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Relevant bibliographies by topics / Gene transcriptional regulatory network

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Academic literature on the topic 'Gene transcriptional regulatory network'

Author: Grafiati

Published: 11 December 2022

Last updated: 26 January 2023

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Journal articles on the topic "Gene transcriptional regulatory network"

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de Luis Balaguer, Maria Angels, Adam P. Fisher, Natalie M. Clark, et al. "Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells." Proceedings of the National Academy of Sciences 114, no. 36 (2017): E7632—E7640. http://dx.doi.org/10.1073/pnas.1707566114.

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Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regula

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Luo, Jiawei, Dan Song, Cheng Liang, Guanghui Li, and Buwen Cao. "Detecting Co-Regulatory Modules from Human Regulatory Network by Randomly Walking Between Regulator and Gene Modules." Journal of Computational and Theoretical Nanoscience 14, no. 1 (2017): 384–88. http://dx.doi.org/10.1166/jctn.2017.6331.

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Gene expression is jointly regulated by microRNAs and transcriptional factors. As such, constructing a regulatory network for microRNAs and transcriptional factors and analyzing their combinatorial effects are vital to understand living organisms. Co-regulatory modules, including functional homogeneous microRNAs, transcriptional factors, and genes, provide insights into coordinate regulation. In this paper, we propose a random walk with restart between regulator and gene modules (RWRRGM) method to detect co-regulatory modules from a human regulatory network. The network integrates large, heter

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Otálora-Otálora, Beatriz Andrea, Liliana López-Kleine, and Adriana Rojas. "Lung Cancer Gene Regulatory Network of Transcription Factors Related to the Hallmarks of Cancer." Current Issues in Molecular Biology 45, no. 1 (2023): 434–64. http://dx.doi.org/10.3390/cimb45010029.

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The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target ge

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Otálora-Otálora, Beatriz Andrea, Cristian González Prieto, Lucia Guerrero, et al. "Identification of the Transcriptional Regulatory Role of RUNX2 by Network Analysis in Lung Cancer Cells." Biomedicines 10, no. 12 (2022): 3122. http://dx.doi.org/10.3390/biomedicines10123122.

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The use of a new bioinformatics pipeline allowed the identification of deregulated transcription factors (TFs) coexpressed in lung cancer that could become biomarkers of tumor establishment and progression. A gene regulatory network (GRN) of lung cancer was created with the normalized gene expression levels of differentially expressed genes (DEGs) from the microarray dataset GSE19804. Moreover, coregulatory and transcriptional regulatory network (TRN) analyses were performed for the main regulators identified in the GRN analysis. The gene targets and binding motifs of all potentially implicate

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Schick, Sandra, Kolja Becker, Sudhir Thakurela, et al. "Identifying Novel Transcriptional Regulators with Circadian Expression." Molecular and Cellular Biology 36, no. 4 (2015): 545–58. http://dx.doi.org/10.1128/mcb.00701-15.

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Organisms adapt their physiology and behavior to the 24-h day-night cycle to which they are exposed. On a cellular level, this is regulated by intrinsic transcriptional-translational feedback loops that are important for maintaining the circadian rhythm. These loops are organized by members of the core clock network, which further regulate transcription of downstream genes, resulting in their circadian expression. Despite progress in understanding circadian gene expression, only a few players involved in circadian transcriptional regulation, including transcription factors, epigenetic regulato

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AWAD, SHERINE, NICHOLAS PANCHY, SEE-KIONG NG, and JIN CHEN. "INFERRING THE REGULATORY INTERACTION MODELS OF TRANSCRIPTION FACTORS IN TRANSCRIPTIONAL REGULATORY NETWORKS." Journal of Bioinformatics and Computational Biology 10, no. 05 (2012): 1250012. http://dx.doi.org/10.1142/s0219720012500126.

