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Huang Y, Zhou X. Research on strip crown by uncertain sampling strategy modified particle swarm optimization with RBF neural network. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109661] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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2
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Tan Y, Neto FBL, Neto UB. PALLAS: Penalized mAximum LikeLihood and pArticle Swarms for Inference of Gene Regulatory Networks From Time Series Data. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022; 19:1807-1816. [PMID: 33170782 DOI: 10.1109/tcbb.2020.3037090] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We present PALLAS, a practical method for gene regulatory network (GRN) inference from time series data, which employs penalized maximum likelihood and particle swarms for optimization. PALLAS is based on the Partially-Observable Boolean Dynamical System (POBDS) model and thus does not require ad-hoc binarization of the data. The penalty in the likelihood is a LASSO regularization term, which encourages the resulting network to be sparse. PALLAS is able to scale to networks of realistic size under no prior knowledge, by virtue of a novel continuous-discrete Fish School Search particle swarm algorithm for efficient simultaneous maximization of the penalized likelihood over the discrete space of networks and the continuous space of observational parameters. The performance of PALLAS is demonstrated by a comprehensive set of experiments using synthetic data generated from real and artificial networks, as well as real time series microarray and RNA-seq data, where it is compared to several other well-known methods for gene regulatory network inference. The results show that PALLAS can infer GRNs more accurately than other methods, while being capable of working directly on gene expression data, without need of ad-hoc binarization. PALLAS is a fully-fledged program, written in python, and available on GitHub (https://github.com/yukuntan92/PALLAS).
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Johnson ZJ, Krutkin DD, Bohutskyi P, Kalyuzhnaya MG. Metals and methylotrophy: Via global gene expression studies. Methods Enzymol 2021; 650:185-213. [PMID: 33867021 DOI: 10.1016/bs.mie.2021.01.046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A number of minerals, such as copper, cobalt, and rare earth elements (REE), are essential modulators of microbial one-carbon metabolism. This chapter provides an overview of the gene expression study design and analysis protocols for uncovering REE-induced changes in methylotrophic bacteria. By interrogating relationships and differences in total gene expression induced by mineral micronutrients, a deeper understanding of gene regulation at a systems scale can be gained. With careful design and execution of RNA-sequencing experiments, thorough processing and assessment of read quality can be utilized to assess and adjust for possible biases. By ensuring only quality data are utilized in downstream processes, differential gene expression, overrepresented analyses, and gene-set enrichment analyses provide reliable and reproducible representation of pathways and functions which are being affected by changes in environmental conditions.
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Affiliation(s)
- Zachary J Johnson
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Dennis D Krutkin
- Department of Biology, San Diego State University, San Diego, CA, United States
| | - Pavlo Bohutskyi
- Pacific Northwest National Laboratory, Richland, WA, United States
| | - Marina G Kalyuzhnaya
- Department of Biology, San Diego State University, San Diego, CA, United States.
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Khan A, Saha G, Pal RK. Modified Half-System Based Method for Reverse Engineering of Gene Regulatory Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:1303-1316. [PMID: 30640623 DOI: 10.1109/tcbb.2019.2892450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
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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Liu L, Liu J. Reconstructing gene regulatory networks via memetic algorithm and LASSO based on recurrent neural networks. Soft comput 2020. [DOI: 10.1007/s00500-019-04185-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ahmed SS, Roy S, Kalita J. Assessing the Effectiveness of Causality Inference Methods for Gene Regulatory Networks. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020; 17:56-70. [PMID: 29994618 DOI: 10.1109/tcbb.2018.2853728] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Causality inference is the use of computational techniques to predict possible causal relationships for a set of variables, thereby forming a directed network. Causality inference in Gene Regulatory Networks (GRNs) is an important, yet challenging task due to the limits of available data and lack of efficiency in existing causality inference techniques. A number of techniques have been proposed and applied to infer causal relationships in various domains, although they are not specific to regulatory network inference. In this paper, we assess the effectiveness of methods for inferring causal GRNs. We introduce seven different inference methods and apply them to infer directed edges in GRNs. We use time-series expression data from the DREAM challenges to assess the methods in terms of quality of inference and rank them based on performance. The best method is applied to Breast Cancer data to infer a causal network. Experimental results show that Causation Entropy is best, however, highly time-consuming and not feasible to use in a relatively large network. We infer Breast Cancer GRN with the second-best method, Transfer Entropy. The topological analysis of the network reveals that top out-degree genes such as SLC39A5 which are considered central genes, play important role in cancer progression.
