Modified Half-System Based Method for Reverse Engineering of Gene Regulatory Networks
01 Jul 2020-IEEE/ACM Transactions on Computational Biology and Bioinformatics (IEEE)-Vol. 17, Iss: 4, pp 1303-1316
TL;DR: This work has proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system, which uses half the number of parameters compared to S-systems and thus significantly reduce the computational complexity.
Abstract: The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load. In this work, we have proposed a novel methodology for reverse engineering of gene regulatory networks based on a new technique: half-system . Half-systems use half the number of parameters compared to S-systems and thus significantly reduce the computational complexity. We have implemented our proposed technique for reconstructing four benchmark networks from their corresponding temporal expression profiles: an 8-gene, a 10-gene, and two 20-gene networks. Being a new technique, to the best of our knowledge, there are no comparable results for this in the contemporary literature. Therefore, we have compared our results with those obtained from the contemporary literature using other methodologies, including the state-of-the-art method, GENIE3 . The results obtained in this work stack favourably against the competition, even showing quantifiable improvements in some cases.
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TL;DR: This paper aims to assess the impact of taking into consideration aspects of topological and information content in the evaluation of the final accuracy of an inference procedure, and compares the best inference methods for preserving topological properties and the original information content of synthetic and biological networks.
Abstract: Network inference is a rapidly advancing field, with new methods being proposed on a regular basis. Understanding the advantages and limitations of different network inference methods is key to their effective application in different circumstances. The common structural properties shared by diverse networks naturally pose a challenge when it comes to devising accurate inference methods, but surprisingly, there is a paucity of comparison and evaluation methods. Historically, every new methodology has only been tested against \textit{gold standard} (true values) purpose-designed synthetic and real-world (validated) biological networks. In this paper we aim to assess the impact of taking into consideration aspects of topological and information content in the evaluation of the final accuracy of an inference procedure. Specifically, we will compare the best inference methods, in both graph-theoretic and information-theoretic terms, for preserving topological properties and the original information content of synthetic and biological networks. New methods for performance comparison are introduced by borrowing ideas from gene set enrichment analysis and by applying concepts from algorithmic complexity. Experimental results show that no individual algorithm outperforms all others in all cases, and that the challenging and non-trivial nature of network inference is evident in the struggle of some of the algorithms to turn in a performance that is superior to random guesswork. Therefore special care should be taken to suit the method to the purpose at hand. Finally, we show that evaluations from data generated using different underlying topologies have different signatures that can be used to better choose a network reconstruction method.
17 citations
19 Jul 2020
TL;DR: This work has implemented the proposed hybrid methodology on the real-world experimental datasets (in vivo) of the SOS DNA Repair network of Escherichia coli and the obtained results are comparable to or better than that of other reverse engineering methodologies present in contemporary literature.
Abstract: In this work, a computational approach has been proposed based on the hybridisation of two modelling formalisms, recurrent neural networks and half-systems, for the reconstruction of gene regulatory networks from time-series gene expression datasets. To the best of our knowledge, the proposed hybridisation has not been attempted previously in this domain. Here, recurrent neural networks and half-systems have been hybridised to capture the underlying dynamics present in the temporal gene expression profiles. The motivation behind this work is to integrate the advantages of both the techniques in the proposed model such that the problem of reverse engineering of gene regulatory networks can be resolved more efficiently. Artificial bee colony optimisation has been used for the estimation of the model parameters. We have implemented the proposed hybrid methodology on the real-world experimental datasets (in vivo) of the SOS DNA Repair network of Escherichia coli. The obtained results are comparable to or better than that of other reverse engineering methodologies present in contemporary literature.
3 citations
Cites background or methods from "Modified Half-System Based Method f..."
...Reconstruction of GRNs is an ill-posed problem, and thus suffers from over-fitting [6]....
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...The total number of parameters to be trained for each gene in the modified HS formalism [6] is (N + 3), which is still less than S-system....
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...To avoid such issues, we have employed the HS formalism [6], in place...
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...Similarly, the proposed technique can identify the same or a greater number of TPs compared to the RNN based technique [30], except for Dataset 1, and the HS based technique [6], except for Dataset 4....
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...Inherently, HS has all the benefits of S-systems except its stability, which has been rectified by the authors [6]....
