Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives
information about at which different environmental conditions genes of particular interest get over
expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between
genes. Interaction can be positive or negative. For inference of GRN, time series data provided by
Microarray technology is used. Key factors to be considered while constructing GRN are scalability,
robustness, reliability and maximum detection of true positive interactions between genes. This paper
gives detailed technical review of existing methods applied for building of GRN along with scope for
future work.
MicroRNA-Disease Predictions Based On Genomic Dataijtsrd
Gene Ontology is a structured library of concepts related with one or more gene products through a process called annotation. Association Rules that discovers biologically relevant and corresponding associations. In the existing system, they used Gene Ontology-based Weighted Association Rules for extracting annotated datasets. We here adapt the MOAL algorithm to mine cross-ontology association rules. Cross ontology rules to manipulate the Protein values from three sub ontologys for identifying the gene attacked disease. It focused on intrinsic and extrinsic values. The Co-Regulatory modules between microRNA, Transcription Factor and gene on function level with multiple genomic data. The regulations are compared with the help of integration technique. Iterative Multiplicative Updating Algorithm is used in our project to solve the optimization module function for the above interactions. Comparing the regulatory modules and protein value for gene and generating Bayesian rose tree for the efficiency of our result. Ajitha. C | DivyaLakshmi. K | Jothi Jayashree. M"MicroRNA-Disease Predictions Based On Genomic Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11386.pdf http://www.ijtsrd.com/computer-science/data-miining/11386/microrna-disease-predictions-based-on-genomic-data/ajitha-c
Analytical Study of Hexapod miRNAs using Phylogenetic Methodscscpconf
MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene expression.
Identification of total number of miRNAs even in completely sequenced organisms is still an
open problem. However, researchers have been using techniques that can predict limited
number of miRNA in an organism. In this paper, we have used homology based approach for
comparative analysis of miRNA of hexapoda group .We have used Apis mellifera, Bombyx
mori, Anopholes gambiae and Drosophila melanogaster miRNA datasets from miRBase
repository. We have done pair wise as well as multiple alignments for the available miRNAs in
the repository to identify and analyse conserved regions among related species. Unfortunately,
to the best of our knowledge, miRNA related literature does not provide in depth analysis of
hexapods. We have made an attempt to derive the commonality among the miRNAs and to
identify the conserved regions which are still not available in miRNA repositories. The results
are good approximation with a small number of mismatches. However, they are encouraging and may facilitate miRNA biogenesis for hexapods.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
Abstract Motivation: Gene regulatory network is the network based approach to represent the interactions between genes. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Gene microarray experiment provides the gene expression data for a particular condition and varying time periods. The expression of a particular gene depends upon the biological conditions and other genes. In this paper, we propose a new method for the analysis of microarray data. The proposed method makes use of S-system, which is a well-accepted model for the gene regulatory network reconstruction. Since the problem has multiple solutions, we have to identify an optimized solution. Evolutionary algorithms have been used to solve such problems. Though there are a number of attempts already been carried out by various researchers, the solutions are still not that satisfactory with respect to the time taken and the degree of accuracy achieved. Therefore, there is a need of huge amount further work in this topic for achieving solutions with improved performances. Results: In this work, we have proposed Clonal selection algorithm for identifying optimal gene regulatory network. The approach is tested on the real life data: SOS Ecoli DNA repairing gene expression data. It is observed that the proposed algorithm converges much faster and provides better results than the existing algorithms. Index Terms: Microarray analysis, Evolutionary Algorithm, Artificial Immune System, S-system, Gene Regulatory Network, SOS Ecoli DNA repairing, Clonal Selection Algorithm.
MicroRNA-Disease Predictions Based On Genomic Dataijtsrd
Gene Ontology is a structured library of concepts related with one or more gene products through a process called annotation. Association Rules that discovers biologically relevant and corresponding associations. In the existing system, they used Gene Ontology-based Weighted Association Rules for extracting annotated datasets. We here adapt the MOAL algorithm to mine cross-ontology association rules. Cross ontology rules to manipulate the Protein values from three sub ontologys for identifying the gene attacked disease. It focused on intrinsic and extrinsic values. The Co-Regulatory modules between microRNA, Transcription Factor and gene on function level with multiple genomic data. The regulations are compared with the help of integration technique. Iterative Multiplicative Updating Algorithm is used in our project to solve the optimization module function for the above interactions. Comparing the regulatory modules and protein value for gene and generating Bayesian rose tree for the efficiency of our result. Ajitha. C | DivyaLakshmi. K | Jothi Jayashree. M"MicroRNA-Disease Predictions Based On Genomic Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-3 , April 2018, URL: http://www.ijtsrd.com/papers/ijtsrd11386.pdf http://www.ijtsrd.com/computer-science/data-miining/11386/microrna-disease-predictions-based-on-genomic-data/ajitha-c
Analytical Study of Hexapod miRNAs using Phylogenetic Methodscscpconf
MicroRNAs (miRNAs) are a class of non-coding RNAs that regulate gene expression.
