Journal articles: 'Gene network reconstruction'

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Bhola, Abhishek, and Sandeep Mittal. "Reconstruction of Gene Regulatory Network using Bayesian Network." IOP Conference Series: Materials Science and Engineering 1042, no. 1 (January 1, 2021): 012009. http://dx.doi.org/10.1088/1757-899x/1042/1/012009.

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Soinov, L. A. "Supervised classification for gene network reconstruction." Biochemical Society Transactions 31, no. 6 (December 1, 2003): 1497–502. http://dx.doi.org/10.1042/bst0311497.

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One of the central problems of functional genomics is revealing gene expression networks – the relationships between genes that reflect observations of how the expression level of each gene affects those of others. Microarray data are currently a major source of information about the interplay of biochemical network participants in living cells. Various mathematical techniques, such as differential equations, Bayesian and Boolean models and several statistical methods, have been applied to expression data in attempts to extract the underlying knowledge. Unsupervised clustering methods are often considered as the necessary first step in visualization and analysis of the expression data. As for supervised classification, the problem mainly addressed so far has been how to find discriminative genes separating various samples or experimental conditions. Numerous methods have been applied to identify genes that help to predict treatment outcome or to confirm a diagnosis, as well as to identify primary elements of gene regulatory circuits. However, less attention has been devoted to using supervised learning to uncover relationships between genes and/or their products. To start filling this gap a machine-learning approach for gene networks reconstruction is described here. This approach is based on building classifiers – functions, which determine the state of a gene's transcription machinery through expression levels of other genes. The method can be applied to various cases where relationships between gene expression levels could be expected.

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Orlov, Y. L., A. G. Galieva, N. G. Orlova, E. N. Ivanova, Y. A. Mozyleva, and A. A. Anashkina. "Reconstruction of gene network associated with Parkinson disease for gene targets search." Biomeditsinskaya Khimiya 67, no. 3 (2021): 222–30. http://dx.doi.org/10.18097/pbmc20216703222.

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Accumulation of genetic data in the field of Parkinson's disease research culminated in identifying risk factors and confident prediction of the disease occurrence. To find new gene-targets for diagnostics and therapy we have to reconstruct gene network of the disease, to cluster genes in the network, to reveal key (hub) genes with largest number of interactions in the network. Using the on-line bioinformatics tools OMIM, PANTHER, g:Profiler, GeneMANIA, and STRING-DB, we have analyzed the current array of data related to Parkinson's disease, calculated the categories of gene ontologies for a large list of genes, visualized them, and built gene networks containing the identified key objects and their relationships. However, translating the results into biological understanding is still a promising major challenge. The analysis of the genes associated with the disease, the assessment of their place in the gene network (connectivity) allows us to evaluate them as target genes for medicinal effects.

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Manshaei, Roozbeh, Pooya Sobhe Bidari, Mahdi Aliyari Shoorehdeli, Amir Feizi, Tahmineh Lohrasebi, Mohammad Ali Malboobi, Matthew Kyan, and Javad Alirezaie. "Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction." ISRN Bioinformatics 2012 (November 1, 2012): 1–16. http://dx.doi.org/10.5402/2012/419419.

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Reverse engineering of gene regulatory networks (GRNs) is the process of estimating genetic interactions of a cellular system from gene expression data. In this paper, we propose a novel hybrid systematic algorithm based on neurofuzzy network for reconstructing GRNs from observational gene expression data when only a medium-small number of measurements are available. The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. The algorithm uses neurofuzzy network to model genes effects on other genes followed by four stages of decision making to extract gene interactions. One of the main features of the proposed algorithm is that an optimal number of fuzzy rules can be easily and rapidly extracted without overparameterizing. Data analysis and simulation are conducted on microarray expression profiles of S. cerevisiae cell cycle and demonstrate that the proposed algorithm not only selects the patterns of the time series gene expression data accurately, but also provides models with better reconstruction accuracy when compared with four published algorithms: DBNs, VBEM, time delay ARACNE, and PF subjected to LASSO. The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task.

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Bansal, Bhavana, Aparajita Nanda, and Anita Sahoo. "Intelligent Framework With Controlled Behavior for Gene Regulatory Network Reconstruction." International Journal of Information Retrieval Research 12, no. 1 (January 2022): 1–17. http://dx.doi.org/10.4018/ijirr.2022010104.

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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.

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ANDRECUT, M., S. A. KAUFFMAN, and A. M. MADNI. "EVIDENCE OF SCALE-FREE TOPOLOGY IN GENE REGULATORY NETWORK OF HUMAN TISSUES." International Journal of Modern Physics C 19, no. 02 (February 2008): 283–90. http://dx.doi.org/10.1142/s0129183108012091.

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We report the reconstruction of the topology of gene regulatory network in human tissues. The results show that the connectivity of the regulatory gene network is characterized by a scale-free distribution. This result supports the hypothesis that scale-free networks may represent the common blueprint for gene regulatory networks.

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Thompson, Dawn, Aviv Regev, and Sushmita Roy. "Comparative Analysis of Gene Regulatory Networks: From Network Reconstruction to Evolution." Annual Review of Cell and Developmental Biology 31, no. 1 (November 13, 2015): 399–428. http://dx.doi.org/10.1146/annurev-cellbio-100913-012908.

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Bhyratae, Suhas A. "Reconstruction of Gene Regulatory Network for Colon Cancer Dataset." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3711–16. http://dx.doi.org/10.22214/ijraset.2022.45879.