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Living cells are realized by complex gene expression programs that are moderated by regulatory proteins called transcription factors (TFs). The TFs control the differential expression of target genes in the context of transcriptional regulatory networks (TRNs), either individually or in groups. Deciphering the mechanisms of how the TFs control the differential expression of a target gene in a TRN is challenging, especially when multiple TFs collaboratively participate in the transcriptional regulation. To unravel the roles of the TFs in the regulatory networks, we model the underlying regulato

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Knaack, Sara A., Alireza Fotuhi Siahpirani, and Sushmita Roy. "A Pan-Cancer Modular Regulatory Network Analysis to Identify Common and Cancer-Specific Network Components." Cancer Informatics 13s5 (January 2014): CIN.S14058. http://dx.doi.org/10.4137/cin.s14058.

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Many human diseases including cancer are the result of perturbations to transcriptional regulatory networks that control context-specific expression of genes. A comparative approach across multiple cancer types is a powerful approach to illuminate the common and specific network features of this family of diseases. Recent efforts from The Cancer Genome Atlas (TCGA) have generated large collections of functional genomic data sets for multiple types of cancers. An emerging challenge is to devise computational approaches that systematically compare these genomic data sets across different cancer

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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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Ahi, Ehsan Pashay, Emmanouil Tsakoumis, Mathilde Brunel, and Monika Schmitz. "Transcriptional study reveals a potential leptin-dependent gene regulatory network in zebrafish brain." Fish Physiology and Biochemistry 47, no. 4 (2021): 1283–98. http://dx.doi.org/10.1007/s10695-021-00967-0.

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AbstractThe signal mediated by leptin hormone and its receptor is a major regulator of body weight, food intake and metabolism. In mammals and many teleost fish species, leptin has an anorexigenic role and inhibits food intake by influencing the appetite centres in the hypothalamus. However, the regulatory connections between leptin and downstream genes mediating its appetite-regulating effects are still not fully explored in teleost fish. In this study, we used a loss of function leptin receptor zebrafish mutant and real-time quantitative PCR to assess brain expression patterns of several pre

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Lou, Yi, Yi-Dan Chen, Fu-Rong Sun, Jun-Ping Shi, Yu Song, and Jin Yang. "Potential Regulators Driving the Transition in Nonalcoholic Fatty Liver Disease: a Stage-Based View." Cellular Physiology and Biochemistry 41, no. 1 (2017): 239–51. http://dx.doi.org/10.1159/000456061.

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Background and Aim: The incidence of nonalcoholic fatty liver disease (NAFLD), ranging from mild steatosis to hepatocellular injury and inflammation, increases with the rise of obesity. However, the implications of transcription factors network in progressive NAFLD remain to be determined. Methods: A co-regulatory network approach by combining gene expression and transcription influence was utilized to dissect transcriptional regulators in different NAFLD stages. In vivo, mice models of NAFLD were used to investigate whether dysregulated expression be undertaken by transcriptional regulators.

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Dissertations / Theses on the topic "Gene transcriptional regulatory network"

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Balasubramanian, Deepak. "Pseudomonas Aeruginosa AmpR Transcriptional Regulatory Network." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/863.

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In Enterobacteriaceae, the transcriptional regulator AmpR, a member of the LysR family, regulates the expression of a chromosomal β-lactamase AmpC. The regulatory repertoire of AmpR is broader in Pseudomonas aeruginosa, an opportunistic pathogen responsible for numerous acute and chronic infections including cystic fibrosis. Previous studies showed that in addition to regulating ampC, P. aeruginosa AmpR regulates the sigma factor AlgT/U and production of some quorum sensing (QS)-regulated virulence factors. In order to better understand the ampR regulon, the transcriptional profiles generated

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Hong, Ted. "Alteration of Human Gene Regulatory Networks by Human Virus Transcriptional Regulators." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1593273403439508.

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MacPherson, Cameron Ross. "Transcriptional Regulatory Networks in the Mouse Hippocampus." Thesis, University of the Western Cape, 2007. http://etd.uwc.ac.za/index.php?module=etd&action=viewtitle&id=gen8Srv25Nme4_1683_1259931126.