Identification of total number of miRNAs even in completely sequenced organisms is still an
open problem. However, researchers have been using techniques that can predict limited
number of miRNA in an organism. In this paper, we have used homology based approach for
comparative analysis of miRNA of hexapoda group .We have used Apis mellifera, Bombyx
mori, Anopholes gambiae and Drosophila melanogaster miRNA datasets from miRBase
repository. We have done pair wise as well as multiple alignments for the available miRNAs in
the repository to identify and analyse conserved regions among related species. Unfortunately,
to the best of our knowledge, miRNA related literature does not provide in depth analysis of
hexapods. We have made an attempt to derive the commonality among the miRNAs and to
identify the conserved regions which are still not available in miRNA repositories. The results
are good approximation with a small number of mismatches. However, they are encouraging and may facilitate miRNA biogenesis for hexapods.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
A clonal based algorithm for the reconstruction of genetic network using s sy...eSAT Journals
Abstract Motivation: Gene regulatory network is the network based approach to represent the interactions between genes. DNA microarray is the most widely used technology for extracting the relationships between thousands of genes simultaneously. Gene microarray experiment provides the gene expression data for a particular condition and varying time periods. The expression of a particular gene depends upon the biological conditions and other genes. In this paper, we propose a new method for the analysis of microarray data. The proposed method makes use of S-system, which is a well-accepted model for the gene regulatory network reconstruction. Since the problem has multiple solutions, we have to identify an optimized solution. Evolutionary algorithms have been used to solve such problems. Though there are a number of attempts already been carried out by various researchers, the solutions are still not that satisfactory with respect to the time taken and the degree of accuracy achieved. Therefore, there is a need of huge amount further work in this topic for achieving solutions with improved performances. Results: In this work, we have proposed Clonal selection algorithm for identifying optimal gene regulatory network. The approach is tested on the real life data: SOS Ecoli DNA repairing gene expression data. It is observed that the proposed algorithm converges much faster and provides better results than the existing algorithms. Index Terms: Microarray analysis, Evolutionary Algorithm, Artificial Immune System, S-system, Gene Regulatory Network, SOS Ecoli DNA repairing, Clonal Selection Algorithm.
This proposed method focus on these issues by developing a novel classification algorithm by combining Gene Expression Graph (GEG) with Manhattan distance. This method will be used to express the gene expression data. Gene Expression Graph provides the optimal view about the relationship between normal and unhealthy genes. The method of using a graph-based gene expression to express gene information was first offered by the authors in [1] and [2], It will permits to construct a classifier based on an association between graphs represented for well-known classes and graphs represented for samples to evaluate. Additionally Euclidean distance is used to measure the strength of relationship which exists between the genes.
Genetic Algorithm based Optimization of Machining ParametersAngshuman Pal
Optimization of a process output with reference to multiple input parameters is an important aspect of any manufacturing or machining process. This project examines and formulates a mechanism to optimize a given process output using the optimization technique of Genetic Algorithm. A new code for this purpose is formulated in MATLAB environment. The code is tested against some standardized functions and some case studies are performed to validate its performance against existing literature. The algorithm is then run over some real process performance data obtained from milling operation, and the optimum input parameters under given constraints required for achieving minimum surface roughness is proposed.
siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.a
Genome structure prediction a review over soft computing techniqueseSAT Journals
Abstract There are some techniques like spectrometry or crystallography for the determination of DNA, RNA or protein structures. These processes provide very accurate results for the structure estimation. But these conventional techniques are very slow and could be applied over a few special cases only. Soft computing techniques guarantee a near appropriate results in much smaller time and have very large applicability. These techniques are much easier to apply. Different approaches have been used in soft computing including nature inspired computing for estimation of genome structures with a considerable accuracy of results. This paper provides a review over different soft computing techniques been applied along with application method for the determination of genome structure. Keywords—DNA, RNA, proteins, structure, soft computing, techniques.