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Wani N, Raza K. Integrative approaches to reconstruct regulatory networks from multi-omics data: A review of state-of-the-art methods. Comput Biol Chem 2019; 83:107120. [PMID: 31499298 DOI: 10.1016/j.compbiolchem.2019.107120] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Revised: 02/22/2019] [Accepted: 08/27/2019] [Indexed: 02/06/2023]
Abstract
Data generation using high throughput technologies has led to the accumulation of diverse types of molecular data. These data have different types (discrete, real, string, etc.) and occur in various formats and sizes. Datasets including gene expression, miRNA expression, protein-DNA binding data (ChIP-Seq/ChIP-ChIP), mutation data (copy number variation, single nucleotide polymorphisms), annotations, interactions, and association data are some of the commonly used biological datasets to study various cellular mechanisms of living organisms. Each of them provides a unique, complementary and partly independent view of the genome and hence embed essential information about the regulatory mechanisms of genes and their products. Therefore, integrating these data and inferring regulatory interactions from them offer a system level of biological insight in predicting gene functions and their phenotypic outcomes. To study genome functionality through regulatory networks, different methods have been proposed for collective mining of information from an integrated dataset. We survey here integration methods that reconstruct regulatory networks using state-of-the-art techniques to handle multi-omics (i.e., genomic, transcriptomic, proteomic) and other biological datasets.
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Affiliation(s)
- Nisar Wani
- Govt. Degree College Baramulla, J & K, India; Department of Computer Science, jamia Milia Islamia, New Delhi, India
| | - Khalid Raza
- Department of Computer Science, jamia Milia Islamia, New Delhi, India.
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Jana B, Mitra S, Acharyya S. Repository and Mutation based Particle Swarm Optimization (RMPSO): A new PSO variant applied to reconstruction of Gene Regulatory Network. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2018.09.027] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
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Kordmahalleh MM, Sefidmazgi MG, Harrison SH, Homaifar A. Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network. BioData Min 2017; 10:29. [PMID: 28785315 PMCID: PMC5543747 DOI: 10.1186/s13040-017-0146-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2017] [Accepted: 07/14/2017] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND The modeling of genetic interactions within a cell is crucial for a basic understanding of physiology and for applied areas such as drug design. Interactions in gene regulatory networks (GRNs) include effects of transcription factors, repressors, small metabolites, and microRNA species. In addition, the effects of regulatory interactions are not always simultaneous, but can occur after a finite time delay, or as a combined outcome of simultaneous and time delayed interactions. Powerful biotechnologies have been rapidly and successfully measuring levels of genetic expression to illuminate different states of biological systems. This has led to an ensuing challenge to improve the identification of specific regulatory mechanisms through regulatory network reconstructions. Solutions to this challenge will ultimately help to spur forward efforts based on the usage of regulatory network reconstructions in systems biology applications. METHODS We have developed a hierarchical recurrent neural network (HRNN) that identifies time-delayed gene interactions using time-course data. A customized genetic algorithm (GA) was used to optimize hierarchical connectivity of regulatory genes and a target gene. The proposed design provides a non-fully connected network with the flexibility of using recurrent connections inside the network. These features and the non-linearity of the HRNN facilitate the process of identifying temporal patterns of a GRN. RESULTS Our HRNN method was implemented with the Python language. It was first evaluated on simulated data representing linear and nonlinear time-delayed gene-gene interaction models across a range of network sizes and variances of noise. We then further demonstrated the capability of our method in reconstructing GRNs of the Saccharomyces cerevisiae synthetic network for in vivo benchmarking of reverse-engineering and modeling approaches (IRMA). We compared the performance of our method to TD-ARACNE, HCC-CLINDE, TSNI and ebdbNet across different network sizes and levels of stochastic noise. We found our HRNN method to be superior in terms of accuracy for nonlinear data sets with higher amounts of noise. CONCLUSIONS The proposed method identifies time-delayed gene-gene interactions of GRNs. The topology-based advancement of our HRNN worked as expected by more effectively modeling nonlinear data sets. As a non-fully connected network, an added benefit to HRNN was how it helped to find the few genes which regulated the target gene over different time delays.