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<p> <p>&nbsp<br></p> </p> <p align="left">This study utilized large-scale gene expression data to define the regulatory networks of genes expressing in the hippocampus to which multiple disease pathologies may be associated. Specific aims were: ident i fy key regulatory transcription factors (TFs) responsible for observed gene expression patterns, reconstruct transcription regulatory networks, and prioritize likely TFs responsible for anatomically restricted gene expression. Most of the analysis was restricted to the CA3 sub-region of Ammon&rsquo<br>s horn within the hippocampus. We identified

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Chua, Xin-Yi. "Prediction of transcriptional regulatory interactions in bacteria : a comparative genomics approach." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/55249/1/Xin-Yi_Chua_Thesis.pdf.

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Exponential growth of genomic data in the last two decades has made manual analyses impractical for all but trial studies. As genomic analyses have become more sophisticated, and move toward comparisons across large datasets, computational approaches have become essential. One of the most important biological questions is to understand the mechanisms underlying gene regulation. Genetic regulation is commonly investigated and modelled through the use of transcriptional regulatory network (TRN) structures. These model the regulatory interactions between two key components: transcription facto

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Zhu, Shaoming. "Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks." Thesis, Queensland University of Technology, 2010. https://eprints.qut.edu.au/41693/1/Shaoming_Zhu_Thesis.pdf.

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Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multis

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Webber, Aaron. "Transcriptional co-regulation of microRNAs and protein-coding genes." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/transcriptional-coregulation-of-micrornas-and-proteincoding-genes(f5b601b2-33f3-4608-9ae8-b7d5a0c6beaf).html.

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This thesis was presented by Aaron Webber on the 4th December 2013 for the degree of Doctor of Philosophy from the University of Manchester. The title of this thesis is ‘Transcriptional co-regulation of microRNAs and protein-coding genes’. The thesis relates to gene expression regulation within humans and closely related primate species. We have investigated the binding site distributions from publically available ChIP-seq data of 117 transcription regulatory factors (TRFs) within the human genome. These were mapped to cis-regulatory regions of two major classes of genes,  20,000 genes encodi

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Linley, A. J. "A dynamic transcriptome technique for transcriptional profiling and gene regulatory network involving the helicase antigen (HAGE)." Thesis, Nottingham Trent University, 2010. http://irep.ntu.ac.uk/id/eprint/243/.

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Increased knowledge into the molecular pathways disrupted in tumours has led to the development of various therapies that can target specific mediators of these cascades. Such therapies have proven successful in patients or demonstrate significant potential for clinical use. However, this better understanding is undermined by the continued prevalence of cancer and the limitations of these drugs. Therefore, it is possible signalling networks could be influenced by as yet unknown molecules or known mediators with function that have not yet been described. As a result of this, work must continue

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Scofield, Michael D. "Elucidating the Transcriptional Network Underlying Expression of a Neuronal Nicotinic Receptor Gene: A Dissertation." eScholarship@UMMS, 2009. http://escholarship.umassmed.edu/gsbs_diss/497.

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Neuronal nicotinic acetylcholine receptors (nAChRs) are involved in a plethora of fundamental biological processes ranging from muscle contraction to the formation of memories. The studies described in this work focus on the transcriptional regulation of the CHRNB4 gene, which encodes the ß4 subunit of neuronal nAChRs. We previously identified a regulatory sequence (5´– CCACCCCT –3´), or “CA box”, critical for CHRNB4 promoter activity in vitro. Here I report transcription factor interaction at the CA box along with an in vivo analysis of CA box transcriptional activity. My data indicate that S

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Scofield, Michael D. "Elucidating the Transcriptional Network Underlying Expression of a Neuronal Nicotinic Receptor Gene: A Dissertation." eScholarship@UMMS, 2010. https://escholarship.umassmed.edu/gsbs_diss/497.