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TL;DR: An intelligent framework of recurrent neural network (RNN) and swarm intelligence (SI) based Particle Swarm Optimization (PSO) with controlled behaviour has been proposed for the reconstruction of GRN from time-series gene expression data and shows superior performance over traditional SI algorithms in searching biologically plausible candidate networks.
Abstract: Gene Regulatory Networks (GRNs) are the pioneering methodology for finding new gene interactions getting insights of the biological processes using time series gene expression data. It remains a challenge to study the temporal nature of gene expression data that mimic complex non-linear dynamics of the network. In this paper, an intelligent framework of recurrent neural network (RNN) and swarm intelligence (SI) based Particle Swarm Optimization (PSO) with controlled behaviour has been proposed for the reconstruction of GRN from time-series gene expression data. A novel PSO algorithm enhanced by human cognition influenced by the ideology of Bhagavad Gita is employed for improved learning of RNN. RNN guided by the proposed algorithm simulates the nonlinear and dynamic gene interactions to a greater extent. The proposed method shows superior performance over traditional SI algorithms in searching biologically plausible candidate networks. The strength of the method is verified by analyzing the small artificial network and real data of Escherichia coli with improved accuracy.
2 citations
01 Jul 2022
TL;DR: In this article , a penalty term was used for candidate selection during the process of optimizing the DRNI (Dynamically Regulated Network Initialization) algorithm, which encourages the generation of valid candidate solutions, and eliminates invalid networks in a systematic manner.
Abstract: S-System models, non-linear differential equation models, are widely used for reconstructing gene regulatory networks from temporal gene expression data. An S-System model involves two states, generation and degeneration, and uses the kinetic parameters gij and hij, to represent the direction, nature, and intensity of the genetic interactions. The need for learning a large number of model parameters results in increased computational expense. Previously, we improved the performance of the algorithm using dynamic allocation of the maximum in-degree for each gene. While the method was effective for smaller networks, a large amount of computation was still needed for larger networks. This problem arose mainly due to the increased occurrence of invalid networks during optimization, primarily because the two kinetic parameters (gij and hij) of the S-System model converge independently during optimization. Being independent, these two parameters can converge to values that can indicate contradictory gene interactions, specifically inhibition or activation. In this study, to address this major challenge in S-System modelling, we developed a novel method that includes two features: a penalty term that penalizes those networks with invalid kinetic orders, and a parameter, wij, derived by combining the kinetic parameters gij and hij. The novel penalty term was used for candidate selection during the process of optimizing the DRNI (Dynamically Regulated Network Initialization) algorithm. Rather than remaining constant, it is dynamic, with its magnitude dependent on the number of invalid interactions in the given network. This approach encourages the generation of valid candidate solutions, and eliminates invalid networks in a systematic manner. The previous DRNI method, a two-stage approach which uses dynamic allocation of the maximum in-degree for each gene, was further improved by adding a third stage which applies the proposed wij to handle the invalid regulations that may still exist in that candidate solutions. The method was tested on different gene expression datasets, and was able to reduce the number of iterations and produce improved network accuracies. For a 20 gene network, the number of generations required for convergence was reduced by 300, and the F-score improved by 0.05 compared to our previously reported DRNI approach. For the well-known 10 gene networks of the DREAM challenge, our method produced an improvement in the average area under the ROC curve of the DREAM4 10 gene networks.
2 citations
TL;DR: In this paper , a multi-objective particle swarm optimization algorithm was applied to two different formulations of the problem model, the S-system and the recently introduced half-system, to reconstruct gene regulatory networks from gene expression data.
Abstract: AbstractVarious approaches are used to reconstruct gene regulatory networks from gene expression data. This work applies a multi-objective particle swarm optimization algorithm to two different formulations of the problem model, the S-system and the recently introduced half-system. Two methods to set a threshold for distinguishing between existing and non-existing gene–gene interactions are tested. The S-system and the half-system show similar performance although the tested implementations applied to a gene expression benchmark dataset could not sufficiently outperform the performances of other methods previously reported in the literature.KeywordsGene regulatory networkS-systemHalf-systemMulti-objective optimizationParticle swarm optimization
References
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TL;DR: The Bat Algorithm as mentioned in this paper is based on the echolocation behavior of bats and combines the advantages of existing algorithms into the new bat algorithm to solve many tough optimization problems.
Abstract: Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
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TL;DR: A new framework for discovering interactions between genes based on multiple expression measurements is proposed and a method for recovering gene interactions from microarray data is described using tools for learning Bayesian networks.
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3,507 citations
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