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Affiliation(s)
- Mina Moradi Kordmahalleh
- Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 E. Market Street, Greensboro, 27411 NC USA
| | - Mohammad Gorji Sefidmazgi
- Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 E. Market Street, Greensboro, 27411 NC USA
| | - Scott H Harrison
- Department of Biology, North Carolina A&T State University, 1601 E. Market Street, Greensboro, 27411 NC USA
| | - Abdollah Homaifar
- Department of Electrical and Computer Engineering, North Carolina A&T State University, 1601 E. Market Street, Greensboro, 27411 NC USA
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A new differential evolution algorithm for solving multimodal optimization problems with high dimensionality. Soft comput 2017. [DOI: 10.1007/s00500-017-2632-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Recurrent neural network based hybrid model for reconstructing gene regulatory network. Comput Biol Chem 2016; 64:322-334. [PMID: 27570069 DOI: 10.1016/j.compbiolchem.2016.08.002] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 05/01/2016] [Accepted: 08/13/2016] [Indexed: 11/22/2022]
Abstract
One of the exciting problems in systems biology research is to decipher how genome controls the development of complex biological system. The gene regulatory networks (GRNs) help in the identification of regulatory interactions between genes and offer fruitful information related to functional role of individual gene in a cellular system. Discovering GRNs lead to a wide range of applications, including identification of disease related pathways providing novel tentative drug targets, helps to predict disease response, and also assists in diagnosing various diseases including cancer. Reconstruction of GRNs from available biological data is still an open problem. This paper proposes a recurrent neural network (RNN) based model of GRN, hybridized with generalized extended Kalman filter for weight update in backpropagation through time training algorithm. The RNN is a complex neural network that gives a better settlement between biological closeness and mathematical flexibility to model GRN; and is also able to capture complex, non-linear and dynamic relationships among variables. Gene expression data are inherently noisy and Kalman filter performs well for estimation problem even in noisy data. Hence, we applied non-linear version of Kalman filter, known as generalized extended Kalman filter, for weight update during RNN training. The developed model has been tested on four benchmark networks such as DNA SOS repair network, IRMA network, and two synthetic networks from DREAM Challenge. We performed a comparison of our results with other state-of-the-art techniques which shows superiority of our proposed model. Further, 5% Gaussian noise has been induced in the dataset and result of the proposed model shows negligible effect of noise on results, demonstrating the noise tolerance capability of the model.
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12
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Taghiyeh S, Xu J. A new particle swarm optimization algorithm for noisy optimization problems. SWARM INTELLIGENCE 2016. [DOI: 10.1007/s11721-016-0125-2] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Khan A, Mandal S, Pal RK, Saha G. Construction of Gene Regulatory Networks Using Recurrent Neural Networks and Swarm Intelligence. SCIENTIFICA 2016; 2016:1060843. [PMID: 27298752 PMCID: PMC4889854 DOI: 10.1155/2016/1060843] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Revised: 04/19/2016] [Accepted: 04/24/2016] [Indexed: 06/06/2023]
Abstract
We have proposed a methodology for the reverse engineering of biologically plausible gene regulatory networks from temporal genetic expression data. We have used established information and the fundamental mathematical theory for this purpose. We have employed the Recurrent Neural Network formalism to extract the underlying dynamics present in the time series expression data accurately. We have introduced a new hybrid swarm intelligence framework for the accurate training of the model parameters. The proposed methodology has been first applied to a small artificial network, and the results obtained suggest that it can produce the best results available in the contemporary literature, to the best of our knowledge. Subsequently, we have implemented our proposed framework on experimental (in vivo) datasets. Finally, we have investigated two medium sized genetic networks (in silico) extracted from GeneNetWeaver, to understand how the proposed algorithm scales up with network size. Additionally, we have implemented our proposed algorithm with half the number of time points. The results indicate that a reduction of 50% in the number of time points does not have an effect on the accuracy of the proposed methodology significantly, with a maximum of just over 15% deterioration in the worst case.