As increasing numbers of people choose to have their genomes sequenced and made available for research, more genomic data is available for analysis by machine learning approaches. Single Nucleotide Polymorphisms (SNPs) are known to be a major factor influencing many physical traits, diseases and other phenotypes. Using publicly available data and tools we predict phenotype from genotype using SNP data (1 to 2 million SNPs). We utilize data analysis and machine learning approaches only, no domain knowledge, so that our automated approach may be generally used to predict different phenotypes from genotype. In the first application of our method we predicted eye color with 87% accuracy.
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
This proposed method focus on these issues by developing a novel classification algorithm by combining Gene Expression Graph (GEG) with Manhattan distance. This method will be used to express the gene expression data. Gene Expression Graph provides the optimal view about the relationship between normal and unhealthy genes. The method of using a graph-based gene expression to express gene information was first offered by the authors in [1] and [2], It will permits to construct a classifier based on an association between graphs represented for well-known classes and graphs represented for samples to evaluate. Additionally Euclidean distance is used to measure the strength of relationship which exists between the genes.
Genetic Algorithm based Optimization of Machining ParametersAngshuman Pal
Optimization of a process output with reference to multiple input parameters is an important aspect of any manufacturing or machining process. This project examines and formulates a mechanism to optimize a given process output using the optimization technique of Genetic Algorithm. A new code for this purpose is formulated in MATLAB environment. The code is tested against some standardized functions and some case studies are performed to validate its performance against existing literature. The algorithm is then run over some real process performance data obtained from milling operation, and the optimum input parameters under given constraints required for achieving minimum surface roughness is proposed.
siRNA has become an indispensible tool for silencing gene expression. It can act as an antiviral agent in RNAi pathway against plant diseases caused by plant viruses. However, identification of appropriate features for effective siRNA design has become a pressing issue for researchers which need to be resolved. Feature selection is a vital pre-processing technique involved in bioinformatics data set to find the most discriminative information not only for dimensionality reduction and detection of relevance features but also for minimizing the cost associated with features to design an accurate learning system. In this paper, we propose an ANN based feature selection approach using hybrid GA-PSO for selecting feature subset by discarding the irrelevant features and evaluating the cost of the model training. The results showed that the performance of proposed hybrid GA-PSO model outperformed the results of general PSO.a
Genome structure prediction a review over soft computing techniqueseSAT Journals
Abstract There are some techniques like spectrometry or crystallography for the determination of DNA, RNA or protein structures. These processes provide very accurate results for the structure estimation. But these conventional techniques are very slow and could be applied over a few special cases only. Soft computing techniques guarantee a near appropriate results in much smaller time and have very large applicability. These techniques are much easier to apply. Different approaches have been used in soft computing including nature inspired computing for estimation of genome structures with a considerable accuracy of results. This paper provides a review over different soft computing techniques been applied along with application method for the determination of genome structure. Keywords—DNA, RNA, proteins, structure, soft computing, techniques.
As increasing numbers of people choose to have their genomes sequenced and made available for research, more genomic data is available for analysis by machine learning approaches. Single Nucleotide Polymorphisms (SNPs) are known to be a major factor influencing many physical traits, diseases and other phenotypes. Using publicly available data and tools we predict phenotype from genotype using SNP data (1 to 2 million SNPs). We utilize data analysis and machine learning approaches only, no domain knowledge, so that our automated approach may be generally used to predict different phenotypes from genotype. In the first application of our method we predicted eye color with 87% accuracy.
Sample Work For Engineering Literature Review and Gap IdentificationPhD Assistance
Sample Work For Engineering Literature Review and Gap Identification - PhD Assistance - http://bit.ly/2E9fAVq
2.1 INTRODUCTION
2.2 RESEARCH GAPS IN EXISTING METHODS
2.3 OBJECTIVES OF THIS WORK
Read More : http://bit.ly/2Rl7XT5
#gapanalysis #strategicmanagement #datagapanalysis #gapanalysisppt #gapanalysishealthcare #gapanalysisfinance #gapanalysisEngineering
Clustering Approaches for Evaluation and Analysis on Formal Gene Expression C...rahulmonikasharma
Enormous generation of biological data and the need of analysis of that data led to the generation of the field Bioinformatics. Data mining is the stream which is used to derive, analyze the data by exploring the hidden patterns of the biological data. Though, data mining can be used in analyzing biological data such as genomic data, proteomic data here Gene Expression (GE) Data is considered for evaluation. GE is generated from Microarrays such as DNA and oligo micro arrays. The generated data is analyzed through the clustering techniques of data mining. This study deals with an implement the basic clustering approach K-Means and various clustering approaches like Hierarchal, Som, Click and basic fuzzy based clustering approach. Eventually, the comparative study of those approaches which lead to the effective approach of cluster analysis of GE.The experimental results shows that proposed algorithm achieve a higher clustering accuracy and takes less clustering time when compared with existing algorithms.
