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Journal articles on the topic 'Gene regulatory networks – Research'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

FUJITA, ANDRÉ, JOÃO RICARDO SATO, HUMBERTO MIGUEL GARAY-MALPARTIDA, MARI CLEIDE SOGAYAR, CARLOS EDUARDO FERREIRA, and SATORU MIYANO. "MODELING NONLINEAR GENE REGULATORY NETWORKS FROM TIME SERIES GENE EXPRESSION DATA." Journal of Bioinformatics and Computational Biology 06, no. 05 (2008): 961–79. http://dx.doi.org/10.1142/s0219720008003746.

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In cells, molecular networks such as gene regulatory networks are the basis of biological complexity. Therefore, gene regulatory networks have become the core of research in systems biology. Understanding the processes underlying the several extracellular regulators, signal transduction, protein–protein interactions, and differential gene expression processes requires detailed molecular description of the protein and gene networks involved. To understand better these complex molecular networks and to infer new regulatory associations, we propose a statistical method based on vector autoregress

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Cussat-Blanc, Sylvain, Kyle Harrington, and Wolfgang Banzhaf. "Artificial Gene Regulatory Networks—A Review." Artificial Life 24, no. 4 (2019): 296–328. http://dx.doi.org/10.1162/artl_a_00267.

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In nature, gene regulatory networks are a key mediator between the information stored in the DNA of living organisms (their genotype) and the structural and behavioral expression this finds in their bodies, surviving in the world (their phenotype). They integrate environmental signals, steer development, buffer stochasticity, and allow evolution to proceed. In engineering, modeling and implementations of artificial gene regulatory networks have been an expanding field of research and development over the past few decades. This review discusses the concept of gene regulation, describes the curr

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Phuc Le, Phillip, Joshua R. Friedman, Jonathan Schug, et al. "Glucocorticoid Receptor-Dependent Gene Regulatory Networks." PLoS Genetics 1, no. 2 (2005): e16. http://dx.doi.org/10.1371/journal.pgen.0010016.

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Buescher, M., J. Chin, Y. Chen, and S. Cohen. "P102. Gene-regulatory networks in trichome morphogenesis." Differentiation 80 (November 2010): S51. http://dx.doi.org/10.1016/j.diff.2010.09.108.

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Yu, Min-Cheng, Ji-Xiang Liu, Xiao-Lu Ma, et al. "Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds." Future Oncology 15, no. 34 (2019): 3917–34. http://dx.doi.org/10.2217/fon-2019-0275.

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Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hs

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Sun, Lidan, Libo Jiang, Christa N. Grant, et al. "Computational Identification of Gene Networks as a Biomarker of Neuroblastoma Risk." Cancers 12, no. 8 (2020): 2086. http://dx.doi.org/10.3390/cancers12082086.

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Neuroblastoma is a common cancer in children, affected by a number of genes that interact with each other through intricate but coordinated networks. Traditional approaches can only reconstruct a single regulatory network that is topologically not informative enough to explain the complexity of neuroblastoma risk. We implemented and modified an advanced model for recovering informative, omnidirectional, dynamic, and personalized networks (idopNetworks) from static gene expression data for neuroblastoma risk. We analyzed 3439 immune genes of neuroblastoma for 217 high-risk patients and 30 low-r

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Quan, Yuan, Hong-Yu Zhang, Jiang-Hui Xiong, Rui-Feng Xu, and Min Gao. "Heat Diffusion Kernel Algorithm-Based Interpretation of the Disease Intervention Mechanism for DHA." Genes 11, no. 7 (2020): 754. http://dx.doi.org/10.3390/genes11070754.

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Docosahexaenoic acid (DHA) is effective in the prevention and treatment of cancer, congenital disorders, and various chronic diseases. According to the omnigenic hypothesis, these complex diseases are caused by disordered gene regulatory networks comprising dozens to hundreds of core genes and a mass of peripheral genes. However, conventional research on the disease intervention mechanism of DHA only focused on specific types of genes or pathways instead of examining genes at the network level, resulting in conflicting conclusions. In this study, we used HotNet2, a heat diffusion kernel algori

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Wang, Li Qin, Gui Qiang Chen, and Hong Hai Zhao. "Construction of Regulatory Boolean Networks Based on Expression Profiles Data Noise." Advanced Materials Research 588-589 (November 2012): 2046–50. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.2046.