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Affiliation(s)
- Abhinandan Khan
- Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector III, Salt Lake City, Kolkata, West Bengal 700 098, India
| | - Sudip Mandal
- Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector III, Salt Lake City, Kolkata, West Bengal 700 098, India
| | - Rajat Kumar Pal
- Department of Computer Science and Engineering, University of Calcutta, Acharya Prafulla Chandra Roy Siksha Prangan, JD-2, Sector III, Salt Lake City, Kolkata, West Bengal 700 098, India
| | - Goutam Saha
- Department of Information Technology, North Eastern Hill University, Umshing-Mawkynroh, Shillong, Meghalaya 793 022, India
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Chen ZY, Kuo RJ, Hu TL. An Integrated Hybrid Algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning. INT J ARTIF INTELL T 2016. [DOI: 10.1142/s0218213016500044] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.
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Affiliation(s)
- Zhen-Yao Chen
- Department of Business Administration, DE LIN Institute of Technology, No. 1, Ln. 380, Qingyun Rd., Tucheng Dist., New Taipei City 23654, Taiwan
| | - R. J. Kuo
- Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Rd., Taipei City 10607, Taiwan
| | - Tung-Lai Hu
- Department of Business Management, National Taipei University of Technology, No. 1, Sec. 3, Zhongxiao E. Rd., Taipei City 10608, Taiwan
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15
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Neural model of gene regulatory network: a survey on supportive meta-heuristics. Theory Biosci 2016; 135:1-19. [DOI: 10.1007/s12064-016-0224-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2015] [Accepted: 03/21/2016] [Indexed: 12/21/2022]
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16
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Tang B, Zhu Z, Luo J. Hybridizing Particle Swarm Optimization and Differential Evolution for the Mobile Robot Global Path Planning. INT J ADV ROBOT SYST 2016. [DOI: 10.5772/63812] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022] Open
Abstract
Global path planning is a challenging issue in the filed of mobile robotics due to its complexity and the nature of non-deterministic polynomial-time hard (NP-hard). Particle swarm optimization (PSO) has gained increasing popularity in global path planning due to its simplicity and high convergence speed. However, since the basic PSO has difficulties balancing exploration and exploitation, and suffers from stagnation, its efficiency in solving global path planning may be restricted. Aiming at overcoming these drawbacks and solving the global path planning problem efficiently, this paper proposes a hybrid PSO algorithm that hybridizes PSO and differential evolution (DE) algorithms. To dynamically adjust the exploration and exploitation abilities of the hybrid PSO, a novel PSO, the nonlinear time-varying PSO (NTVPSO), is proposed for updating the velocities and positions of particles in the hybrid PSO. In an attempt to avoid stagnation, a modified DE, the ranking-based self-adaptive DE (RBSADE), is developed to evolve the personal best experience of particles in the hybrid PSO. The proposed algorithm is compared with four state-of-the-art evolutionary algorithms. Simulation results show that the proposed algorithm is highly competitive in terms of path optimality and can be considered as a vital alternative for solving global path planning.