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Abstract: Molecular networks involve interacting proteins, RNA, and DNA molecules, which underlie the major functions of living cells. DNA microarray probes how the gene expression changes to perform complex coordinated tasks in adaptation to a changing environment at a genome-wide scale. Microarray is a technology that has been widely used to probe the presence of genes in a sample of DNA or RNA. This technology helps to check the expression levels of thousands of genes together. The DNA microarray was established as a tool for the efficient collection of mRNA expression for a large number of genes. The mapping function route maps pairs of genes that present similar positive, and negative interactions and also defines how the range of each gene is going to be segmented. From all the combinations a function transforms each pair of labels into another one that classifies the type of interaction. This project addresses the challenge of reconstructing molecular networks and gene regulation from gene expression data. Reconstruction of gene regulatory networks which can also be called reverse engineering is a process of identifying gene interaction networks from the experimental microarray gene expression profiles through computation techniques. The main features involved in the computation of interaction in the filtered genes are the discretization mapping function, gene-gene mapping function, and filtering function.

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Di Filippo, Marzia, Chiara Damiani, and Dario Pescini. "GPRuler: Metabolic gene-protein-reaction rules automatic reconstruction." PLOS Computational Biology 17, no. 11 (November 8, 2021): e1009550. http://dx.doi.org/10.1371/journal.pcbi.1009550.

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Metabolic network models are increasingly being used in health care and industry. As a consequence, many tools have been released to automate their reconstruction process de novo. In order to enable gene deletion simulations and integration of gene expression data, these networks must include gene-protein-reaction (GPR) rules, which describe with a Boolean logic relationships between the gene products (e.g., enzyme isoforms or subunits) associated with the catalysis of a given reaction. Nevertheless, the reconstruction of GPRs still remains a largely manual and time consuming process. Aiming at fully automating the reconstruction process of GPRs for any organism, we propose the open-source python-based framework GPRuler. By mining text and data from 9 different biological databases, GPRuler can reconstruct GPRs starting either from just the name of the target organism or from an existing metabolic model. The performance of the developed tool is evaluated at small-scale level for a manually curated metabolic model, and at genome-scale level for three metabolic models related to Homo sapiens and Saccharomyces cerevisiae organisms. By exploiting these models as benchmarks, the proposed tool shown its ability to reproduce the original GPR rules with a high level of accuracy. In all the tested scenarios, after a manual investigation of the mismatches between the rules proposed by GPRuler and the original ones, the proposed approach revealed to be in many cases more accurate than the original models. By complementing existing tools for metabolic network reconstruction with the possibility to reconstruct GPRs quickly and with a few resources, GPRuler paves the way to the study of context-specific metabolic networks, representing the active portion of the complete network in given conditions, for organisms of industrial or biomedical interest that have not been characterized metabolically yet.

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Leday, Gwenaël G. R., Mathisca C. M. de Gunst, Gino B. Kpogbezan, Aad W. van der Vaart, Wessel N. van Wieringen, and Mark A. van de Wiel. "Gene network reconstruction using global-local shrinkage priors." Annals of Applied Statistics 11, no. 1 (March 2017): 41–68. http://dx.doi.org/10.1214/16-aoas990.

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Liang, Kuo-Ching, and Xiaodong Wang. "Gene Regulatory Network Reconstruction Using Conditional Mutual Information." EURASIP Journal on Bioinformatics and Systems Biology 2008 (2008): 1–14. http://dx.doi.org/10.1155/2008/253894.

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Zhong, Rui, Jeffrey D. Allen, Guanghua Xiao, and Yang Xie. "Ensemble-Based Network Aggregation Improves the Accuracy of Gene Network Reconstruction." PLoS ONE 9, no. 11 (November 12, 2014): e106319. http://dx.doi.org/10.1371/journal.pone.0106319.

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Dokhoyan, Anastasiya Yur'evna, Maksim Vital'evich Glushchenko, and Yuriy L'vovich Orlov. "RECONSTRUCTION OF SCHIZOPHRENIA GENE NETWORK IN SEARCH FOR TARGET GENES." Ulyanovsk Medico-biological Journal, no. 3 (September 26, 2022): 6–22. http://dx.doi.org/10.34014/2227-1848-2022-3-6-22.

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Nowadays, schizophrenia is a poorly understood disease with a variety of symptoms attributed to other malconditions, and controversial diagnosis without well-defined treatment. Target therapy implies disease gene network reconstruction, gene clustering, identification of gene ontology categories and genes with the largest number of network contacts. The aim of the study is to analyze schizophrenia-associated genes, determine their position in the gene network, establish their correlation, identify key genes related to the disease, and evaluate them as target genes for drug therapy. Materials and Methods. The authors analyzed currently relevant data on schizophrenia using such online databases as OMIM, PANTHER, DAVID, GeneMANIA, STRING-DB, and GeneCards. They calculated categories of gene ontologies for 200 genes, such as biological processes, molecular functions and cellular compartments that reflect schizophrenia impact on the transmission of neuronal impulses. The authors also visualized and built gene networks containing the identified key objects and their interaction, identified the most relevant schizophrenia genes (COMT, DISC1, HTR2A, NRXN1) and a strongly connected cluster, including such genes as BDNF, SLC6A4, HTR2A, HTR2C, CHRM1, SRC, AKT, YWHAE, DISC1, DRD2, COMT, NDEL1, NOS1, CAMK28, etc. Results. The biological interpretation of the results obtained is still a great challenge, since schizophrenia is a genetically complex disease with numerous causes and triggering events. Analysis of schizophrenia-associated genes, and identification of their position in the gene network (connectivity) makes it possible to find out their interaction, determine the key genes of the disease, and evaluate their prospects as target genes for drug therapy.