Improving the effectiveness of information retrieval system using adaptive ge...ijcsit
Traditional Genetic Algorithm which is used in previous studies depends on fixed control parameters
especially crossover and mutation probabilities, but in this research we tried to use adaptive genetic
algorithm.
Genetic algorithm started to be applied in information retrieval system in order to optimize the query by
genetic algorithm, a good query is a set of terms that express accurately the information need while being
usable within collection corpus, the last part of this specification is critical for the matching process to be
efficient, that is why most research efforts are actually put toward the query improvement.
We investigated the use of adaptive genetic algorithm (AGA) under vector space model, Extended Boolean
model, and Language model in information retrieval (IR), the algorithm used crossover and mutation
operators with variable probability, where a traditional genetic algorithm (GA) uses fixed values of those,
and remain unchanged during execution. GA is developed to support adaptive adjustment of mutation and
crossover probability; this allows faster attainment of better solutions. The paper has been tested using
242 Arabic abstracts collected from the proceedings of the Saudi Arabian National conference.
Particle Swarm Optimization for Gene cluster IdentificationEditor IJCATR
The understanding of gene regulation is the most basic need for the classification of genes within a DNA. These genes
within the DNA are grouped together into clusters also known as Transcription Units. The genes are grouped into transcription units
for the purpose of construction and regulation of gene expression and synthesis of proteins. This knowledge further contributes as
essential information for the process of drug design and to determine the protein functions of newly sequenced genomes. It is possible
to use the diverse biological information across multiple genomes as an input to the classification problem. The purpose of this work is
to show that Particle Swarm Optimization may provide for more efficient classification as compared to other algorithms. To validate
the approach E.Coli complete genome is taken as the benchmark genome.
COMPUTATIONAL METHODS FOR FUNCTIONAL ANALYSIS OF GENE EXPRESSIONcsandit
Sequencing projects arising from high throughput technologies including those of sequencing DNA microarrays allowed to simultaneously measure the expression levels of millions of genes of a biological sample as well as annotate and identify the role (function) of those genes. Consequently, to better manage and organize this significant amount of information,
bioinformatics approaches have been developed. These approaches provide a representation and a more 'relevant' integration of data in order to test and validate the hypothesis of researchers throughout the experimental cycle. In this context, this article describes and discusses some of techniques used for the functional analysis of gene expression data.
Introduction
Transcriptome analysis
Goal of functional genomics
Why we need functional genomics
Technique
1. At DNA level
2.At RNA level
3. At protein level
4. loss of function
5. functional genomic and bioinformatics
Application
Latest research and reviews
Websites of functional genomics
Conclusions
Reference
Classification of Microarray Gene Expression Data by Gene Combinations using ...IJCSEA Journal
Feature selection has attracted a huge amount of interest in both research and application communities of data mining. Among the large amount of genes presented in gene expression data, only a small fraction of them is effective for performing a certain diagnostic test. Hence, one of the major tasks with the gene expression data is to find groups of co regulated genes whose collective expression is strongly associated with the sample categories or response variables. A framework is proposed in this paper to find informative gene combinations and to classify gene combinations belonging to its relevant subtype by using fuzzy logic. The genes are ranked based on their statistical scores and highly informative genes are filtered. Such genes are fuzzified to identify 2-gene and 3-gene combinations and the intermediate value for each gene is calculated to select top gene combinations to further classify gene lymphoma subtypes by using fuzzy rules. Finally the accuracy of top gene combinations is compared with clustering results. The classification is done using the gene combinations and it is analyzed to predict the accuracy of the results. The work is implemented using java language.
A Review of Various Methods Used in the Analysis of Functional Gene Expressio...ijitcs
Sequencing projects arising from high-throughput technologies including those of sequencing DNA microarray allowed measuring simultaneously the expression levels of millions of genes of a biological sample as well as to annotate and to identify the role (function) of those genes. Consequently, to better manage and organize this significant amount of information, bioinformatics approaches have been developed. These approaches provide a representation and a more 'relevant' integration of data in order to test and validate the researchers’ hypothesis. In this context, this article describes and discusses some techniques used for the functional analysis of gene expression data.
BioNetVisA 2018 ECCB workshop
From biological network reconstruction to data visualization and analysis in molecular biology and medicine.