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After while the “Human Genome Project” proposes, the people complete measures the foreword plan after the multi-gene genome team, also starts to change to these genes and their reciprocity function understanding research. Varieties of gene regulation Boolean networks algorithms have been proposed of the gene expression profiles, however, the problem of noise could always be found in creating a Boolean network. Due to gene expression data are always noisy. In this paper, it show that after the Boolean networks logic function are learned from noisy data, some noise in the Boolean function could

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WANG, Zheng-Hua. "Research on modular organization of gene regulatory network." Hereditas (Beijing) 30, no. 1 (2008): 20–27. http://dx.doi.org/10.3724/sp.j.1005.2008.00020.

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Gebert, J., N. Radde, and G. W. Weber. "Modeling gene regulatory networks with piecewise linear differential equations." European Journal of Operational Research 181, no. 3 (2007): 1148–65. http://dx.doi.org/10.1016/j.ejor.2005.11.044.

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Li, Luolan, Cecile L. Maire, Misha Bilenky, et al. "Epigenomic programming in early fetal brain development." Epigenomics 12, no. 12 (2020): 1053–70. http://dx.doi.org/10.2217/epi-2019-0319.

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Aim: To provide a comprehensive understanding of gene regulatory networks in the developing human brain and a foundation for interpreting pathogenic deregulation. Materials & methods: We generated reference epigenomes and transcriptomes of dissected brain regions and primary neural progenitor cells (NPCs) derived from cortical and ganglionic eminence tissues of four normal human fetuses. Results: Integration of these data across developmental stages revealed a directional increase in active regulatory states, transcription factor activities and gene transcription with developmental stage.

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Fang, Zhen Cheng. "The Simulation Gene Regulatory Boolean Network Based on the Sequential Circuit." Applied Mechanics and Materials 686 (October 2014): 463–69. http://dx.doi.org/10.4028/www.scientific.net/amm.686.463.

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Along with the completion of HGP (human genome project), huge amounts of genetic data constantly emerge. Research suggests that genes are not in independent existence and the expression of a gene will promote or inhibit the expression of another gene; if the expression of a gene makes the biochemical environment of cells changed, the expression of a series of genes will be affected. In order to get a better understanding of the relationship between genes, all sorts of gene regulatory network models have been established by scientists. In this paper, a variety of gene regulatory networks are fi

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Soond, Surinder M., Maria V. Kozhevnikova, Paul A. Townsend, and Andrey A. Zamyatnin. "Integrative p53, micro-RNA and Cathepsin Protease Co-Regulatory Expression Networks in Cancer." Cancers 12, no. 11 (2020): 3454. http://dx.doi.org/10.3390/cancers12113454.

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As the direct regulatory role of p53 and some of its isoform proteins are becoming established in modulating gene expression in cancer research, another aspect of this mode of gene regulation that has captured significant interest over the years is the mechanistic interplay between p53 and micro-RNA transcriptional regulation. The input of this into modulating gene expression for some of the cathepsin family members has been viewed as carrying noticeable importance based on their biological effects during normal cellular homeostasis and cancer progression. While this area is still in its infan

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Babu, M. Madan. "Structure, evolution and dynamics of transcriptional regulatory networks." Biochemical Society Transactions 38, no. 5 (2010): 1155–78. http://dx.doi.org/10.1042/bst0381155.

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The availability of entire genome sequences and the wealth of literature on gene regulation have enabled researchers to model an organism's transcriptional regulation system in the form of a network. In such a network, TFs (transcription factors) and TGs (target genes) are represented as nodes and regulatory interactions between TFs and TGs are represented as directed links. In the present review, I address the following topics pertaining to transcriptional regulatory networks. (i) Structure and organization: first, I introduce the concept of networks and discuss our understanding of the struc

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Fiscon, Giulia, Federica Conte, Lorenzo Farina, and Paola Paci. "Network-Based Approaches to Explore Complex Biological Systems towards Network Medicine." Genes 9, no. 9 (2018): 437. http://dx.doi.org/10.3390/genes9090437.