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Neuronal nicotinic acetylcholine receptors (nAChRs) are involved in a plethora of fundamental biological processes ranging from muscle contraction to the formation of memories. The studies described in this work focus on the transcriptional regulation of the CHRNB4 gene, which encodes the ß4 subunit of neuronal nAChRs. We previously identified a regulatory sequence (5´– CCACCCCT –3´), or “CA box”, critical for CHRNB4 promoter activity in vitro. Here I report transcription factor interaction at the CA box along with an in vivo analysis of CA box transcriptional activity. My data indicate that S

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Han, Nam Shik. "Systematic approaches for modelling and visualising responses to perturbation of transcriptional regulatory networks." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/systematic-approaches-for-modelling-and-visualising-responses-to-perturbation-of-transcriptional-regulatory-networks(3f4cf115-3b68-457f-8fd6-0f7609d5b9bc).html.

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One of the greatest challenges in modern biology is to understand quantitatively the mechanisms underlying messenger Ribonucleic acid (mRNA) transcription within the cell. To this end, integrated functional genomics attempts to use the vast wealth of data produced by modern large scale genomic projects to understand how the genome is deployed to create a diversity of tissues and species. The expression levels of tens or hundreds of thousands genes are profiled at multiple time points or different experimental conditions in the genomic projects. The profiling results are deposited in large scal

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Books on the topic "Gene transcriptional regulatory network"

1

Babu, M. Madan. Bacterial gene regulation and transcriptional networks. Caister Academic Press, 2013.

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Stem cell transcriptional networks: Methods and protocols. Humana Press/Springer, 2014.

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Gene regulatory networks: Methods and protocols. Humana Press, 2012.

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Ahsen, Mehmet Eren, Hitay Özbay, and Silviu-Iulian Niculescu. Analysis of Deterministic Cyclic Gene Regulatory Network Models with Delays. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15606-4.

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Nagai, Ryōzō. The biology of Krüppel-like factors. Springer, 2009.

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Lamoreux, M. Lynn. The colors of mice: A model genetic network. Wiley-Blackwell, 2010.

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Lynn, Lamoreux M., ed. The colors of mice: A model genetic network. Wiley-Blackwell, 2010.

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Gene network inference: Verification of methods for systems genetics data. Springer, 2013.

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Kidder, Benjamin L. Stem Cell Transcriptional Networks: Methods and Protocols. Springer, 2021.

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Kidder, Benjamin L. Stem Cell Transcriptional Networks: Methods and Protocols. Springer New York, 2016.

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Book chapters on the topic "Gene transcriptional regulatory network"

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Handzlik, Joanna E., Yen Lee Loh, and Manu. "Dynamic Modeling of Transcriptional Gene Regulatory Networks." In Modeling Transcriptional Regulation. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_5.

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Sehgal, Muhammad Shoaib B., Iqbal Gondal, Laurence Dooley, Ross Coppel, and Goh Kiah Mok. "Transcriptional Gene Regulatory Network Reconstruction Through Cross Platform Gene Network Fusion." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75286-8_27.

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Gonye, Gregory E., Praveen Chakravarthula, James S. Schwaber, and Rajanikanth Vadigepalli. "From Promoter Analysis to Transcriptional Regulatory Network Prediction Using PAINT." In Gene Function Analysis. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-547-3_4.

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Kumar, Nilesh, Bharat Mishra, Mohammad Athar, and Shahid Mukhtar. "Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC." In Modeling Transcriptional Regulation. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_10.

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Li, Song, Haidong Yan, and Jiyoung Lee. "Identification of Gene Regulatory Networks from Single-Cell Expression Data." In Modeling Transcriptional Regulation. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_9.

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Kumar, Nilesh, Bharat Mishra, Mohammad Athar, and Shahid Mukhtar. "Correction to: Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC." In Modeling Transcriptional Regulation. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_20.

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Zhang, Shilu, Sara Knaack, and Sushmita Roy. "Enabling Studies of Genome-Scale Regulatory Network Evolution in Large Phylogenies with MRTLE." In Methods in Molecular Biology. Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2257-5_24.