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Stark, J., D. Brewer, M. Barenco, D. Tomescu, R. Callard, and M. Hubank. "Reconstructing gene networks: what are the limits?" Biochemical Society Transactions 31, no. 6 (December 1, 2003): 1519–25. http://dx.doi.org/10.1042/bst0311519.

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To fully realize the benefits of high-throughput post-genomic technologies it is necessary to reconstruct and analyse the complicated network of interactions through which most genes operate. We briefly summarize the mathematical frameworks that can be used to model such networks, and the types of algorithms available for their reconstruction. We then focus on dynamic models, typically described using differential equations, and explain the two main reconstruction approaches in current use. We discuss the data requirements of these algorithms and ask how well they correspond to current microarray data.

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Dimitrakopoulos, Georgios N. "XGRN: Reconstruction of Biological Networks Based on Boosted Trees Regression." Computation 9, no. 4 (April 20, 2021): 48. http://dx.doi.org/10.3390/computation9040048.

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In Systems Biology, the complex relationships between different entities in the cells are modeled and analyzed using networks. Towards this aim, a rich variety of gene regulatory network (GRN) inference algorithms has been developed in recent years. However, most algorithms rely solely on gene expression data to reconstruct the network. Due to possible expression profile similarity, predictions can contain connections between biologically unrelated genes. Therefore, previously known biological information should also be considered by computational methods to obtain more consistent results, such as experimentally validated interactions between transcription factors and target genes. In this work, we propose XGBoost for gene regulatory networks (XGRN), a supervised algorithm, which combines gene expression data with previously known interactions for GRN inference. The key idea of our method is to train a regression model for each known interaction of the network and then utilize this model to predict new interactions. The regression is performed by XGBoost, a state-of-the-art algorithm using an ensemble of decision trees. In detail, XGRN learns a regression model based on gene expression of the two interactors and then provides predictions using as input the gene expression of other candidate interactors. Application on benchmark datasets and a real large single-cell RNA-Seq experiment resulted in high performance compared to other unsupervised and supervised methods, demonstrating the ability of XGRN to provide reliable predictions.

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Li, Y., and S. A. Jackson. "Gene Network Reconstruction by Integration of Prior Biological Knowledge." G3: Genes|Genomes|Genetics 5, no. 6 (March 30, 2015): 1075–79. http://dx.doi.org/10.1534/g3.115.018127.

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Böck, Matthias, Soichi Ogishima, Hiroshi Tanaka, Stefan Kramer, and Lars Kaderali. "Hub-Centered Gene Network Reconstruction Using Automatic Relevance Determination." PLoS ONE 7, no. 5 (May 3, 2012): e35077. http://dx.doi.org/10.1371/journal.pone.0035077.

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Dimitrakopoulos, Georgios N., Ioannis A. Maraziotis, Kyriakos Sgarbas, and Anastasios Bezerianos. "A Clustering based Method Accelerating Gene Regulatory Network Reconstruction." Procedia Computer Science 29 (2014): 1993–2002. http://dx.doi.org/10.1016/j.procs.2014.05.183.

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Mombaerts, Laurent, Atte Aalto, Johan Markdahl, and Jorge Gonçalves. "A multifactorial evaluation framework for gene regulatory network reconstruction." IFAC-PapersOnLine 52, no. 26 (2019): 262–68. http://dx.doi.org/10.1016/j.ifacol.2019.12.268.

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Ceci, Michelangelo, Gianvito Pio, Vladimir Kuzmanovski, and Sašo Džeroski. "Semi-Supervised Multi-View Learning for Gene Network Reconstruction." PLOS ONE 10, no. 12 (December 7, 2015): e0144031. http://dx.doi.org/10.1371/journal.pone.0144031.

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van IJzendoorn, David G. P., Kimberly Glass, John Quackenbush, and Marieke L. Kuijjer. "PyPanda: a Python package for gene regulatory network reconstruction." Bioinformatics 32, no. 21 (July 10, 2016): 3363–65. http://dx.doi.org/10.1093/bioinformatics/btw422.

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Liu, Kuan, Haiyuan Liu, Dongyan Sun, and Lei Zhang. "Network Inference from Gene Expression Data with Distance Correlation and Network Topology Centrality." Algorithms 14, no. 2 (February 15, 2021): 61. http://dx.doi.org/10.3390/a14020061.

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The reconstruction of gene regulatory networks based on gene expression data can effectively uncover regulatory relationships between genes and provide a deeper understanding of biological control processes. Non-linear dependence is a common problem in the regulatory mechanisms of gene regulatory networks. Various methods based on information theory have been developed to infer networks. However, the methods have introduced many redundant regulatory relationships in the network inference process. A recent measurement method called distance correlation has, in many cases, shown strong and computationally efficient non-linear correlations. In this paper, we propose a novel regulatory network inference method called the distance-correlation and network topology centrality network (DCNTC) method. The method is based on and extends the Local Density Measurement of Network Node Centrality (LDCNET) algorithm, which has the same choice of network centrality ranking as the LDCNET algorithm, but uses a simpler and more efficient distance correlation measure of association between genes. In this work, we integrate distance correlation and network topological centrality into the reasoning about the structure of gene regulatory networks. We will select optimal thresholds based on the characteristics of the distribution of each gene pair in relation to distance correlation. Experiments were carried out on four network datasets and their performance was compared.

