node kernel can generate a large number of discriminative features that can be efficiently processed by linear regularized machine learning classifiers. We report state-of-the-art results on 12 disease–gene associations and on a time-stamped benchmark containing 42 newly discovered associations. Availability and implementation Source code: https://github.com/dinhinfotech/DiGI.git. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Plant behaviour in response to the environment: information processing in the solid state

2019 ◽  
Vol 374 (1774) ◽  
pp. 20180370 ◽  
Author(s):  
Salva Duran-Nebreda ◽  
George W. Bassel
Keyword(s):  
Information Processing ◽  
Computational Models ◽  
Biological Information ◽  
Biological Processes ◽  
Cognitive Architectures ◽  
Theoretical Frameworks ◽  
Processing And Storage ◽  
Biological Information Processing ◽  
And Storage ◽  
Plant Behaviour

Information processing and storage underpins many biological processes of vital importance to organism survival. Like animals, plants also acquire, store and process environmental information relevant to their fitness, and this is particularly evident in their decision-making. The control of plant organ growth and timing of their developmental transitions are carefully orchestrated by the collective action of many connected computing agents, the cells, in what could be addressed as distributed computation. Here, we discuss some examples of biological information processing in plants, with special interest in the connection to formal computational models drawn from theoretical frameworks. Research into biological processes with a computational perspective may yield new insights and provide a general framework for information processing across different substrates.This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

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