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/
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SURVEY ON MODELLING METHODS APPLICABLE TO GENE REGULATORY NETWORK
1. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
DOI: 10.5121/ijbb.2013.3302 13
SURVEY ON MODELLING METHODS APPLICABLE
TO GENE REGULATORY NETWORK
Chanda Panse1
and Dr. Manali Kshirsagar1
1
Department of Computer Technology, Yeshwantrao Chavan College of Engineering,
Nagpur, India
1
Department of Computer Technology, Yeshwantrao Chavan College of Engineering,
Nagpur, India
ABSTRACT
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. It gives
information about at which different environmental conditions genes of particular interest get over
expressed or under expressed. Modelling of GRN is nothing but finding interactive relationships between
genes. Interaction can be positive or negative. For inference of GRN, time series data provided by
Microarray technology is used. Key factors to be considered while constructing GRN are scalability,
robustness, reliability and maximum detection of true positive interactions between genes. This paper
gives detailed technical review of existing methods applied for building of GRN along with scope for
future work.
KEYWORDS
Gene Regulatory Network, Microarray, robustness, scalability, reliability
1. INTRODUCTION
Cellular biology is now becoming growing area for researchers to carry out research. There is
ample amount of biological data available because of different, advanced experimental
technologies like Microarray. In order to analyze and retrieve informative knowledge from these
data efficient computational methods are required. These methods helps in knowing interactions
carried out in organisms at genomic levels through some mathematical formalism. Such gene to
gene communications are represented in terms of special network known as Gene Regulatory
Network (GRN). It is a graphical representation in which nodes consist of genes or protein or
metabolites and edges connecting them show regulatory relationships between them. It is
constructed by observing the behaviour of genes and their impact on other genes at particular
experimental condition. This behaviour is analysed in terms of measuring the values of gene
expressions with the help Microarray technology. Gene expressions are dynamic and non-linear
in nature.
When specialised proteins known as Transcription Factors (TFs) bind to promoter region of
DNA, it intervenes the rate of protein synthesis which in turn results in sudden change in values
of gene expressions. This intervention can be positive or negative. If rate of protein synthesis
increases then it is called as activation or up-regulation or positive regulation and if it decreases
then it is called as inhibition or down regulation or negative regulation. If gene g1 positively
regulates gene g2 then it mean there is increase in expression level of g2 because of g1 and in
negative regulation it decreases expression level of g2. GRN is useful for analyzing the effect of
2. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
14
drugs on genes. It also helps in studying dynamics of specific gene under particular diseased or
experimental conditions. It helps in studying diseases caused due to genes (hereditary diseases).
Considering all advantages of GRN, identifying GRN is quite complicated and interesting task
for researchers. There are many mathematical models like [1][2][6][26][17][13] proposed for
inference of GRN but still this research area is in the stage of babyhood because of inability to
reach to maximum detection of true positive interactions between genes.
Depends on the availability of models they are classified into two major categories i.e. traditional
and untraditional. A traditional model includes Boolean Network, Bayesian Network, Linear
Differential Model etc and non-traditional model includes Neural Network model and Model
based on Evolutionary algorithms. This paper gives detailed review of all those mathematical
formalisms used for identifying GRN along with database and experimental setup used. It also
describes challenges while handling gene expression data generated from microarray technology.
The paper is organized as follows: section 2 describes properties of GRN. Section 3 talks about
difficulties in gathering data. Section 4 discusses detailed literature survey of existing models in
terms of model name, mathematical representation and database used accompanying with
advantages and limitation followed by summary and conclusion in last section.
2. PROPERTIES
Gene regulation occurs at any stage of transcription, translation and post-transcription or post-
translation. Regulatory elements involved can be proteins (TFs) or genes or RNAs or
metabolites. Depends on elements involved and mathematical model used GRN has following
properties which have to be considered while inventing new method [11].
2.1. Anatomy of network
GRN is represented using graph. Anatomy of GRN is nothing but the topology of how regulatory
elements are arranged in it. Generally genes are used as nodes of graph and solution from
mathematical models decoded into directed or undirected edges depends on models used. Those
edges can be activatory or inhibitory. How to represent genes and different types of interactive
edges is going to create topology of GRN.
2.2 Sensitivity
GRN are insensitive to the variations in model parameters. This robustness is guaranteed by
models following specific topology. Robust nature of GRN assures same plan for functioning of
genes even though genome is dynamic in nature.
2.3 Noise
Data required for modelling GRN is a biological data which has inherited noise from
biochemical reactions. This noise can be advantageous or disadvantageous to some models.
