Survey on Modelling Methods Applicable to Gene Regulatory Network (original) (raw)
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A Review of Modeling Techniques for Genetic Regulatory Networks
Understanding the genetic regulatory networks, the discovery of interactions between genes and understanding regulatory processes in a cell at the gene level are the major goals of system biology and computational biology. Modeling gene regulatory networks and describing the actions of the cells at the molecular level are used in medicine and molecular biology applications such as metabolic pathways and drug discovery. Modeling these networks is also one of the important issues in genomic signal processing. After the advent of microarray technology, it is possible to model these networks using time-series data. In this paper, we provide an extensive review of methods that have been used on time-series data and represent the features, advantages and disadvantages of each. Also, we classify these methods according to their nature. A parallel study of these methods can lead to the discovery of new synthetic methods or improve previous methods.
Computational Method for Reconstruction ofGene Regulatory Network Using MicroarrayData
International Journal of Innovative Research in Computer and Communication Engineering, 2014
The DNA microarray has been established as a tool for efficient collection of mRNA expression data for a large number of genes simultaneously.Mapping function approach maps pairs of genes that present similar positive and/or negative interactions and also specifies how the range of each gene is going to be segmented (labels). From all the label combinations a function transforms each pair of labels into another one, which identifies the type of interaction.
Biological research topics gradually shift from structural genomics into functional genomics. DNA microarrays have been used to generate abundant data for exploring functions and interactions among genes. We propose a reverse-engineering strategy to predict the interactions between genes within a genetic network. Our inputs are perturbation matrices experimentally obtained from DNA microarrays. First, we make some assumptions for the interactions in the network. The proposed network is represented as a directed graph. After that, we enumerate all possible network models according to the assumptions. And then, some candidate models are obtained, resulted from calculated perturbation matrices out of computational simulation. The network involves in not only the transcription level but also the nucleotide/protein interactions in general. To justify this method, we take a well-known genetic regulatory network in yeast Saccharomyces cerevisia for a test. The result shows that one of the ...
Lecture Notes in Computer Science, 2004
The interplay of interactions between DNA, RNA and proteins leads to genetic regulatory networks (GRN) and in turn controls the gene regulation. Directly or indirectly in a cell such molecules either interact in a positive or in repressive manner therefore it is hard to obtain the accurate computational models through which the final state of a cell can be predicted with certain accuracy. This paper describes biological behaviour of actual regulatory systems and we propose a novel method for GRN discovery of a large number of genes from multiple time series gene expression observations over small and irregular time intervals. The method integrates a genetic algorithm (GA) to select a small number of genes and a Kalman filter to derive the GRN of these genes. After GRNs of smaller number of genes are obtained, these GRNs may be integrated in order to create the GRN of a larger group of genes of interest. Nikola K. Kasabov1, Zeke S. H. Chan1, Vishal Jain1, Igor Sidorov2 and Dimiter S. Dimitrov2 the model likelihood as an optimization objective. The biological implications of the identified networks are complex and currently under investigation.
A survey of models for inference of gene regulatory networks
Nonlinear Analysis: Modelling and Control, 2013
In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks (GRNs). The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. A novel model for integration of prior biological knowledge in the GRNs inference is presented, too. The advantages and disadvantages of the described models are compared. The GRNs validation criteria are depicted. Current trends and further directions for GRNs inference using prior knowledge are given at the end of the paper.
BMC …, 2007
Background: Proteins are the primary regulatory agents of transcription even though mRNA expression data alone, from systems like DNA microarrays, are widely used. In addition, the regulation process in genetic systems is inherently non-linear in nature, and most studies employ a time-course analysis of mRNA expression. These considerations should be taken into account in the development of methods for the inference of regulatory interactions in genetic networks. Results: We use an S-system based model for the transcription and translation process. We propose an optimizationbased regulatory network inference approach that uses time-varying data from DNA microarray analysis. Currently, this seems to be the only model-based method that can be used for the analysis of time-course "relative" expressions (expression ratios). We perform an analysis of the dynamic behavior of the system when the number of experimental samples available is varied, when there are different levels of noise in the data and when there are genes that are not considered by the experimenter. Our studies show that the principal factor affecting the ability of a method to infer interactions correctly is the similarity in the time profiles of some or all the genes. The less similar the profiles are to each other the easier it is to infer the interactions. We propose a heuristic method for resolving networks and show that it displays reasonable performance on a synthetic network. Finally, we validate our approach using real experimental data for a chosen subset of genes involved in the sporulation cascade of Bacillus anthracis. We show that the method captures most of the important known interactions between the chosen genes. Conclusion: The performance of any inference method for regulatory interactions between genes depends on the noise in the data, the existence of unknown genes affecting the network genes, and the similarity in the time profiles of some or all genes. Though subject to these issues, the inference method proposed in this paper would be useful because of its ability to infer important interactions, the fact that it can be used with time-course DNA microarray data and because it is based on a non-linear model of the process that explicitly accounts for the regulatory role of proteins.