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Iliopoulos, A. C., G. Beis, P. Apostolou, and I. Papasotiriou. "Complex Networks, Gene Expression and Cancer Complexity: A Brief Review of Methodology and Applications." Current Bioinformatics 15, no. 6 (November 11, 2020): 629–55. http://dx.doi.org/10.2174/1574893614666191017093504.

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In this brief survey, various aspects of cancer complexity and how this complexity can be confronted using modern complex networks’ theory and gene expression datasets, are described. In particular, the causes and the basic features of cancer complexity, as well as the challenges it brought are underlined, while the importance of gene expression data in cancer research and in reverse engineering of gene co-expression networks is highlighted. In addition, an introduction to the corresponding theoretical and mathematical framework of graph theory and complex networks is provided. The basics of network reconstruction along with the limitations of gene network inference, the enrichment and survival analysis, evolution, robustness-resilience and cascades in complex networks, are described. Finally, an indicative and suggestive example of a cancer gene co-expression network inference and analysis is given.

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Rolfsson, Óttar, Giuseppe Paglia, Manuela Magnusdóttir, Bernhard Ø. Palsson, and Ines Thiele. "Inferring the metabolism of human orphan metabolites from their metabolic network context affirms human gluconokinase activity." Biochemical Journal 449, no. 2 (December 14, 2012): 427–35. http://dx.doi.org/10.1042/bj20120980.

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Metabolic network reconstructions define metabolic information within a target organism and can therefore be used to address incomplete metabolic information. In the present study we used a computational approach to identify human metabolites whose metabolism is incomplete on the basis of their detection in humans but exclusion from the human metabolic network reconstruction RECON 1. Candidate solutions, composed of metabolic reactions capable of explaining the metabolism of these compounds, were then identified computationally from a global biochemical reaction database. Solutions were characterized with respect to how metabolites were incorporated into RECON 1 and their biological relevance. Through detailed case studies we show that biologically plausible non-intuitive hypotheses regarding the metabolism of these compounds can be proposed in a semi-automated manner, in an approach that is similar to de novo network reconstruction. We subsequently experimentally validated one of the proposed hypotheses and report that C9orf103, previously identified as a candidate tumour suppressor gene, encodes a functional human gluconokinase. The results of the present study demonstrate how semi-automatic gap filling can be used to refine and extend metabolic reconstructions, thereby increasing their biological scope. Furthermore, we illustrate how incomplete human metabolic knowledge can be coupled with gene annotation in order to prioritize and confirm gene functions.

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Cinquemani, Eugenio. "Identifiability and Reconstruction of Biochemical Reaction Networks from Population Snapshot Data." Processes 6, no. 9 (August 22, 2018): 136. http://dx.doi.org/10.3390/pr6090136.

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Inference of biochemical network models from experimental data is a crucial problem in systems and synthetic biology that includes parameter calibration but also identification of unknown interactions. Stochastic modelling from single-cell data is known to improve identifiability of reaction network parameters for specific systems. However, general results are lacking, and the advantage over deterministic, population-average approaches has not been explored for network reconstruction. In this work, we study identifiability and propose new reconstruction methods for biochemical interaction networks. Focusing on population-snapshot data and networks with reaction rates affine in the state, for parameter estimation, we derive general methods to test structural identifiability and demonstrate them in connection with practical identifiability for a reporter gene in silico case study. In the same framework, we next develop a two-step approach to the reconstruction of unknown networks of interactions. We apply it to compare the achievable network reconstruction performance in a deterministic and a stochastic setting, showing the advantage of the latter, and demonstrate it on population-snapshot data from a simulated example.

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Babichev, Sergii. "An Evaluation of the Information Technology of Gene Expression Profiles Processing Stability for Different Levels of Noise Components." Data 3, no. 4 (November 5, 2018): 48. http://dx.doi.org/10.3390/data3040048.

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This paper presents the results of research concerning the evaluation of stability of information technology of gene expression profiles processing with the use of gene expression profiles, which contain different levels of noise components. The information technology is presented as a structural block-chart, which contains all stages of the studied data processing. The hybrid model of objective clustering based on the SOTA algorithm and the technology of gene regulatory networks reconstruction have been investigated to evaluate the stability to the level of the noise components. The results of the simulation have shown that the hybrid model of the objective clustering has high level of stability to noise components and vice versa, the technology of gene regulatory networks reconstruction is rather sensitive to the level of noise component. The obtained results indicate the importance of gene expression profiles preprocessing at the early stage of the gene regulatory network reconstruction in order to remove background noise and non-informative genes in terms of the used criteria.

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Chang, C., Z. Ding, Y. S. Hung, and P. C. W. Fung. "Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data." Bioinformatics 24, no. 11 (April 9, 2008): 1349–58. http://dx.doi.org/10.1093/bioinformatics/btn131.