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Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic anal

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Emmert-Streib, Frank, and Matthias Dehmer. "Inference of Genome-Scale Gene Regulatory Networks: Are There Differences in Biological and Clinical Validations?" Machine Learning and Knowledge Extraction 1, no. 1 (2018): 138–48. http://dx.doi.org/10.3390/make1010008.

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Causal networks, e.g., gene regulatory networks (GRNs) inferred from gene expression data, contain a wealth of information but are defying simple, straightforward and low-budget experimental validations. In this paper, we elaborate on this problem and discuss distinctions between biological and clinical validations. As a result, validation differences for GRNs reflect known differences between basic biological and clinical research questions making the validations context specific. Hence, the meaning of biologically and clinically meaningful GRNs can be very different. For a concerted approach

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Li, Peng, Maozu Guo, and Bo Sun. "Integration of multi-omics data to mine cancer-related gene modules." Journal of Bioinformatics and Computational Biology 17, no. 06 (2019): 1950038. http://dx.doi.org/10.1142/s0219720019500380.

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The identification of cancer-related genes is a major research goal, with implications for determining the pathogenesis of cancer and identifying biomarkers for early diagnosis and treatment. In this study, by integrating multi-omics data, including gene expression, DNA copy number variation, DNA methylation, transcription factors, miRNA, and lncRNA data, we propose a method for mining cancer-related genes based on network models. First, using random forest-based feature selection method multi-omics data are integrated to identify key regulatory factors that affect gene expression, and then ge

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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 (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 o

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Chatrabgoun, O., A. Hosseinian-Far, and A. Daneshkhah. "Constructing gene regulatory networks from microarray data using non-Gaussian pair-copula Bayesian networks." Journal of Bioinformatics and Computational Biology 18, no. 04 (2020): 2050023. http://dx.doi.org/10.1142/s0219720020500237.

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Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality, multi-modality and heavy tailedness that are commonly seen in current massive genetic data. To relax this limitative constraint, one can apply copula function which is a multivariate cumulative

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CHING, WAI-KI, MICHAEL M. NG, ERIC S. FUNG, and TATSUYA AKUTSU. "ON CONSTRUCTION OF STOCHASTIC GENETIC NETWORKS BASED ON GENE EXPRESSION SEQUENCES." International Journal of Neural Systems 15, no. 04 (2005): 297–310. http://dx.doi.org/10.1142/s0129065705000256.

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Reconstruction of genetic regulatory networks from time series data of gene expression patterns is an important research topic in bioinformatics. Probabilistic Boolean Networks (PBNs) have been proposed as an effective model for gene regulatory networks. PBNs are able to cope with uncertainty, corporate rule-based dependencies between genes and discover the sensitivity of genes in their interactions with other genes. However, PBNs are unlikely to use directly in practice because of huge amount of computational cost for obtaining predictors and their corresponding probabilities. In this paper,

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He, Qi-en, Yi-fan Tong, Zhou Ye, et al. "A multiple genomic data fused SF2 prediction model, signature identification, and gene regulatory network inference for personalized radiotherapy." Technology in Cancer Research & Treatment 19 (January 1, 2020): 153303382090911. http://dx.doi.org/10.1177/1533033820909112.

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Radiotherapy is one of the most important cancer treatments, but its response varies greatly among individual patients. Therefore, the prediction of radiosensitivity, identification of potential signature genes, and inference of their regulatory networks are important for clinical and oncological reasons. Here, we proposed a novel multiple genomic fused partial least squares deep regression method to simultaneously analyze multi-genomic data. Using 60 National Cancer Institute cell lines as examples, we aimed to identify signature genes by optimizing the radiosensitivity prediction model and u

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Li, Zhi, Tianyue Zhang, Haojie Lei, et al. "Research on Gastric Cancer’s Drug-resistant Gene Regulatory Network Model." Current Bioinformatics 15, no. 3 (2020): 225–34. http://dx.doi.org/10.2174/1574893614666190722102557.