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AbstractTranscriptional regulatory networks specify context-specific patterns of genes and play a central role in how species evolve and adapt. Inferring genome-scale regulatory networks in non-model species is the first step for examining patterns of conservation and divergence of regulatory networks. Transcriptomic data obtained under varying environmental stimuli in multiple species are becoming increasingly available, which can be used to infer regulatory networks. However, inference and analysis of multiple gene regulatory networks in a phylogenetic setting remains challenging. We developed an algorithm, Multi-species Regulatory neTwork LEarning (MRTLE), to facilitate such studies of regulatory network evolution. MRTLE is a probabilistic graphical model-based algorithm that uses phylogenetic structure, transcriptomic data for multiple species, and sequence-specific motifs in each species to simultaneously infer genome-scale regulatory networks across multiple species. We applied MRTLE to study regulatory network evolution across six ascomycete yeasts using transcriptomic measurements collected across different stress conditions. MRTLE networks recapitulated experimentally derived interactions in the model organism S. cerevisiae as well as non-model species, and it was more beneficial for network inference than methods that do not use phylogenetic information. We examined the regulatory networks across species and found that regulators associated with significant expression and network changes are involved in stress-related processes. MTRLE and its associated downstream analysis provide a scalable and principled framework to examine evolutionary dynamics of transcriptional regulatory networks across multiple species in a large phylogeny.

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Moyano, Tomás C., Rodrigo A. Gutiérrez, and José M. Alvarez. "Genomic Footprinting Analyses from DNase-seq Data to Construct Gene Regulatory Networks." In Modeling Transcriptional Regulation. Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-1534-8_3.

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da Silva Neto, José F. "Transcriptional Analysis of Iron-responsive Regulatory Networks inCaulobacter Crescentus." In Stress and Environmental Regulation of Gene Expression and Adaptation in Bacteria. John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119004813.ch107.

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Bhalla, Parinishtha, Anukriti Verma, Bhawna Rathi, Shivani Sharda, and Pallavi Somvanshi. "Exploring Molecular Signatures in Spondyloarthritis: A Step Towards Early Diagnosis." In Proceedings of the Conference BioSangam 2022: Emerging Trends in Biotechnology (BIOSANGAM 2022). Atlantis Press International BV, 2022. http://dx.doi.org/10.2991/978-94-6463-020-6_15.

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AbstractSpondyloarthritis is an acute inflammatory disorder of the musculoskeletal system often accompanied by pain, stiffness, bone and tissue damage. It majorly consists of ankylosing spondylitis, psoriatic arthritis and reactive arthritis. It follows a differential diagnosis pattern for demarcation between the spondyloarthritis subtypes and other arthritic subtypes such as rheumatoid arthritis, juvenile arthritis and osteoarthritis due to the heterogeneity causing gradual chronicity and complications. Presence of definite molecular markers can not only improve diagnosis efficiency but also aid in their prognosis and therapy. This study is an attempt to compose a refined list of such unique and common molecular signatures of the considered subtypes, by employing a reductionist approach amalgamating gene retrieval, protein-protein interaction network, functional, pathway, micro-RNA-gene and transcription factor-gene regulatory network analysis. Gene retrieval and protein-protein interaction network analysis resulted in unique and common interacting genes of arthritis subtypes. Functional annotation and pathway analysis found vital functions and pathways unique and common in arthritis subtypes. Furthermore, miRNA-gene and transcription factor-gene interaction networks retrieved unique and common miRNA’s and transcription factors in arthritis subtypes. Furthermore, the study identified important signatures of arthritis subtypes that can serve as markers assisting in prognosis, early diagnosis and personalized treatment of arthritis patients requiring validation via prospective experimental studies.