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D’Arcy, Cian, Olivia Bass, Philipp Junk, Thomas Sevrin, Giorgio Oliviero, Kieran Wynne, Melinda Halasz, and Christina Kiel. "Disease–Gene Networks of Skin Pigmentation Disorders and Reconstruction of Protein–Protein Interaction Networks." Bioengineering 10, no. 1 (December 21, 2022): 13. http://dx.doi.org/10.3390/bioengineering10010013.

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Melanin, a light and free radical absorbing pigment, is produced in melanocyte cells that are found in skin, but also in hair follicles, eyes, the inner ear, heart, brain and other organs. Melanin synthesis is the result of a complex network of signaling and metabolic reactions. It therefore comes as no surprise that mutations in many of the genes involved are associated with various types of pigmentation diseases and phenotypes (‘pigmentation genes’). Here, we used bioinformatics tools to first reconstruct gene-disease/phenotype associations for all pigmentation genes. Next, we reconstructed protein–protein interaction (PPI) networks centered around pigmentation gene products (‘pigmentation proteins’) and supplemented the PPI networks with protein expression information obtained by mass spectrometry in a panel of melanoma cell lines (both pigment producing and non-pigment producing cells). The analysis provides a systems network representation of all genes/ proteins centered around pigmentation and melanin biosynthesis pathways (‘pigmentation network map’). Our work will enable the pigmentation research community to experimentally test new hypothesis arising from the pigmentation network map and to identify new targets for drug discovery.

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Raza, Khalid. "Reconstruction, Topological and Gene Ontology Enrichment Analysis of Cancerous Gene Regulatory Network Modules." Current Bioinformatics 11, no. 2 (April 1, 2016): 243–58. http://dx.doi.org/10.2174/1574893611666160115212806.

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You, Na, Peng Mou, Ting Qiu, Qiang Kou, Huaijin Zhu, Yuexi Chen, and Xueqin Wang. "Gene Expression Network Reconstruction by LEP Method Using Microarray Data." Scientific World Journal 2012 (2012): 1–6. http://dx.doi.org/10.1100/2012/753430.

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Gene expression network reconstruction using microarray data is widely studied aiming to investigate the behavior of a gene cluster simultaneously. Under the Gaussian assumption, the conditional dependence between genes in the network is fully described by the partial correlation coefficient matrix. Due to the high dimensionality and sparsity, we utilize the LEP method to estimate it in this paper. Compared to the existing methods, the LEP reaches the highest PPV with the sensitivity controlled at the satisfactory level. A set of gene expression data from the HapMap project is analyzed for illustration.

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Pio, Gianvito, Michelangelo Ceci, Francesca Prisciandaro, and Donato Malerba. "Exploiting causality in gene network reconstruction based on graph embedding." Machine Learning 109, no. 6 (December 3, 2019): 1231–79. http://dx.doi.org/10.1007/s10994-019-05861-8.

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Liu, Lili, Qian Mei, Zhenning Yu, Tianhao Sun, Zijun Zhang, and Ming Chen. "An Integrative Bioinformatics Framework for Genome-scale Multiple Level Network Reconstruction of Rice." Journal of Integrative Bioinformatics 10, no. 2 (June 1, 2013): 94–102. http://dx.doi.org/10.1515/jib-2013-223.

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Summary Understanding how metabolic reactions translate the genome of an organism into its phenotype is a grand challenge in biology. Genome-wide association studies (GWAS) statistically connect genotypes to phenotypes, without any recourse to known molecular interactions, whereas a molecular mechanistic description ties gene function to phenotype through gene regulatory networks (GRNs), protein-protein interactions (PPIs) and molecular pathways. Integration of different regulatory information levels of an organism is expected to provide a good way for mapping genotypes to phenotypes. However, the lack of curated metabolic model of rice is blocking the exploration of genome-scale multi-level network reconstruction. Here, we have merged GRNs, PPIs and genome-scale metabolic networks (GSMNs) approaches into a single framework for rice via omics’ regulatory information reconstruction and integration. Firstly, we reconstructed a genome-scale metabolic model, containing 4,462 function genes, 2,986 metabolites involved in 3,316 reactions, and compartmentalized into ten subcellular locations. Furthermore, 90,358 pairs of protein-protein interactions, 662,936 pairs of gene regulations and 1,763 microRNA-target interactions were integrated into the metabolic model. Eventually, a database was developped for systematically storing and retrieving the genome-scale multi-level network of rice. This provides a reference for understanding genotype-phenotype relationship of rice, and for analysis of its molecular regulatory network.

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Liu, Gui-xia, Wei Feng, Han Wang, Lei Liu, and Chun-guang Zhou. "Reconstruction of Gene Regulatory Networks Based on Two-Stage Bayesian Network Structure Learning Algorithm." Journal of Bionic Engineering 6, no. 1 (March 2009): 86–92. http://dx.doi.org/10.1016/s1672-6529(08)60103-1.

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Yeung, Enoch, Jongmin Kim, Ye Yuan, Jorge Gonçalves, and Richard M. Murray. "Data-driven network models for genetic circuits from time-series data with incomplete measurements." Journal of The Royal Society Interface 18, no. 182 (September 2021): 20210413. http://dx.doi.org/10.1098/rsif.2021.0413.