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Objective: Based on bioinformatics, differentially expressed gene data of drug-resistance in gastric cancer were analyzed, screened and mined through modeling and network modeling to find valuable data associated with multi-drug resistance of gastric cancer. Methods: First, data sets were preprocessed from three aspects: data processing, data annotation and classification, and functional clustering. Secondly, based on the preprocessed data, each classified primary gene regulatory network was constructed by mining interactions among the genes. This paper computed the values of each node in each

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Mihai, Ionut Sebastian, Debojyoti Das, Gabija Maršalkaite, and Johan Henriksson. "Meta-Analysis of Gene Popularity: Less Than Half of Gene Citations Stem from Gene Regulatory Networks." Genes 12, no. 2 (2021): 319. http://dx.doi.org/10.3390/genes12020319.

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The reasons for selecting a gene for further study might vary from historical momentum to funding availability, thus leading to unequal attention distribution among all genes. However, certain biological features tend to be overlooked in evaluating a gene’s popularity. Here we present a meta-analysis of the reasons why different genes have been studied and to what extent, with a focus on the gene-specific biological features. From unbiased datasets we can define biological properties of genes that reasonably may affect their perceived importance. We make use of both linear and nonlinear comput

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Fritah, Sabrina, Arnaud Muller, Wei Jiang, et al. "Temozolomide-Induced RNA Interactome Uncovers Novel LncRNA Regulatory Loops in Glioblastoma." Cancers 12, no. 9 (2020): 2583. http://dx.doi.org/10.3390/cancers12092583.

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Resistance to chemotherapy by temozolomide (TMZ) is a major cause of glioblastoma (GBM) recurrence. So far, attempts to characterize factors that contribute to TMZ sensitivity have largely focused on protein-coding genes, and failed to provide effective therapeutic targets. Long noncoding RNAs (lncRNAs) are essential regulators of epigenetic-driven cell diversification, yet, their contribution to the transcriptional response to drugs is less understood. Here, we performed RNA-seq and small RNA-seq to provide a comprehensive map of transcriptome regulation upon TMZ in patient-derived GBM stem-l

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Le, Son, Bo Ma, Sergei Zolotukhin, Darin Falk, and David Tran. "STEM-18. DEVELOPMENT OF A NOVEL GENE THERAPY APPROACH TARGETING GLIOBLASTOMA FOLLOWING AI-DIRECTED IDENTIFICATION OF A MASTER REGULATORY GENE NETWORK." Neuro-Oncology 22, Supplement_2 (2020): ii200. http://dx.doi.org/10.1093/neuonc/noaa215.835.

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Abstract BACKGROUND Profound heterogeneity has severely hampered therapeutic advancements in glioblastoma (GBM). Remarkably, GBM exhibits broad clinical and histopathologic overlaps suggesting the presence of a common regulatory state. The GBM state embodies restructuring of the master regulatory gene network (MRGN) forced by founding mutations and perpetuated in subclones of GBM stem-like cells (GSC). Successful targeting and altering of the MRGN promise to circumvent the heterogeneity. METHODS To decipher the common MRGN in GSC, we applied a robust AI suite, NETZEN, that integrates deep neur

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Alexiou, Athanasios, Stylianos Chatzichronis, Asma Perveen, Abdul Hafeez, and Ghulam Md Ashraf. "Algorithmic and Stochastic Representations of Gene Regulatory Networks and Protein-Protein Interactions." Current Topics in Medicinal Chemistry 19, no. 6 (2019): 413–25. http://dx.doi.org/10.2174/1568026619666190311125256.

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Background:Latest studies reveal the importance of Protein-Protein interactions on physiologic functions and biological structures. Several stochastic and algorithmic methods have been published until now, for the modeling of the complex nature of the biological systems.Objective:Biological Networks computational modeling is still a challenging task. The formulation of the complex cellular interactions is a research field of great interest. In this review paper, several computational methods for the modeling of GRN and PPI are presented analytically.Methods:Several well-known GRN and PPI model

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Olsen, Anders Krüger, Mette Boyd, Erik Thomas Danielsen, and Jesper Thorvald Troelsen. "Current and emerging approaches to define intestinal epithelium-specific transcriptional networks." American Journal of Physiology-Gastrointestinal and Liver Physiology 302, no. 3 (2012): G277—G286. http://dx.doi.org/10.1152/ajpgi.00362.2011.