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Conference papers on the topic "Gene transcriptional regulatory network"

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Sehgal, Muhammad Shoaib B., Iqbal Gondal, Laurence Dooley, and Ross Coppel. "Coalesce Gene Regulatory Network Reconstruction: A Cross-Platform Transcriptional Gene Network Fusion Framework." In TENCON 2006 - 2006 IEEE Region 10 Conference. IEEE, 2006. http://dx.doi.org/10.1109/tencon.2006.343719.

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Shan, Feng, Anthony Cillo, Carly Cardello, et al. "1049 Reconstruction of gene regulatory networks dissects transcriptional control of intratumoral regulatory T cells." In SITC 37th Annual Meeting (SITC 2022) Abstracts. BMJ Publishing Group Ltd, 2022. http://dx.doi.org/10.1136/jitc-2022-sitc2022.1049.

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Kamapantula, Bhanu, Michael Mayo, Edward Perkins, and Preetam Ghosh. "Dynamical impacts from structural redundancy of transcriptional motifs in gene-regulatory networks." In 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2015. http://dx.doi.org/10.4108/icst.bict.2014.257928.

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Mayo, Michael, Ahmed Abdelzaher, Bhanu Kamapantula, Edward Perkins, and Preetam Ghosh. "Networks of interactions between feed-forward loop transcriptional motifs in gene-regulatory networks." In 8th International Conference on Bio-inspired Information and Communications Technologies (formerly BIONETICS). ACM, 2015. http://dx.doi.org/10.4108/icst.bict.2014.257926.

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Zhang, L., H. C. Wu, S. C. Chan, and C. Wang. "Dynamic gene and transcriptional regulatory networks inferring with multi-Laplacian prior from time-course gene microarray data." In 2017 22nd International Conference on Digital Signal Processing (DSP). IEEE, 2017. http://dx.doi.org/10.1109/icdsp.2017.8096114.

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Nakabayashi, Jun. "Structural changes in transcriptional regulatory networks for cell-type-specific gene expression during hematopoiesis." In 2020 24th International Conference on Circuits, Systems, Communications and Computers (CSCC). IEEE, 2020. http://dx.doi.org/10.1109/cscc49995.2020.00025.

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Kim, Sheehyun, and Dongsup Kim. "Inference of Gene Regulatory Networks Using Time Sliding Comparison and Transcriptional Lagging Time from Time Series Gene Expression Profiles." In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375684.

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Li, Yichao, Sushil Kumar Jaiswal, Rupleen Kaur, et al. "Abstract 2137: Differential gene expression identifies a transcriptional regulatory network involving ESR1and PITX1 in invasive epithelial ovarian cancer." In Proceedings: AACR Annual Meeting 2021; April 10-15, 2021 and May 17-21, 2021; Philadelphia, PA. American Association for Cancer Research, 2021. http://dx.doi.org/10.1158/1538-7445.am2021-2137.

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Cheng-Long Chuang, Chung-Ming Chen, Grace S. Shieh, and Joe-Air Jiang. "A fuzzy logic approach to infer transcriptional regulatory network in saccharomyces cerevisiae using promoter site prediction and gene expression pattern recognition." In 2008 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2008. http://dx.doi.org/10.1109/cec.2008.4631021.

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Hong Hu and Yang Dai. "A Model-based approach to transcription regulatory network reconstruction from time-course gene expression data." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944690.

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Reports on the topic "Gene transcriptional regulatory network"

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Lers, Amnon, and Gan Susheng. Study of the regulatory mechanism involved in dark-induced Postharvest leaf senescence. United States Department of Agriculture, 2009. http://dx.doi.org/10.32747/2009.7591734.bard.

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Postharvest leaf senescence contributes to quality losses in flowers and leafy vegetables. The general goal of this research project was to investigate the regulatory mechanisms involved in dark-induced leaf senescence. The regulatory system involved in senescence induction and control is highly complex and possibly involves a network of senescence promoting pathways responsible for activation of the senescence-associated genes. Pathways involving different internal signals and environmental factors may have distinctive importance in different leaf senescence systems. Darkness is known to have

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Salari, Keyan. Reconstructing the Prostate Cancer Transcriptional Regulatory Network. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada536827.