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Affiliation(s)
- Biwei Tang
- National Key Laboratory of Aerospace and Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi'an, China
| | - Zhanxia Zhu
- National Key Laboratory of Aerospace and Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi'an, China
| | - Jianjun Luo
- National Key Laboratory of Aerospace and Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi'an, China
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Berrones A, Jiménez E, Alcorta-García MA, Almaguer FJ, Peña B. Parameter inference of general nonlinear dynamical models of gene regulatory networks from small and noisy time series. Neurocomputing 2016. [DOI: 10.1016/j.neucom.2015.10.095] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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18
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Time-Delayed Models of Gene Regulatory Networks. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:347273. [PMID: 26576197 PMCID: PMC4632181 DOI: 10.1155/2015/347273] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Revised: 08/31/2015] [Accepted: 09/14/2015] [Indexed: 11/17/2022]
Abstract
We discuss different mathematical models of gene regulatory networks as relevant to the onset and development of cancer. After discussion of alternative modelling approaches, we use a paradigmatic two-gene network to focus on the role played by time delays in the dynamics of gene regulatory networks. We contrast the dynamics of the reduced model arising in the limit of fast mRNA dynamics with that of the full model. The review concludes with the discussion of some open problems.
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Ling G, Guan ZH, He DX, Liao RQ, Zhang XH. Stability and bifurcation analysis of new coupled repressilators in genetic regulatory networks with delays. Neural Netw 2014; 60:222-31. [PMID: 25261687 DOI: 10.1016/j.neunet.2014.08.012] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2013] [Revised: 08/07/2014] [Accepted: 08/27/2014] [Indexed: 10/24/2022]
Abstract
The genetic regulatory networks are complex dynamic systems which reflect various kinetic behaviors of living things. In this paper, a new structure of coupled repressilators is introduced to exploit the underlying functions. The new coupled repressilator model consists of two identical repressilators inhibiting each other directly. The coupling delays are taken into account. The existence of a unique equilibrium for this system is verified firstly, then the stability criteria for equilibrium are analyzed without and with coupling delays. The different functions on equilibrium and its stability played by related biochemical parameters in the structure including maximal transcription rate, coupling strength, the decay rate ratio between proteins and mRNAs, and coupling delays are discussed. At last, several numerical simulations are made to demonstrate our results.
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Affiliation(s)
- Guang Ling
- College of Automation, Huazhong University of Science and Technology, Wuhan 430074, China; School of Science, Tianjin University of Technology and Education, Tianjin 300222, China
| | - Zhi-Hong Guan
- College of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Ding-Xin He
- College of Automation, Huazhong University of Science and Technology, Wuhan 430074, China.
| | - Rui-Quan Liao
- Petroleum Engineering College, Yangtze University, Jingzhou 420400, China
| | - Xian-He Zhang
- College of Mechatronics and Control Engineering, Hubei Normal University, Huangshi 435002, China
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Biochemical systems identification by a random drift particle swarm optimization approach. BMC Bioinformatics 2014; 15 Suppl 6:S1. [PMID: 25078435 PMCID: PMC4158603 DOI: 10.1186/1471-2105-15-s6-s1] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background Finding an efficient method to solve the parameter estimation problem (inverse
problem) for nonlinear biochemical dynamical systems could help promote the
functional understanding at the system level for signalling pathways. The problem
is stated as a data-driven nonlinear regression problem, which is converted into a
nonlinear programming problem with many nonlinear differential and algebraic
constraints. Due to the typical ill conditioning and multimodality nature of the
problem, it is in general difficult for gradient-based local optimization methods
to obtain satisfactory solutions. To surmount this limitation, many stochastic
optimization methods have been employed to find the global solution of the
problem. Results This paper presents an effective search strategy for a particle swarm optimization
(PSO) algorithm that enhances the ability of the algorithm for estimating the
parameters of complex dynamic biochemical pathways. The proposed algorithm is a
new variant of random drift particle swarm optimization (RDPSO), which is used to
solve the above mentioned inverse problem and compared with other well known
stochastic optimization methods. Two case studies on estimating the parameters of
two nonlinear biochemical dynamic models have been taken as benchmarks, under both
the noise-free and noisy simulation data scenarios. Conclusions The experimental results show that the novel variant of RDPSO algorithm is able to
successfully solve the problem and obtain solutions of better quality than other
global optimization methods used for finding the solution to the inverse problems
in this study.