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Upon developmental or environmental cues, the composition of transcription factors in a transcriptional regulatory network is deeply implicated in controlling the signature of the gene expression and thereby specifies the cell or tissue type. Novel methods including ChIP-chip and ChIP-Seq have been applied to analyze known transcription factors and their interacting regulatory DNA elements in the intestine. The intestine is an example of a dynamic tissue where stem cells in the crypt proliferate and undergo a differentiation process toward the villus. During this differentiation process, speci

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Amin, Samir B., Zhenyu Yan, Parantu Shah, et al. "An Integrative Analysis of Network Motifs and Gene Expression Data to Discover Experimentally Testable Transcription Factor-miRNA-Gene Regulatory Loops In Multiple Myeloma." Blood 116, no. 21 (2010): 1926. http://dx.doi.org/10.1182/blood.v116.21.1926.1926.

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Abstract Abstract 1926 Gene regulatory networks with regulatory circuits at different domains are the fundamental mechanism in phenotypic expression of the underlying genome. Regulation by transcription factors (TF) and microRNA (miRNA) are important such domains beside other epigenetic and post-translational gene regulation. With many genomic data sets in recent years, improved understanding of such regulatory network motifs would be of substantial value to find causal genomic alternations in the cancer. A particular example of one such TF-miRNA-gene feed-forward loop (FFL) is, TF (e.g. MYC)

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Sung, Ying Ying, and Edwin Cheung. "Androgen receptor co-regulatory networks in castration-resistant prostate cancer." Endocrine-Related Cancer 21, no. 1 (2013): R1—R11. http://dx.doi.org/10.1530/erc-13-0326.

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Androgen and the androgen receptor (AR) are critical effectors of prostate cancer. Consequently, androgen deprivation therapy is typically employed as a first-line treatment for prostate cancer patients. While initial responses are generally positive, prostate tumors frequently recur and progress to a lethal form known as castration-resistant prostate cancer (CRPC). Recently, considerable effort has been directed toward elucidating the molecular mechanisms of CRPC. Results from both preclinical and clinical studies suggest that AR-mediated signaling persists and remains functionally important

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Overton, Ian M., Andrew H. Sims, Jeremy A. Owen, et al. "Functional Transcription Factor Target Networks Illuminate Control of Epithelial Remodelling." Cancers 12, no. 10 (2020): 2823. http://dx.doi.org/10.3390/cancers12102823.

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Cell identity is governed by gene expression, regulated by transcription factor (TF) binding at cis-regulatory modules. Decoding the relationship between TF binding patterns and gene regulation is nontrivial, remaining a fundamental limitation in understanding cell decision-making. We developed the NetNC software to predict functionally active regulation of TF targets; demonstrated on nine datasets for the TFs Snail, Twist, and modENCODE Highly Occupied Target (HOT) regions. Snail and Twist are canonical drivers of epithelial to mesenchymal transition (EMT), a cell programme important in devel

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Tsutsui, Taishi, Hironori Kawahara, Ryouken Kimura, et al. "Glioma-derived extracellular vesicles promote tumor progression by conveying WT1." Carcinogenesis 41, no. 9 (2020): 1238–45. http://dx.doi.org/10.1093/carcin/bgaa052.

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Abstract Glioma persists as one of the most aggressive primary tumors of the central nervous system. Glioma cells are known to communicate with tumor-associated macrophages/microglia via various cytokines to establish the tumor microenvironment. However, how extracellular vesicles (EVs), emerging regulators of cell–cell communication networks, function in this process is still elusive. We report here that glioma-derived EVs promote tumor progression by affecting microglial gene expression in an intracranial implantation glioma model mouse. The gene expression of thrombospondin-1 (Thbs1), a neg

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Spidlova, Petra, Pavla Stojkova, Anders Sjöstedt, and Jiri Stulik. "Control of Francisella tularensis Virulence at Gene Level: Network of Transcription Factors." Microorganisms 8, no. 10 (2020): 1622. http://dx.doi.org/10.3390/microorganisms8101622.

