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Proceedings ArticleDOI

Gene regulatory networks using bat algorithm inspired particle swarm optimization

TL;DR: A statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data and results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success.
Abstract: Here, we have proposed a statistical framework based on a novel bat algorithm inspired particle swarm optimisation algorithm for the reconstruction of gene regulatory networks from temporal gene expression data. The recurrent neural network formalism has been implemented to extract the underlying dynamics from time series microarray datasets accurately. The proposed swarm intelligence framework has been used for optimising the parameters of the recurrent neural network model. Preliminary research with the proposed methodology has been done on a small, artificial network and the experimental (in vivo) microarray data of the SOS DNA repair network of Escherichia coli. Results obtained suggest that the proposed methodology can infer the underlying network structures with a better degree of success.
Citations
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Book ChapterDOI
26 Apr 2017
TL;DR: A novel quantum computing based technique for the reverse engineering of gene regulatory networks from time-series genetic expression datasets is proposed, suggesting that quantum computing technique significantly reduces the computational time, retaining the accuracy of the inferred gene Regulatory networks to a comparatively satisfactory level.
Abstract: The accurate reconstruction of gene regulatory networks from temporal gene expression data is crucial for the identification of genetic inter-regulations at the cellular level. This will help us to comprehend the working of living entities properly. Here, we have proposed a novel quantum computing based technique for the reverse engineering of gene regulatory networks from time-series genetic expression datasets. The dynamics of the temporal expression profiles have been modelled using the recurrent neural network formalism. The corresponding training of model parameters has been realised with the help of the proposed quantum computing methodology based concepts. This is based on entanglement and decoherence concepts. The application of quantum computing technique in this domain of research is comparatively new. The results obtained using this technique is highly satisfactory. We have applied it to a 4-gene artificial genetic network model, which was previously studied by other researchers. Also, a 10-gene and a 20-gene genetic network have been studied using the proposed technique. The obtained results suggest that quantum computing technique significantly reduces the computational time, retaining the accuracy of the inferred gene regulatory networks to a comparatively satisfactory level.

2 citations

References
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Journal ArticleDOI
TL;DR: The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data, and opening the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.
Abstract: A basic challenge in systems biology is to understand the dynamical behavior of gene regulation networks. Current approaches aim at determining the network structure based on genomic-scale data. However, the network connectivity alone is not sufficient to define its dynamics; one needs to also specify the kinetic parameters for the regulation reactions. Here, we ask whether effective kinetic parameters can be assigned to a transcriptional network based on expression data. We present a combined experimental and theoretical approach based on accurate high temporal-resolution measurement of promoter activities from living cells by using green fluorescent protein (GFP) reporter plasmids. We present algorithms that use these data to assign effective kinetic parameters within a mathematical model of the network. To demonstrate this, we employ a well defined network, the SOS DNA repair system of Escherichia coli. We find a strikingly detailed temporal program of expression that correlates with the functional role of the SOS genes and is driven by a hierarchy of effective kinetic parameter strengths for the various promoters. The calculated parameters can be used to determine the kinetics of all SOS genes given the expression profile of just one representative, allowing a significant reduction in complexity. The concentration profile of the master SOS transcriptional repressor can be calculated, demonstrating that relative protein levels may be determined from purely transcriptional data. This finding opens the possibility of assigning kinetic parameters to transcriptional networks on a genomic scale.

539 citations


"Gene regulatory networks using bat ..." refers background in this paper

  • ...[12] experimentally studied, in addition to recA and lexA, six genes majorly involved in the SOS repair system, namely, uvrA, uvrD, uvrY, umuD, ruvA and polB....

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Journal ArticleDOI
TL;DR: In this review, genetic network models are put in a historical perspective that explains why certain models were introduced and the principal differences and similarities between the approaches are given by considering the qualitative properties of the chosen models and their learning strategies.
Abstract: The inference of genetic interactions from measured expression data is one of the most challenging tasks of modern functional genomics. When successful, the learned network of regulatory interactions yields a wealth of useful information. An inferred genetic network contains information about the pathway to which a gene belongs and which genes it interacts with. Furthermore, it explains the function of the gene in terms of how it influences other genes and indicates which genes are pathway initiators and therefore potential drug targets. Obviously, such wealth comes at a price and that of genetic network modeling is that it is an extremely complex task. Therefore, it is necessary to develop sophisticated computational tools that are able to extract relevant information from a limited set of microarray measurements and integrate this with different information sources, to come up with reliable hypotheses of a genetic regulatory network. Thus far, a multitude of modeling approaches have been proposed for discovering genetic networks. However, it is unclear what the advantages and disadvantages of each of the different approaches are and how their results can be compared. In this review, genetic network models are put in a historical perspective that explains why certain models were introduced. Various modeling assumptions and their consequences are also highlighted. In addition, an overview of the principal differences and similarities between the approaches is given by considering the qualitative properties of the chosen models and their learning strategies.

207 citations


"Gene regulatory networks using bat ..." refers background in this paper

  • ...Fortunately, extensive biological investigations, in the context of the reconstruction of GRNs, reveal that there is only a handful of regulators in a GRN [11]....

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Journal ArticleDOI
TL;DR: Zhang et al. as mentioned in this paper proposed a particle swarm optimization (PSO) based approach to infer genetic regulatory networks from time series gene expression data, which can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.
Abstract: Genetic regulatory network inference is critically important for revealing fundamental cellular processes, investigating gene functions, and understanding their relations. The availability of time series gene expression data makes it possible to investigate the gene activities of whole genomes, rather than those of only a pair of genes or among several genes. However, current computational methods do not sufficiently consider the temporal behavior of this type of data and lack the capability to capture the complex nonlinear system dynamics. We propose a recurrent neural network (RNN) and particle swarm optimization (PSO) approach to infer genetic regulatory networks from time series gene expression data. Under this framework, gene interaction is explained through a connection weight matrix. Based on the fact that the measured time points are limited and the assumption that the genetic networks are usually sparsely connected, we present a PSO-based search algorithm to unveil potential genetic network constructions that fit well with the time series data and explore possible gene interactions. Furthermore, PSO is used to train the RNN and determine the network parameters. Our approach has been applied to both synthetic and real data sets. The results demonstrate that the RNN/PSO can provide meaningful insights in understanding the nonlinear dynamics of the gene expression time series and revealing potential regulatory interactions between genes.

173 citations

Journal ArticleDOI
TL;DR: A method for determining the parameters of genetic regulatory networks, given expression level time series data, is introduced and evaluated using artificial data and applied to a set of actual expression data from the development of rat central nervous system.
Abstract: We have modeled genetic regulatory networks in the framework of continuous-time recurrent neural networks. A method for determining the parameters of such networks, given expression level time series data, is introduced and evaluated using artificial data. The method is also applied to a set of actual expression data from the development of rat central nervous system.

158 citations


Additional excerpts

  • ...genetic algorithm (GA) [7], differential evolution [8-9], particle swarm optimisation [9-10], etc....

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Journal ArticleDOI
TL;DR: The comparison proves that the neural network model describes behavior of the system in full agreement with experiments; moreover, it predicts its function in experimentally inaccessible situations and explains the experimental observations.

144 citations


"Gene regulatory networks using bat ..." refers methods in this paper

  • ...The recurrent neural network (RNN) formalism is used to model the dynamics of a GRN [4]....

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  • ...Another such model is the RNN [4], which has been employed effectively in the reconstruction of GRNs from temporal expression data....

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