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21
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Discrete particle swarm optimization for identifying community structures in signed social networks. Neural Netw 2014; 58:4-13. [PMID: 24856248 DOI: 10.1016/j.neunet.2014.04.006] [Citation(s) in RCA: 72] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2013] [Revised: 04/28/2014] [Accepted: 04/29/2014] [Indexed: 11/20/2022]
Abstract
Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising.
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22
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An algebra-based method for inferring gene regulatory networks. BMC SYSTEMS BIOLOGY 2014; 8:37. [PMID: 24669835 PMCID: PMC4022379 DOI: 10.1186/1752-0509-8-37] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/12/2013] [Accepted: 03/06/2014] [Indexed: 11/10/2022]
Abstract
BACKGROUND The inference of gene regulatory networks (GRNs) from experimental observations is at the heart of systems biology. This includes the inference of both the network topology and its dynamics. While there are many algorithms available to infer the network topology from experimental data, less emphasis has been placed on methods that infer network dynamics. Furthermore, since the network inference problem is typically underdetermined, it is essential to have the option of incorporating into the inference process, prior knowledge about the network, along with an effective description of the search space of dynamic models. Finally, it is also important to have an understanding of how a given inference method is affected by experimental and other noise in the data used. RESULTS This paper contains a novel inference algorithm using the algebraic framework of Boolean polynomial dynamical systems (BPDS), meeting all these requirements. The algorithm takes as input time series data, including those from network perturbations, such as knock-out mutant strains and RNAi experiments. It allows for the incorporation of prior biological knowledge while being robust to significant levels of noise in the data used for inference. It uses an evolutionary algorithm for local optimization with an encoding of the mathematical models as BPDS. The BPDS framework allows an effective representation of the search space for algebraic dynamic models that improves computational performance. The algorithm is validated with both simulated and experimental microarray expression profile data. Robustness to noise is tested using a published mathematical model of the segment polarity gene network in Drosophila melanogaster. Benchmarking of the algorithm is done by comparison with a spectrum of state-of-the-art network inference methods on data from the synthetic IRMA network to demonstrate that our method has good precision and recall for the network reconstruction task, while also predicting several of the dynamic patterns present in the network. CONCLUSIONS Boolean polynomial dynamical systems provide a powerful modeling framework for the reverse engineering of gene regulatory networks, that enables a rich mathematical structure on the model search space. A C++ implementation of the method, distributed under LPGL license, is available, together with the source code, at http://www.paola-vera-licona.net/Software/EARevEng/REACT.html.
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Chai LE, Loh SK, Low ST, Mohamad MS, Deris S, Zakaria Z. A review on the computational approaches for gene regulatory network construction. Comput Biol Med 2014; 48:55-65. [PMID: 24637147 DOI: 10.1016/j.compbiomed.2014.02.011] [Citation(s) in RCA: 148] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2013] [Revised: 02/14/2014] [Accepted: 02/17/2014] [Indexed: 01/08/2023]
Abstract
Many biological research areas such as drug design require gene regulatory networks to provide clear insight and understanding of the cellular process in living cells. This is because interactions among the genes and their products play an important role in many molecular processes. A gene regulatory network can act as a blueprint for the researchers to observe the relationships among genes. Due to its importance, several computational approaches have been proposed to infer gene regulatory networks from gene expression data. In this review, six inference approaches are discussed: Boolean network, probabilistic Boolean network, ordinary differential equation, neural network, Bayesian network, and dynamic Bayesian network. These approaches are discussed in terms of introduction, methodology and recent applications of these approaches in gene regulatory network construction. These approaches are also compared in the discussion section. Furthermore, the strengths and weaknesses of these computational approaches are described.