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Synthetic gene networks are frequently conceptualized and visualized as static graphs. This view of biological programming stands in stark contrast to the transient nature of biomolecular interaction, which is frequently enacted by labile molecules that are often unmeasured. Thus, the network topology and dynamics of synthetic gene networks can be difficult to verify in vivo or in vitro , due to the presence of unmeasured biological states. Here we introduce the dynamical structure function as a new mesoscopic, data-driven class of models to describe gene networks with incomplete measurements of state dynamics. We develop a network reconstruction algorithm and a code base for reconstructing the dynamical structure function from data, to enable discovery and visualization of graphical relationships in a genetic circuit diagram as time-dependent functions rather than static, unknown weights. We prove a theorem, showing that dynamical structure functions can provide a data-driven estimate of the size of crosstalk fluctuations from an idealized model. We illustrate this idea with numerical examples. Finally, we show how data-driven estimation of dynamical structure functions can explain failure modes in two experimentally implemented genetic circuits, a previously reported in vitro genetic circuit and a new E. coli -based transcriptional event detector.

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Tu, Jia-Juan, Le Ou-Yang, Hong Yan, Xiao-Fei Zhang, and Hong Qin. "Joint reconstruction of multiple gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity." Bioinformatics 36, no. 9 (January 23, 2020): 2755–62. http://dx.doi.org/10.1093/bioinformatics/btaa014.

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Abstract Motivation Reconstruction of cancer gene networks from gene expression data is important for understanding the mechanisms underlying human cancer. Due to heterogeneity, the tumor tissue samples for a single cancer type can be divided into multiple distinct subtypes (inter-tumor heterogeneity) and are composed of non-cancerous and cancerous cells (intra-tumor heterogeneity). If tumor heterogeneity is ignored when inferring gene networks, the edges specific to individual cancer subtypes and cell types cannot be characterized. However, most existing network reconstruction methods do not simultaneously take inter-tumor and intra-tumor heterogeneity into account. Results In this article, we propose a new Gaussian graphical model-based method for jointly estimating multiple cancer gene networks by simultaneously capturing inter-tumor and intra-tumor heterogeneity. Given gene expression data of heterogeneous samples for different cancer subtypes, a non-cancerous network shared across different cancer subtypes and multiple subtype-specific cancerous networks are estimated jointly. Tumor heterogeneity can be revealed by the difference in the estimated networks. The performance of our method is first evaluated using simulated data, and the results indicate that our method outperforms other state-of-the-art methods. We also apply our method to The Cancer Genome Atlas breast cancer data to reconstruct non-cancerous and subtype-specific cancerous gene networks. Hub nodes in the networks estimated by our method perform important biological functions associated with breast cancer development and subtype classification. Availability and implementation The source code is available at https://github.com/Zhangxf-ccnu/NETI2. Supplementary information Supplementary data are available at Bioinformatics online.

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Joshi, Anagha, Yvonne Beck, and Tom Michoel. "Multi-Species Network Inference Improves Gene Regulatory Network Reconstruction for Early Embryonic Development inDrosophila." Journal of Computational Biology 22, no. 4 (April 2015): 253–65. http://dx.doi.org/10.1089/cmb.2014.0290.

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Meisig, Johannes, and Nils Blüthgen. "The gene regulatory network of mESC differentiation: a benchmark for reverse engineering methods." Philosophical Transactions of the Royal Society B: Biological Sciences 373, no. 1750 (May 21, 2018): 20170222. http://dx.doi.org/10.1098/rstb.2017.0222.

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A large body of data have accumulated that characterize the gene regulatory network of stem cells. Yet, a comprehensive and integrative understanding of this complex network is lacking. Network reverse engineering methods that use transcriptome data to derive these networks may help to uncover the topology in an unbiased way. Many methods exist that use co-expression to reconstruct networks. However, it remains unclear how these methods perform in the context of stem cell differentiation, as most systematic assessments have been made for regulatory networks of unicellular organisms. Here, we report a systematic benchmark of different reverse engineering methods against functional data. We show that network pruning is critical for reconstruction performance. We also find that performance is similar for algorithms that use different co-expression measures, i.e. mutual information or correlation. In addition, different methods yield very different network topologies, highlighting the challenge of interpreting these resulting networks as a whole. This article is part of the theme issue ‘Designer human tissue: coming to a lab near you’.

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Patumcharoenpol, Preecha, Narumol Doungpan, Asawin Meechai, Bairong Shen, Jonathan H. Chan, and Wanwipa Vongsangnak. "An integrated text mining framework for metabolic interaction network reconstruction." PeerJ 4 (March 21, 2016): e1811. http://dx.doi.org/10.7717/peerj.1811.

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Text mining (TM) in the field of biology is fast becoming a routine analysis for the extraction and curation of biological entities (e.g., genes, proteins, simple chemicals) as well as their relationships. Due to the wide applicability of TM in situations involving complex relationships, it is valuable to apply TM to the extraction of metabolic interactions (i.e., enzyme and metabolite interactions) through metabolic events. Here we present an integrated TM framework containing two modules for the extraction of metabolic events (Metabolic Event Extraction module—MEE) and for the construction of a metabolic interaction network (Metabolic Interaction Network Reconstruction module—MINR). The proposed integrated TM framework performed well based on standard measures of recall, precision and F-score. Evaluation of the MEE module using the constructed Metabolic Entities (ME) corpus yielded F-scores of 59.15% and 48.59% for the detection of metabolic events for production and consumption, respectively. As for the testing of the entity tagger for Gene and Protein (GP) and metabolite with the test corpus, the obtained F-score was greater than 80% for the Superpathway of leucine, valine, and isoleucine biosynthesis. Mapping of enzyme and metabolite interactions through network reconstruction showed a fair performance for the MINR module on the test corpus with F-score >70%. Finally, an application of our integrated TM framework on a big-scale data (i.e., EcoCyc extraction data) for reconstructing a metabolic interaction network showed reasonable precisions at 69.93%, 70.63% and 46.71% for enzyme, metabolite and enzyme–metabolite interaction, respectively. This study presents the first open-source integrated TM framework for reconstructing a metabolic interaction network. This framework can be a powerful tool that helps biologists to extract metabolic events for further reconstruction of a metabolic interaction network. The ME corpus, test corpus, source code, and virtual machine image with pre-configured software are available atwww.sbi.kmutt.ac.th/ preecha/metrecon.