Microarray Data Analysis: Gene Regulatory Networks
Cerutti/Advanced, 2011
Cellular processes involve million of molecules playing a coherent role in the exchange of matter, energy, and information, both among themselves and with the environment. These processes are regulated by proteins whose expression is controlled by a tight network of interactions between genes, proteins, and other molecules. There is evidence that some pathologies of major social impact, such as cancer and diabetes, involve groups of genes and proteins that are functionally related pathways rather than the expression of a single gene or protein. Therefore, it is important to investigate the global modifications of a specific regulatory pathway rather than the expression of a single gene. This is a major goal of systems-biology approaches, devoted to the elucidation of the complex network of interacting DNA sequences, RNAs, and proteins regulating and controlling gene expression. Today, high-throughput technologies such as microarray and mass spectrometry measure the cellular molecular expression in a given instant, thus making possible, at least in principle, the reconstruction of the regulatory network from its observed output through reverse engineering approaches. Unfortunately, microarray technology cost restricts the number of samples (on the order of ΙΟ'-ΙΟ 2) available for each experiment with respect to the number of monitored genes (on the order of 10 4). Mass spectrometry techniques have some limitations as well, since at present they are not able to provide a precise quantification of protein expression and require one to identify the original protein from the spectrum of its frag
Most biological characteristics arise from complex interactions between numerous entities of a living cell, such us DNA, RNA, proteins and other small molecules. Therefore, understanding the structure and dynamics of the complex cellular interactions that contribute to the structure and function of living cells is a fundamental and challenging problem for experimental and computational biologists in the post-genomic era [1]- . These cellular entities and interactions form various biological networks that can be generally divided into the following three types:
A new multiple regression approach for the construction of genetic regulatory networks
Artificial Intelligence in Medicine, 2010
The construction of genetic regulatory networks from time series gene expression data is an important research topic in bioinformatics as large amounts of quantitative gene expression data can be routinely generated nowadays. One of the main difficulties in building such genetic networks is that the data set has huge number of genes but small number of time points. In this paper, we propose a novel linear regression model for uncovering the relations among the genes. Methods The model is based on the multiple regression. It takes into account of the fact that the real biological networks have the scale-free property. Based on this property and the statistical tests, a filter can be constructed to filter some redundant interactions among the genes. By minimizing the distance between the observed data and the predicted data, the model can be finally constructed. Results * Preliminary version has presented in the International Conference on BioMedical Engineering and Informatics, 2008. Numerical examples based on the yeast gene expression data are given to demonstrate our method. The proposed model can fit the data quite well. Some properties of the genes and the network are obtained. Among them, some are consistent with the experimental results. Conclusions In this paper, we proposed a new multiple regression approach to model the genegene interactions by taking into account the scale-free property. Numerical results show the effectiveness of our method. The comparison with some other models which didnot consider the scale-free property will be as one of our future research topics.
A Simple Approach for Representation of Gene Regulatory Networks (GRN)
International Journal of Advanced Computer Science and Applications, 2018
Gene expressions are controlled by a series of processes known as Gene Regulation, and their abstract mapping is represented by Gene Regulatory Network (GRN) which is a descriptive model of gene interactions. Reverse engineering GRNs can reveal the complexity of gene interactions whose comprehension can lead to several other details. RNA-seq data provides better measurement of gene expressions; however it is difficult to infer GRNs using it because of its discreteness. Multiple other methods have already been proposed to infer GRN using RNA-seq data, but these methodologies are difficult to grasp. In this paper, a simple model is presented to infer GRNs, using RNA-seq based coexpression map provided by GeneFriends database, and a graph-based database tool is used to create regulatory network. The obtained results show that it is convenient to use graph database tools to work with regulatory networks instead of developing a new model from scratch.