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Regulation of gene transcription is the initial step in the complex process that controls gene expression within bacteria. Transcriptional control involves the joint effort of RNA polymerases and numerous other regulatory factors. Whether global or local, positive or negative, regulators play an essential role in the bacterial cell. For instance, some regulators specifically modify the transcription of virulence genes, thereby being indispensable to pathogenic bacteria. Here, we provide a comprehensive overview of important transcription factors and DNA-binding proteins described for the virul

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Tang, Ren-Yi, Zun Wang, Hong-Qi Chen, and Si-Bo Zhu. "Negative Correlation between miR-200c and Decorin Plays an Important Role in the Pathogenesis of Colorectal Carcinoma." BioMed Research International 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/1038984.

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Aim. To demonstrate the regulatory role of miRNA in colorectal carcinoma (CRC) and reveal the transcript markers that may be associated with CRC clinical outcomes. Method. Herein, we analyzed both mRNA and miRNA gene expression profiles of 255 CRC tumor samples from The Cancer Genome Atlas project to reveal the regulatory association between miRNA and mRNA. Also, the potential role of gene coexpression network in CRC has been explored. Results. The negative correlation between miR-200c and DCN (Decorin) was calculated in CRC, indicating that DCN could be a potential target of miR-200c. Clinica

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Kabir Ahmad, Farzana, and Siti Sakira Kamaruddin. "Research Trends in Microarray Data Analysis: Modelling Gene Regulatory Network by Integrating Transcription Factors Data." Scientific Research Journal 12, no. 1 (2015): 39. http://dx.doi.org/10.24191/srj.v12i1.5437.

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The invention of microarray technology has enabled expression levels of thousands of genes to be monitored at once. This modernized approach has created large amount of data to be examined. Recently, gene regulatory network has been an interesting topic and generated impressive research goals in computational biology. Better understanding of the genetic regulatory processes would bring significant implications in the biomedical fields and many other pharmaceutical industries. As a result, various mathematical and computational methods have been used to model gene regulatory network from microa

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Cantarella, Simona, Elena Di Nisio, Davide Carnevali, Giorgio Dieci, and Barbara Montanini. "Interpreting and integrating big data in non-coding RNA research." Emerging Topics in Life Sciences 3, no. 4 (2019): 343–55. http://dx.doi.org/10.1042/etls20190004.

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Abstract In the last two decades, we have witnessed an impressive crescendo of non-coding RNA studies, due to both the development of high-throughput RNA-sequencing strategies and an ever-increasing awareness of the involvement of newly discovered ncRNA classes in complex regulatory networks. Together with excitement for the possibility to explore previously unknown layers of gene regulation, these advancements led to the realization of the need for shared criteria of data collection and analysis and for novel integrative perspectives and tools aimed at making biological sense of very large bo

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Ottaviani, Silvia, Alexander de Giorgio, Victoria Harding, Justin Stebbing, and Leandro Castellano. "Noncoding RNAs and the control of hormonal signaling via nuclear receptor regulation." Journal of Molecular Endocrinology 53, no. 2 (2014): R61—R70. http://dx.doi.org/10.1530/jme-14-0134.

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Despite its identification over 100 years ago, new discoveries continue to add to the complexity of the regulation of the endocrine system. Today the nuclear receptors (NRs) that play such a pivotal role in the extensive communication networks of hormones and gene expression remain an area of intense research. By orchestrating core processes, from metabolism to organismal development, the gene expression programs they control are dependent on their cellular context, their own levels, and those of numerous co-regulatory proteins. A previously unknown component of these networks, noncoding RNAs

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Jung, Hee Chan, Sung Hwan Kim, Jeong Hoon Lee, Ju Han Kim, and Sung Won Han. "Gene Regulatory Network Analysis for Triple-Negative Breast Neoplasms by Using Gene Expression Data." Journal of Breast Cancer 20, no. 3 (2017): 240. http://dx.doi.org/10.4048/jbc.2017.20.3.240.