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Affiliation(s)
- Lian En Chai
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
| | - Swee Kuan Loh
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
| | - Swee Thing Low
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
| | - Mohd Saberi Mohamad
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia.
| | - Safaai Deris
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
| | - Zalmiyah Zakaria
- Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai, 81310 Johor, Malaysia
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Inference of Vohradský's models of genetic networks by solving two-dimensional function optimization problems. PLoS One 2014; 8:e83308. [PMID: 24386175 PMCID: PMC3875442 DOI: 10.1371/journal.pone.0083308] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2013] [Accepted: 11/01/2013] [Indexed: 11/21/2022] Open
Abstract
The inference of a genetic network is a problem in which mutual interactions among genes are inferred from time-series of gene expression levels. While a number of models have been proposed to describe genetic networks, this study focuses on a mathematical model proposed by Vohradský. Because of its advantageous features, several researchers have proposed the inference methods based on Vohradský's model. When trying to analyze large-scale networks consisting of dozens of genes, however, these methods must solve high-dimensional non-linear function optimization problems. In order to resolve the difficulty of estimating the parameters of the Vohradský's model, this study proposes a new method that defines the problem as several two-dimensional function optimization problems. Through numerical experiments on artificial genetic network inference problems, we showed that, although the computation time of the proposed method is not the shortest, the method has the ability to estimate parameters of Vohradský's models more effectively with sufficiently short computation times. This study then applied the proposed method to an actual inference problem of the bacterial SOS DNA repair system, and succeeded in finding several reasonable regulations.
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Fathi A, Mozaffari A. Modeling a shape memory alloy actuator using an evolvable recursive black-box and hybrid heuristic algorithms inspired based on the annual migration of salmons in nature. Appl Soft Comput 2014. [DOI: 10.1016/j.asoc.2013.03.019] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Optimization of Laminar Free Convection in a Horizontal Cavity Consisting of Flow Diverters Using ICA. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2013. [DOI: 10.1007/s13369-013-0741-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Zhou Y, Li X, Gao L. A differential evolution algorithm with intersect mutation operator. Appl Soft Comput 2013. [DOI: 10.1016/j.asoc.2012.08.014] [Citation(s) in RCA: 80] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Sambo F, de Oca MAM, Di Camillo B, Toffolo G, Stützle T. MORE: mixed optimization for reverse engineering--an application to modeling biological networks response via sparse systems of nonlinear differential equations. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:1459-1471. [PMID: 22837424 DOI: 10.1109/tcbb.2012.56] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.
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Affiliation(s)
- Francesco Sambo
- Department of Information Engineering, University of Padova, Padova, Italy.
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Xin B, Chen J, Zhang J, Fang H, Peng ZH. Hybridizing Differential Evolution and Particle Swarm Optimization to Design Powerful Optimizers: A Review and Taxonomy. ACTA ACUST UNITED AC 2012. [DOI: 10.1109/tsmcc.2011.2160941] [Citation(s) in RCA: 124] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Rui Xu, Jie Xu, Wunsch DC. A Comparison Study of Validity Indices on Swarm-Intelligence-Based Clustering. ACTA ACUST UNITED AC 2012; 42:1243-56. [DOI: 10.1109/tsmcb.2012.2188509] [Citation(s) in RCA: 73] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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Controllability and optimal control of a temporal Boolean network. Neural Netw 2012; 34:10-7. [PMID: 22784925 DOI: 10.1016/j.neunet.2012.06.002] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2011] [Revised: 05/21/2012] [Accepted: 06/12/2012] [Indexed: 11/23/2022]
Abstract
This paper investigates the controllability and optimal control of a temporal Boolean network, where the time delays are time variant. First, using the theory of semi-tensor product of matrices, the logical systems can be converted into a discrete time variant system. Second, necessary and sufficient conditions for the controllability via two types of controls are provided respectively. Third, optimal control design algorithms are presented. Finally, examples are given to illustrate the proposed results.