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Salari, Keyan. Reconstructing the Prostate Cancer Transcriptional Regulatory Network. Defense Technical Information Center, 2010. http://dx.doi.org/10.21236/ada552450.

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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide si

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Coruzzi, Gloria, Mattjew Brooks, and Ying Li. Asparagine synthetase gene regulatory network and plant nitrogen metabolism. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1463278.

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Friedman, Haya, Julia Vrebalov, and James Giovannoni. Elucidating the ripening signaling pathway in banana for improved fruit quality, shelf-life and food security. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594401.bard.

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Background : Banana being a monocot and having distinct peel and pulp tissues is unique among the fleshy fruits and hence can provide a more comprehensive understanding of fruit ripening. Our previous research which translated ripening discoveries from tomato, led to the identification of six banana fruit-associated MADS-box genes, and we confirmed the positive role of MaMADS1/2 in banana ripening. The overall goal was to further elucidate the banana ripening signaling pathway as mediated by MADS-boxtranscriptional regulators. Specific objectives were: 1) characterize transcriptional profiles

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Barg, Rivka, Erich Grotewold, and Yechiam Salts. Regulation of Tomato Fruit Development by Interacting MYB Proteins. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7592647.bard.

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Background to the topic: Early tomato fruit development is executed via extensive cell divisions followed by cell expansion concomitantly with endoreduplication. The signals involved in activating the different modes of growth during fruit development are still inadequately understood. Addressing this developmental process, we identified SlFSM1 as a gene expressed specifically during the cell-division dependent stages of fruit development. SlFSM1 is the founder of a class of small plant specific proteins containing a divergent SANT/MYB domain (Barg et al 2005). Before initiating this project,

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Fromm, Hillel, Paul Michael Hasegawa, and Aaron Fait. Calcium-regulated Transcription Factors Mediating Carbon Metabolism in Response to Drought. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699847.bard.

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Original objectives: The long-term goal of the proposed research is to elucidate the transcription factors, genes and metabolic networks involved in carbon metabolism and partitioning in response to water deficit. The proposed research focuses on the GTLcalcium/calmodulinbindingTFs and the gene and metabolic networks modulated by these TFs in Arabidopsis thaliana. The specific objectives are as follows. Objective-1 (USA): Physiological analyses of GTL1 loss- and gain-of-function plants under water sufficient and drought stress conditions Objective 2 (USA / Israel-TAU): Characterizion of GTL ta

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Li, Li, Joseph Burger, Nurit Katzir, Yaakov Tadmor, Ari Schaffer, and Zhangjun Fei. Characterization of the Or regulatory network in melon for carotenoid biofortification in food crops. United States Department of Agriculture, 2015. http://dx.doi.org/10.32747/2015.7594408.bard.

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The general goals of the BARD research grant US-4423-11 are to understand how Or regulates carotenoid accumulation and to reveal novel strategies for breeding agricultural crops with enhanced β-carotene level. The original objectives are: 1) to identify the genes and proteins in the Or regulatory network in melon; 2) to genetically and molecularly characterize the candidate genes; and 3) to define genetic and functional allelic variation of these genes in a representative germplasm collection of the C. melo species. Or was found by the US group to causes provitamin A accumulation in chromoplas

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Prusky, Dov, and Jeffrey Rollins. Modulation of pathogenicity of postharvest pathogens by environmental pH. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7587237.bard.

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Until recently, environmental pH was not considered a factor in determining pathogen compatibility. Our hypothesis was that the environmental pH at the infection site, which is dynamically controlled by activities of both the host and the pathogen, regulates the expression of genes necessary for disease development in Colletotrichum gloeosporioides and Sclerotinia sclerotiorum. This form of regulation ensures that genes are expressed at optimal conditions for their encoded activities.Pectate lyase encoded by pelB, has been demonstrated to play a key role in virulence of C. gloeosporioides in a

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