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AGUILAR-HIDALGO, DANIEL, ANTONIO CÓRDOBA ZURITA, and Ma CARMEN LEMOS FERNÁNDEZ. "COMPLEX NETWORKS EVOLUTIONARY DYNAMICS USING GENETIC ALGORITHMS." International Journal of Bifurcation and Chaos 22, no. 07 (2012): 1250156. http://dx.doi.org/10.1142/s0218127412501568.

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Gene regulatory networks set a second order approximation to genetics understanding, where the first order is the knowledge at the single gene activity level. With the increasing number of sequenced genomes, including humans, the time has come to investigate the interactions among myriads of genes that result in complex behaviors. These characteristics are included in the novel discipline of Systems Biology. The composition and unfolding of interactions among genes determine the activity of cells and, when is considered during development, the organogenesis. Hence the interest of building repr

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Li, Junyi, Yi-Xue Li, and Yuan-Yuan Li. "Differential Regulatory Analysis Based on Coexpression Network in Cancer Research." BioMed Research International 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/4241293.

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With rapid development of high-throughput techniques and accumulation of big transcriptomic data, plenty of computational methods and algorithms such as differential analysis and network analysis have been proposed to explore genome-wide gene expression characteristics. These efforts are aiming to transform underlying genomic information into valuable knowledges in biological and medical research fields. Recently, tremendous integrative research methods are dedicated to interpret the development and progress of neoplastic diseases, whereas differential regulatory analysis (DRA) based on gene c

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Dunipace, Leslie, Zsuzsa Ákos, and Angelike Stathopoulos. "Coacting enhancers can have complementary functions within gene regulatory networks and promote canalization." PLOS Genetics 15, no. 12 (2019): e1008525. http://dx.doi.org/10.1371/journal.pgen.1008525.

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Nghe, Philippe, Bela M. Mulder, and Sander J. Tans. "A graph-based algorithm for the multi-objective optimization of gene regulatory networks." European Journal of Operational Research 270, no. 2 (2018): 784–93. http://dx.doi.org/10.1016/j.ejor.2018.04.020.

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Vemuri, Goutham N., and Aristos A. Aristidou. "Metabolic Engineering in the -omics Era: Elucidating and Modulating Regulatory Networks." Microbiology and Molecular Biology Reviews 69, no. 2 (2005): 197–216. http://dx.doi.org/10.1128/mmbr.69.2.197-216.2005.

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SUMMARY The importance of regulatory control in metabolic processes is widely acknowledged, and several enquiries (both local and global) are being made in understanding regulation at various levels of the metabolic hierarchy. The wealth of biological information has enabled identifying the individual components (genes, proteins, and metabolites) of a biological system, and we are now in a position to understand the interactions between these components. Since phenotype is the net result of these interactions, it is immensely important to elucidate them not only for an integrated understanding

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Aqaqe, Nasma, and Michael Milyavsky. "Characterization of gene regulatory networks responsible for human leukemia cells regeneration after genotoxic stress." Experimental Hematology 44, no. 9 (2016): S56. http://dx.doi.org/10.1016/j.exphem.2016.06.084.

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Wang, Li, Hongying Zhao, Jing Li, et al. "Identifying functions and prognostic biomarkers of network motifs marked by diverse chromatin states in human cell lines." Oncogene 39, no. 3 (2019): 677–89. http://dx.doi.org/10.1038/s41388-019-1005-1.

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Abstract Epigenetic modifications play critical roles in modulating gene expression, yet their roles in regulatory networks in human cell lines remain poorly characterized. We integrated multiomics data to construct directed regulatory networks with nodes and edges labeled with chromatin states in human cell lines. We observed extensive association of diverse chromatin states and network motifs. The gene expression analysis showed that diverse chromatin states of coherent type-1 feedforward loop (C1-FFL) and incoherent type-1 feedforward loops (I1-FFL) contributed to the dynamic expression pat

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Salleh, Faridah Hani Mohamed, Suhaila Zainudin, and Shereena M. Arif. "Multiple Linear Regression for Reconstruction of Gene Regulatory Networks in Solving Cascade Error Problems." Advances in Bioinformatics 2017 (January 29, 2017): 1–14. http://dx.doi.org/10.1155/2017/4827171.