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Saik, Olga V., and Vadim V. Klimontov. "Bioinformatic Reconstruction and Analysis of Gene Networks Related to Glucose Variability in Diabetes and Its Complications." International Journal of Molecular Sciences 21, no. 22 (November 18, 2020): 8691. http://dx.doi.org/10.3390/ijms21228691.

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Glucose variability (GV) has been recognized recently as a promoter of complications and therapeutic targets in diabetes. The aim of this study was to reconstruct and analyze gene networks related to GV in diabetes and its complications. For network analysis, we used the ANDSystem that provides automatic network reconstruction and analysis based on text mining. The network of GV consisted of 37 genes/proteins associated with both hyperglycemia and hypoglycemia. Cardiovascular system, pancreas, adipose and muscle tissues, gastrointestinal tract, and kidney were recognized as the loci with the highest expression of GV-related genes. According to Gene Ontology enrichment analysis, these genes are associated with insulin secretion, glucose metabolism, glycogen biosynthesis, gluconeogenesis, MAPK and JAK-STAT cascades, protein kinase B signaling, cell proliferation, nitric oxide biosynthesis, etc. GV-related genes were found to occupy central positions in the networks of diabetes complications (cardiovascular disease, diabetic nephropathy, retinopathy, and neuropathy) and were associated with response to hypoxia. Gene prioritization analysis identified new gene candidates (THBS1, FN1, HSP90AA1, EGFR, MAPK1, STAT3, TP53, EGF, GSK3B, and PTEN) potentially involved in GV. The results expand the understanding of the molecular mechanisms of the GV phenomenon in diabetes and provide molecular markers and therapeutic targets for future research.

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Peng, Chien-Hua, Yi-Zhi Jiang, An-Shun Tai, Chun-Bin Liu, Shih-Chi Peng, Chun-Ta Liao, Tzu-Chen Yen, and Wen-Ping Hsieh. "Causal inference of gene regulation with subnetwork assembly from genetical genomics data." Nucleic Acids Research 42, no. 5 (December 9, 2013): 2803–19. http://dx.doi.org/10.1093/nar/gkt1277.

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Abstract Deciphering the causal networks of gene interactions is critical for identifying disease pathways and disease-causing genes. We introduce a method to reconstruct causal networks based on exploring phenotype-specific modules in the human interactome and including the expression quantitative trait loci (eQTLs) that underlie the joint expression variation of each module. Closely associated eQTLs help anchor the orientation of the network. To overcome the inherent computational complexity of causal network reconstruction, we first deduce the local causality of individual subnetworks using the selected eQTLs and module transcripts. These subnetworks are then integrated to infer a global causal network using a random-field ranking method, which was motivated by animal sociology. We demonstrate how effectively the inferred causality restores the regulatory structure of the networks that mediate lymph node metastasis in oral cancer. Network rewiring clearly characterizes the dynamic regulatory systems of distinct disease states. This study is the first to associate an RXRB-causal network with increased risks of nodal metastasis, tumor relapse, distant metastases and poor survival for oral cancer. Thus, identifying crucial upstream drivers of a signal cascade can facilitate the discovery of potential biomarkers and effective therapeutic targets.

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Liu, Qi, Louis J. Muglia, and Lei Frank Huang. "Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction." Genes 10, no. 8 (August 9, 2019): 602. http://dx.doi.org/10.3390/genes10080602.

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With the advances in different biological networks including gene regulation, gene co-expression, protein–protein interaction networks, and advanced approaches for network reconstruction, analysis, and interpretation, it is possible to discover reliable and accurate molecular network-based biomarkers for monitoring cancer treatment. Such efforts will also pave the way toward the realization of biomarker-driven personalized medicine against cancer. Previously, we have reconstructed disease-specific driver signaling networks using multi-omics profiles and cancer signaling pathway data. In this study, we developed a network-based sparse Bayesian machine (NBSBM) approach, using previously derived disease-specific driver signaling networks to predict cancer cell responses to drugs. NBSBM made use of the information encoded in a disease-specific (differentially expressed) network to improve its prediction performance in problems with a reduced amount of training data and a very high-dimensional feature space. Sparsity in NBSBM is favored by a spike and slab prior distribution, which is combined with a Markov random field prior that encodes the network of feature dependencies. Gene features that are connected in the network are assumed to be both relevant and irrelevant to drug responses. We compared the proposed method with network-based support vector machine (NBSVM) approaches and found that the NBSBM approach could achieve much better accuracy than the other two NBSVM methods. The gene modules selected from the disease-specific driver networks for predicting drug sensitivity might be directly involved in drug sensitivity or resistance. This work provides a disease-specific network-based drug sensitivity prediction approach and can uncover the potential mechanisms of the action of drugs by selecting the most predictive sub-networks from the disease-specific network.