Therefore handling this noise is the main concern. It is considered in terms of inclusion of some
parameters in mathematical form of model
3. CHALLENGES
For the designing of GRN time series data from Microarray is used which is in the form of
matrix of order N by M where N represents number of genes and M represents length of
3. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
15
experiments which is shown in Fig1. Gij represents expression of gene i at jth time interval of
particular experimental condition.
Genes T1 T2 ....... TM
G1 G11 G12 ....... G1M
G2 G21 G22 ....... G2M
:
:
:
:
:
:
:
:
:
:
GN GN1 GN2 ....... GNM
Figure 1. Microarray time series data
As microarray captures temporal response of genes it is very difficult to select specific model
which will analyze these expression patterns of genes and state causal relationship among them.
Because of this there are many computational challenges described by [29] at different levels of
analysis of time series gene expression. Those four different levels are experimental design, data
analysis, pattern recognition and network. This section is going to focus on challenges to be
faced at above levels.
If there is deficiency in sampling (under sampling) then there is a possibility of missing major
change in gene expression which might respond in later stage of sampling. Along with this if
ample amount of samples are needed then again it is very time consuming as well as costly
process. This is the main challenge at the level of experimental designing.
While doing data analysis there is possibility of unavailability of continuous gene expression for
every time slot. This is the challenge at second level. Microarray experiments also add noise to
data which might or might not be advantageous. For calculating missing gene expression some
effective interpolation techniques are needed.
Third challenge is at pattern analysis level. In pattern analysis genes are grouped into different
clusters on the basis of their mere expression values. Built in dependencies of consecutive time
points are not exploited by existing clustering algorithms which is very important in GRN
modelling. None of the clustering algorithms is taking advantage of this while clustering.
Major challenge at network level is going to generation of such a predictive model which
actually predict biologically possible interaction among genes. i.e. which gene is going to
interact with which other gene, which gene is going to expressed first, which is going to
expressed next. Thus considering limitations of sampling and data analysis it requires pre-
processing of gene expression data before designing model. Pre-processing is in terms of
clustering or noise removing or interpolating missing values. Next section is going to describe
methods and algorithms proposed by different authors for modelling GRN.
4. MATHEMATICAL MODELS
There are large number mathematical models proposed for inference of GRN. On the
basis of their chronological order those are classified into: traditional and un-traditional
models. Traditional model includes Boolean network, Bayesian network, Dynamic
Bayesian. network model, Differential equation model and untraditional model includes models
based on evolutionary algorithms like Genetic algorithms and PSO.
The model discussed in [9] is based on Boolean network without time delay. Value of gene
regulatory relationship is calculated by applying Boolean function which contains Boolean
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16
operators like AND, OR, NOT etc. on at most two genes contributing to in degree of some other
gene G. This model considers conditions of gene disruption and over expression for building
GRN but it is limited to only two regulators not more than that.
Discrete time model developed in [20] is based on weight matrices. All regulatory interactions
between genes are computed as linear coefficient which is summation of independent regulatory
input. i.e. net regulatory effect of gene j on gene i at some time t is given by the expression
( ) = , ( )
where Wij is regulatory influence of gene j on i and uj(t) represents expression level of gene j at
time t. Positive value of Wij represents activation of gene i by gene j and negative value indicates
inhibition. This model suffers from data loss because of not considering dependency in gene
interactions.
Hybrid model developed in [30] uses integration of Boolean network and S-system model.
Boolean network of this model helps in treating large genome data and S-system model adds
loop structure in genetic network which is important and not discussed in prior existing models.
From the observation of pattern of gene expressions a new model is proposed in [12]. From those
observation directed graph with labelled edges are constructed. Because of possibility of existing
false positive regulatory edges the graph is then pruned using combinatorial optimization
problem i.e. simulated annealing but concluded with assumption that some factors like
phosphorylation are required for activation of gene expression which cannot be measured by
gene chips at RNA levels.
Because of only considering two states of gene by Boolean network, intermediate state get
neglected [25] [31]. As an extension of Boolean network, Generalized Logical Network (GLN) is
developed by Thomas and colleagues [32]. It allows having intermediate values of gene
expression (other than 0 and 1) and transition can occur synchronously and asynchronously
between these values. In GLN each node (gene) is associated with generalized truth table (GTT)
which contains all possible values of parent nodes. This GTT maps values of parent nodes to the
value of corresponding child node.