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Sun J, Garibaldi JM, Hodgman C. Parameter estimation using meta-heuristics in systems biology: a comprehensive review. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012; 9:185-202. [PMID: 21464505 DOI: 10.1109/tcbb.2011.63] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
This paper gives a comprehensive review of the application of meta-heuristics to optimization problems in systems biology, mainly focussing on the parameter estimation problem (also called the inverse problem or model calibration). It is intended for either the system biologist who wishes to learn more about the various optimization techniques available and/or the meta-heuristic optimizer who is interested in applying such techniques to problems in systems biology. First, the parameter estimation problems emerging from different areas of systems biology are described from the point of view of machine learning. Brief descriptions of various meta-heuristics developed for these problems follow, along with outlines of their advantages and disadvantages. Several important issues in applying meta-heuristics to the systems biology modelling problem are addressed, including the reliability and identifiability of model parameters, optimal design of experiments, and so on. Finally, we highlight some possible future research directions in this field.
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Tang N, Zhang DJ. Application of a Load Forecasting Model Based on Improved Grey Neural Network in the Smart Grid. ACTA ACUST UNITED AC 2011. [DOI: 10.1016/j.egypro.2011.10.025] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/14/2022]
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Xu W, Zhang L, Gu X. Modeling of ammonia conversion rate in ammonia synthesis based on a hybrid algorithm and least squares support vector regression. ASIA-PAC J CHEM ENG 2010. [DOI: 10.1002/apj.517] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Abstract
Neural networks are a class of intelligent learning machines establishing the relationships between descriptors of real-world objects. As optimisation tools they are also a class of computational algorithms implemented using statistical/numerical techniques for parameter estimate, model selection, and generalisation enhancement. In bioinformatics applications, neural networks have played an important role for classification, function approximation, knowledge discovery, and data visualisation. This chapter will focus on supervised neural networks and discuss their applications to bioinformatics.
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Sîrbu A, Ruskin HJ, Crane M. Comparison of evolutionary algorithms in gene regulatory network model inference. BMC Bioinformatics 2010; 11:59. [PMID: 20105328 PMCID: PMC2831005 DOI: 10.1186/1471-2105-11-59] [Citation(s) in RCA: 57] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2009] [Accepted: 01/27/2010] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.
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Affiliation(s)
- Alina Sîrbu
- Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland
| | - Heather J Ruskin
- Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland
| | - Martin Crane
- Centre for Scientific Computing and Complex Systems Modelling, Dublin City University, Dublin 9, Ireland
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Lee WP, Tzou WS. Computational methods for discovering gene networks from expression data. Brief Bioinform 2009; 10:408-23. [PMID: 19505889 DOI: 10.1093/bib/bbp028] [Citation(s) in RCA: 87] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Designing and conducting experiments are routine practices for modern biologists. The real challenge, especially in the post-genome era, usually comes not from acquiring data, but from subsequent activities such as data processing, analysis, knowledge generation and gaining insight into the research question of interest. The approach of inferring gene regulatory networks (GRNs) has been flourishing for many years, and new methods from mathematics, information science, engineering and social sciences have been applied. We review different kinds of computational methods biologists use to infer networks of varying levels of accuracy and complexity. The primary concern of biologists is how to translate the inferred network into hypotheses that can be tested with real-life experiments. Taking the biologists' viewpoint, we scrutinized several methods for predicting GRNs in mammalian cells, and more importantly show how the power of different knowledge databases of different types can be used to identify modules and subnetworks, thereby reducing complexity and facilitating the generation of testable hypotheses.
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Affiliation(s)
- Wei-Po Lee
- Department of Information Management, National Sun Yat-sen University, Kaohsiung, Taiwan.
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Shen Q, Mei Z, Ye BX. Simultaneous genes and training samples selection by modified particle swarm optimization for gene expression data classification. Comput Biol Med 2009; 39:646-9. [DOI: 10.1016/j.compbiomed.2009.04.008] [Citation(s) in RCA: 29] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2008] [Revised: 03/13/2009] [Accepted: 04/29/2009] [Indexed: 11/30/2022]
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Nema S, Goulermas J, Sparrow G, Cook P. A Hybrid Particle Swarm Branch-and-Bound (HPB) Optimizer for Mixed Discrete Nonlinear Programming. ACTA ACUST UNITED AC 2008. [DOI: 10.1109/tsmca.2008.2003536] [Citation(s) in RCA: 27] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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