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Yang, Yunfeng, Daniel P. Harris, Feng Luo, Wenlu Xiong, Marcin Joachimiak, Liyou Wu, Paramvir Dehal, et al. "Snapshot of iron response in Shewanella oneidensis by gene network reconstruction." BMC Genomics 10, no. 1 (2009): 131. http://dx.doi.org/10.1186/1471-2164-10-131.

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Dimitrova, Elena S., Brandilyn Stigler, Abdul Salam Jarrah, and Reinhard Laubenbacher. "Applications of the gröbner fan to gene network reconstruction (abstract only)." ACM Communications in Computer Algebra 42, no. 1-2 (July 25, 2008): 69. http://dx.doi.org/10.1145/1394042.1394079.

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Wang, Y. X. Rachel, and Haiyan Huang. "Review on statistical methods for gene network reconstruction using expression data." Journal of Theoretical Biology 362 (December 2014): 53–61. http://dx.doi.org/10.1016/j.jtbi.2014.03.040.

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Williams, Ruth M., Ivan Candido-Ferreira, Emmanouela Repapi, Daria Gavriouchkina, Upeka Senanayake, Irving T. C. Ling, Jelena Telenius, Stephen Taylor, Jim Hughes, and Tatjana Sauka-Spengler. "Reconstruction of the Global Neural Crest Gene Regulatory Network In Vivo." Developmental Cell 51, no. 2 (October 2019): 255–76. http://dx.doi.org/10.1016/j.devcel.2019.10.003.

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Qiu, Peng, Andrew J. Gentles, and Sylvia K. Plevritis. "Fast calculation of pairwise mutual information for gene regulatory network reconstruction." Computer Methods and Programs in Biomedicine 94, no. 2 (May 2009): 177–80. http://dx.doi.org/10.1016/j.cmpb.2008.11.003.

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Kong, Wei, Xiaoyang Mou, Xing Zhi, Xin Zhang, and Yang Yang. "Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques." Computational and Mathematical Methods in Medicine 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/891761.

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Alzheimer’s disease (AD) is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF) activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA) algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA), which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

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WERHLI, ADRIANO V., and DIRK HUSMEIER. "GENE REGULATORY NETWORK RECONSTRUCTION BY BAYESIAN INTEGRATION OF PRIOR KNOWLEDGE AND/OR DIFFERENT EXPERIMENTAL CONDITIONS." Journal of Bioinformatics and Computational Biology 06, no. 03 (June 2008): 543–72. http://dx.doi.org/10.1142/s0219720008003539.

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There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al.11 where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested a Markov chain Monte Carlo (MCMC) scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior knowledge and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets, which were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. The proposed coupling scheme is a compromise between (1) learning networks from the different subsets separately, whereby no information between the different experiments is shared; and (2) learning networks from a monolithic fusion of the individual data sets, which does not provide any mechanism for uncovering differences between the network structures associated with the different experimental conditions. We have assessed the viability of all proposed methods on data related to the Raf signaling pathway, generated both synthetically and in cytometry experiments.

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Dimitrakopoulou, Konstantina, Charalampos Tsimpouris, George Papadopoulos, Claudia Pommerenke, Esther Wilk, Kyriakos N. Sgarbas, Klaus Schughart, and Anastasios Bezerianos. "Dynamic gene network reconstruction from gene expression data in mice after influenza A (H1N1) infection." Journal of Clinical Bioinformatics 1, no. 1 (2011): 27. http://dx.doi.org/10.1186/2043-9113-1-27.

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Zhang, Jiayao, Chunling Hu, and Qianqian Zhang. "Constructing a Gene Regulatory Network Based on a Nonhomogeneous Dynamic Bayesian Network." Electronics 11, no. 18 (September 16, 2022): 2936. http://dx.doi.org/10.3390/electronics11182936.

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Since the regulatory relationship between genes is usually non-stationary, the homogeneity assumption cannot be satisfied when modeling with dynamic Bayesian networks (DBNs). For this reason, the homogeneity assumption in dynamic Bayesian networks should be relaxed. Various methods of combining multiple changepoint processes and DBNs have been proposed to relax the homogeneity assumption. When using a non-homogeneous dynamic Bayesian network to model a gene regulatory network, it is inevitable to infer the changepoints of the gene data. Based on this analysis, this paper first proposes a data-based birth move (ED-birth move). The ED-birth move makes full use of the potential information of data to infer the changepoints. The greater the Euclidean distance of the mean of the data in the two components, the more likely this data point will be selected as a new changepoint by the ED-birth move. In brief, the selection of the changepoint is proportional to the Euclidean distance of the mean on both sides of the data. Furthermore, an improved Markov chain Monte Carlo (MCMC) method is proposed, and the improved MCMC introduces the Pearson correlation coefficient (PCCs) to sample the parent node-set. The larger the absolute value of the Pearson correlation coefficient between two data points, the easier it is to be sampled. Compared with other classical models on Saccharomyces cerevisiae data, synthetic data, RAF pathway data, and Arabidopsis data, the PCCs-ED-DBN proposed in this paper improves the accuracy of gene network reconstruction and further improves the convergence and stability of the modeling process.

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Journal articles on the topic 'Gene network reconstruction'

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