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Gene regulatory network (GRN) reconstruction is the process of identifying regulatory gene interactions from experimental data through computational analysis. One of the main reasons for the reduced performance of previous GRN methods had been inaccurate prediction of cascade motifs. Cascade error is defined as the wrong prediction of cascade motifs, where an indirect interaction is misinterpreted as a direct interaction. Despite the active research on various GRN prediction methods, the discussion on specific methods to solve problems related to cascade errors is still lacking. In fact, the e

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Tanabe, Shihori, Sabina Quader, Ryuichi Ono, et al. "Molecular Network Profiling in Intestinal- and Diffuse-Type Gastric Cancer." Cancers 12, no. 12 (2020): 3833. http://dx.doi.org/10.3390/cancers12123833.

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Epithelial-mesenchymal transition (EMT) plays an important role in the acquisition of cancer stem cell (CSC) feature and drug resistance, which are the main hallmarks of cancer malignancy. Although previous findings have shown that several signaling pathways are activated in cancer progression, the precise mechanism of signaling pathways in EMT and CSCs are not fully understood. In this study, we focused on the intestinal and diffuse-type gastric cancer (GC) and analyzed the gene expression of public RNAseq data to understand the molecular pathway regulation in different subtypes of gastric ca

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Altmäe, Signe, Jüri Reimand, Outi Hovatta, et al. "Research Resource: Interactome of Human Embryo Implantation: Identification of Gene Expression Pathways, Regulation, and Integrated Regulatory Networks." Molecular Endocrinology 26, no. 1 (2012): 203–17. http://dx.doi.org/10.1210/me.2011-1196.

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Cai, Chunhui, Lujia Chen, Xia Jiang, and Xinghua Lu. "Modeling Signal Transduction from Protein Phosphorylation to Gene Expression." Cancer Informatics 13s1 (January 2014): CIN.S13883. http://dx.doi.org/10.4137/cin.s13883.

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Background Signaling networks are of great importance for us to understand the cell's regulatory mechanism. The rise of large-scale genomic and proteomic data, and prior biological knowledge has paved the way for the reconstruction and discovery of novel signaling pathways in a data-driven manner. In this study, we investigate computational methods that integrate proteomics and transcriptomic data to identify signaling pathways transmitting signals in response to specific stimuli. Such methods can be applied to cancer genomic data to infer perturbed signaling pathways. Method We proposed a nov

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Soma, Fumiyuki, Fuminori Takahashi, Kazuko Yamaguchi-Shinozaki, and Kazuo Shinozaki. "Cellular Phosphorylation Signaling and Gene Expression in Drought Stress Responses: ABA-Dependent and ABA-Independent Regulatory Systems." Plants 10, no. 4 (2021): 756. http://dx.doi.org/10.3390/plants10040756.

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Drought is a severe and complex abiotic stress that negatively affects plant growth and crop yields. Numerous genes with various functions are induced in response to drought stress to acquire drought stress tolerance. The phytohormone abscisic acid (ABA) accumulates mainly in the leaves in response to drought stress and then activates subclass III SNF1-related protein kinases 2 (SnRK2s), which are key phosphoregulators of ABA signaling. ABA mediates a wide variety of gene expression processes through stress-responsive transcription factors, including ABA-RESPONSIVE ELEMENT BINDING PROTEINS (AR

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Sánchez-Ramírez, Santiago, Jörg G. Weiss, Cristel G. Thomas, and Asher D. Cutter. "Widespread misregulation of inter-species hybrid transcriptomes due to sex-specific and sex-chromosome regulatory evolution." PLOS Genetics 17, no. 3 (2021): e1009409. http://dx.doi.org/10.1371/journal.pgen.1009409.

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When gene regulatory networks diverge between species, their dysfunctional expression in inter-species hybrid individuals can create genetic incompatibilities that generate the developmental defects responsible for intrinsic post-zygotic reproductive isolation. Both cis- and trans-acting regulatory divergence can be hastened by directional selection through adaptation, sexual selection, and inter-sexual conflict, in addition to cryptic evolution under stabilizing selection. Dysfunctional sex-biased gene expression, in particular, may provide an important source of sexually-dimorphic genetic in

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