Because of complex nature of gene regulatory process, model based on Bayesian Network (BN)
is proposed in [33]. BN has ability to capture complex stochastic process. In this model genes are
assumed to be random variables and regulatory interaction is obtained by applying joint
probability distribution for each random expression of gene. The Bayesian network which
formed, gives the topology of GRN. Drawback of this network is that it fails to consider dynamic
nature of genes as well feedback loops. To overcome this the model is extended to Dynamic
Bayesian Network(DBN) which considers temporal aspects to gene data.
Inference of GRN using DBN is same as that of Bayesian network except that extra parameters
of time t-1 are added [35]. It is limited to small network because of high computational overhead.
Another two probabilistic graphical models used for reconstruction of GRN using microarray are
discussed in [19]. One is independence graph model which is based on forward and backward
search algorithm and another is based on Gaussian Network (GN) model which is rooted on
Greedy Search method. Out of these two models GN provides better results in terms of gene-
gene interactions than later model but it suffers from longer learning time.
These are all traditional models which can either applied to discrete or continuous data.
In [24] author proposed Neural network based model. Genes are represented by nodes of neural
network and connection between nodes represents regulatory relationship between genes and
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17
weight matrix obtained after training gives type of regulatory relationships. Expression of a gene
gi at time t+Δt is obtained from weight of edge Wij between nodes i,j and expression yi at time t.
Thus regulatory effect on gene gi is obtained from
≈
Sigmoid function is then applied to this equation to transform it in the interval <0,1>. The model
was computationally intensive because of large number of parameters obtained from
experimental data. This is the first non-traditional model used for inference of GRN. It provides
better description of biological system and can be used to better measure the reasonableness of
mathematical model.
Another model based on linear differential equation is implemented in [21]. In this model author
has utilized a statistical method the Lasso to reverse engineer the network. The linear time-
continuous dynamical model is of the form
( )
= ( ) +∈
This method helps in providing insight of GRN on a large scale but cannot be used to get
detailed information of small sub-networks related to biological function.
In order to deal with uncertainty in the function of genes, models based on fuzzy system are
described in [16][5][8]. Once clustering is done in [5], the model is simplified as linear model
and with the help of evidence code generated by Gene Ontology. This assessed function of gene
and gene interaction. But the major limitation of this method is that it shows relationship
between clusters instead of individual gene. Another fuzzy based model in [16] talks about fuzzy
data mining technique which transfers quantitative gene expression values into linguistic terms
which in turn discovers fuzzy dependency relationships between genes from unseen samples and
genes in original database.
Dynamic fuzzy model is developed in [8]. It incorporates structural knowledge in gene network.
This helps in knowing which gene affects another gene and whether effect is positive or
negative. It achieves fast identification of regulatory interaction and captures inherent non-linear
dynamic property of gene network so that prediction of fuzzy gene network will be easier.
A two stage methodology based on Genetic Algorithm (GA) and Expectation Maximization
algorithm is implemented in software GnetXp is mentioned in [10]. In first stage gene
expression trajectories are clustered based on expression values and in second stage Kalman
filtering is applied to parameters of first order differential equation which describes dynamic of
GRN in terms of gene interaction and future time points prediction.
Another method based on ordinary differential equation is employed in [15] which developed
mathematical formulation for identifying gene interaction from time series expression profiles.
But the method is only applicable to small network consisting of 4-8 genes Similar to [10] a
method is proposed in [4]. In this model instead of GA, genetic programming is used to infer
ordinary differential equation framework with noise using time-series data and Kalman filtering
is used to estimate model parameters. Considering this method as a base, Extended Kalman
Filtering (EKF) approach is used to model non-linear dynamics of GRN in [3]. This method
helps to deal with scarcity of time series gene data and EKF is applied to calculate parameters of
model estimated by differential equation.
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As Bayesian Network based modelling is time consuming hybrid approach is proposed in [7]. It
is based on gene expression and gene ontology. This method is going to detect potential gene
regulatory pair which cannot be detected by any general inferring method and also identifies
feedback loops between genes. For this purpose overlap clustering is used to find local
components of global network so that search space of Bayesian Network method get reduced and
then BN is applied on each cluster to infer local networks.
The algorithm based on Bayesian network is proposed in [17]. This algorithm performs several
independence tests on given biological data and then build network from it. Although the
algorithm is for sparse graph, it identifies dense nodes first and then constructs network. The
main advantage of this method is that it can also applicable for large network and helps in
achieving good quality network with minimum computational overhead.
Method discussed in [18] overcomes the problem of Bayesian Network of neglecting time series
data which is useful in identifying direction of regulatory interactions among genes. This method
uses the concept of influence vector. Value of this influence vector associated with individual
gene increases as gene expression increases with time step. Finally vector with highest score
helps in building GRN.
In order to overcome scalability issue of previous methods Markov blanket method for modelling
GRN is presented in [2]. The method starts with formation of Markov Blankets for each gene X.
Putative network model is selected using first and second order partial co-relations on the basis
of achieved excellent meaningful relationships. Again false positive edges are removed from
remaining model using guided GA on the basis of overall network score. Disadvantage of this
method is that there is loss of data as it is applicable to discrete data. Advantage is that it is fast
because of calculation of partial correlations only up to second order and also because of guided
GA which keeps track of intermediate results and calculations.
Another class of evolutionary algorithms i.e. Particle Swarm Optimization (PSO) is used for
finding interaction among genes in many models [26][14][22][6].
Considering limitations of all traditional modelling methods, the best formalism to tackle
complex time series gene data is Recurrent Neural Network (RNN). Inference of GRNs with
RNN Models using PSO uses PSO to train parameters of RNN based model and type of
regulatory interaction between genes is stated with the help of weight matrix obtained after
training.
Modelling GRN using hybrid DEPSO [26] states that main difficulty with RNN is its training.
Training of RNN is done with three different methods: Differential Evolution (DE), PSO, and
hybrid of these two (DE and PSO) DEPSO. Out of these three DEPSO proved to be efficient
than individual method which is then applied for inference of GRN. Parameters of the model are
calculated by training of RNN using this hybrid approach. It has been observed that the method
provides meaningful insight in capturing non-linear dynamics of gene networks and reveals gene
to gene interaction through weight matrix.
Reverse Engineering of Gene Regulatory Networks using Dissipative Particle Swarm
Optimization [22] applied PSO for calculating parameters of S-system which creates
mathematical framework for reverse engineering of GRN. S-system is a Biochemical system
theory represented by following mathematical equation:
= −
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19
Kinetic order gij regulates activation of gene expression Xi due to Xj and hij regulates inhibition of
Xi due to Xj. The method helps in minimizing false positive interactions. It is specifically
applicable to small network of 5-8 genes.
Swarm Intelligence Framework for Reconstructing Gene Networks [6] uses computationally
intelligent two swarm intelligence algorithms i.e. PSO and Ant Colony Optimization (ACO) for
retrieving biologically possible architecture along with RNN. Different architectures are
generated using RNN whose parameters are again trained by PSO and ACO is used for reducing
searching time of networks using stochastic graph generation.
Thus many methods have been reviewed which are summarized in table 1. In accompanying
with this, advantages, limitations, and modelling equations of Bayesian Network, Boolean
network, ordinary differential equation model, and neural network model are discussed in
detailed in [35].
Table 1. Summary Table.
9. International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.3, September 2013
21
3. CONCLUSIONS
Thus, this paper has discussed many methodologies used for inference of GRN.
Many are limited to small GRN consisting of 5-8 genes. Many methodologies are able to find
more true positive interactive edges but along with that they also added false positive edges in
the network. None of the method also discussed about how to deal with curse of dimensionality
problem. Many traditional models are focussing on either structure or structure along with
dynamics [28]. There is lot more scope in modelling GRN with respect to scalability, robustness
and reliability. Major source of Database required for GRN is Gene Expression Omnibus (GEO)
which is freely downloadable. In addition to this tools along with type of data handled by
particular method are discussed in [27].
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Authors
Chanda Panse received her Bachelor of Engineering degree from Pune
University in 2004. She has completed her M.E. in Computer Science and
Engineering from BITS Pilani, Rajasthan in 2008. She is an Asst. Professor in
Yeshwantrao Chavan College of Engineering, Nagpur. She has around 5 Yrs of
teaching experience. Currently she is persueing her Ph.d in Gene Regulation
from Nagpur University. Her area of research includes Data Structures,
Operating Systems, Parallel Programming and Bioinformatics.
Dr. Manali Kshirsagar received her B.E. in Computer Technology, M.E.
Computer Science & Engineering and Ph.D. in the faculty of Computer Science
and Information Technology. Her specialization at the PG and Ph.D. level has
been in the areas of Data Warehousing , Data Mining and Bioinformatics. She
has about 20 technical and research publications to her credit which have
appeared in International Journals, International and National conferences. She
is guiding 08 Ph.D. students at present. With a total experience of 18 years of
teaching and industry. She was Head of Computer Technology Department of
YCCE ,Nagpur from 2008 till 2012. She is currently a Vice- President of ADCC
Infotech, Nagpur.