Relevant bibliographies by topics / Protein and gene networks

Academic literature on the topic 'Protein and gene networks'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Protein and gene networks"

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Akker, Erik van den, Bas Verbruggen, Bas Heijmans, Marian Beekman, Joost Kok, Eline Slagboom, and Marcel Reinders. "Integrating Protein-Protein Interaction Networks with Gene- Gene Co-Expression Networks improves Gene Signatures for Classifying Breast Cancer Metastasis." Journal of Integrative Bioinformatics 8, no. 2 (June 1, 2011): 222–38. http://dx.doi.org/10.1515/jib-2011-188.

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Summary Multiple studies have illustrated that gene expression profiling of primary breast cancers throughout the final stages of tumor development can provide valuable markers for risk prediction of metastasis and disease sub typing. However, the identification of a biologically interpretable and universally shared set of markers proved to be difficult. Here, we propose a method for de novo grouping of genes by dissecting the proteinprotein interaction network into disjoint sub networks using pair wise gene expression correlation measures. We show that the obtained sub networks are functionally coherent and are consistently identified when applied on a compendium composed of six different breast cancer studies. Application of the proposed method using different integration approaches underlines the robustness of the identified sub network related to cell cycle and identifies putative new sub network markers for metastasis related to cell-cell adhesion, the proteasome complex and JUN-FOS signalling. Although gene selection with the proposed method does not directly improve upon previously reported cross study classification performances, it shows great promises for applications in data integration and result interpretation.
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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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Amoutzias, Gregory D., David L. Robertson, and Erich Bornberg-Bauer. "The Evolution of Protein Interaction Networks in Regulatory Proteins." Comparative and Functional Genomics 5, no. 1 (2004): 79–84. http://dx.doi.org/10.1002/cfg.365.

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Interactions between proteins are essential for intracellular communication. They form complex networks which have become an important source for functional analysis of proteins. Combining phylogenies with network analysis, we investigate the evolutionary history of interaction networks from the bHLH, NR and bZIP transcription-factor families. The bHLH and NR networks show a hub-like structure with varying γ values. Mutation and gene duplication play an important role in adding and removing interactions. We conclude that in several of the protein families that we have studied, networks have primarily arisen by the development of heterodimerizing transcription factors, from an ancestral gene which interacts with any of the newly emerging proteins but also homodimerizes.
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Aloraini, Adel, and Karim M. ElSawy. "Potential Breast Anticancer Drug Targets Revealed by Differential Gene Regulatory Network Analysis and Molecular Docking: Neoadjuvant Docetaxel Drug as a Case Study." Cancer Informatics 17 (January 1, 2018): 117693511875535. http://dx.doi.org/10.1177/1176935118755354.

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Understanding gene-gene interaction and its causal relationship to protein-protein interaction is a viable route for understanding drug action at the genetic level, which is largely hindered by inability to robustly map gene regulatory networks. Here, we use biological prior knowledge of family-to-family gene interactions available in the KEGG database to reveal individual gene-to-gene interaction networks that underlie the gene expression profiles of 2 cell line data sets, sensitive and resistive to neoadjuvant docetaxel breast anticancer drug. Comparison of the topology of the 2 networks revealed that the resistant network is highly connected with 2 large domains of connectivity: one in which the RAF1 and MAP2K2 genes form hubs of connectivity and another in which the RAS gene is highly connected. On the contrary, the sensitive network is highly disrupted with a lower degree of connectivity. We investigated the interactions of the neoadjuvant docetaxel drug with the protein chains encoded by gene-gene interactions that underlie the disruption of the sensitive network topology using protein-protein and drug-protein docking techniques. We found that the sensitive network is likely to be disrupted by interaction of the neoadjuvant docetaxel drug with the DAXX and FGR1 proteins, which is consistent with the observed accumulation of cytoplasmic DAXX and overexpression of FGR1 precursors in cancer cell lines. This indicates that the DAXX and FGR1 proteins could be potential targets for the neoadjuvant docetaxel drug. The work, therefore, provides a new route for understanding the effect of the drug mode of action from the viewpoint of the change in the topology of gene-gene regulatory networks and provides a new avenue for bridging the gap between gene-gene interactions and protein-protein interactions which could have deep implications on mainstream drug development protocols.
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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 be restructure Karnaugh Maps. It could find logic relationships between protein and protein and restructure protein logic networks. It find logic relationship among proteins as well as COGs (clusters of orthogous groups) and build the logic network of protein.
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Chatzaroulas, Evangelos, Vytenis Sliogeris, Pedro Victori, Francesca M. Buffa, Sotiris Moschoyiannis, and Roman Bauer. "A Structural Characterisation of the Mitogen-Activated Protein Kinase Network in Cancer." Symmetry 14, no. 5 (May 16, 2022): 1009. http://dx.doi.org/10.3390/sym14051009.

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Gene regulatory networks represent collections of regulators that interact with each other and with other molecules to govern gene expression. Biological signalling networks model how signals are transmitted and how activities are coordinated in the cell. The study of the structure of such networks in complex diseases such as cancer can provide insights into how they function, and consequently, suggest suitable treatment approaches. Here, we explored such topological characteristics in the example of a mitogen-activated protein kinase (MAPK) signalling network derived from published studies in cancer. We employed well-established techniques to conduct network analyses, and collected information on gene function as obtained from large-scale public databases. This allowed us to map topological and functional relationships, and build hypotheses on this network’s functional consequences. In particular, we find that the topology of this MAPK network is highly non-random, modular and robust. Moreover, analysis of the network’s structure indicates the presence of organisational features of cancer hallmarks, expressed in an asymmetrical manner across communities of the network. Finally, our results indicate that the organisation of this network renders it problematic to use treatment approaches that focus on a single target. Our analysis suggests that multi-target attacks in a well-orchestrated manner are required to alter how the network functions. Overall, we propose that complex network analyses combined with pharmacological insights will help inform on future treatment strategies, exploiting structural vulnerabilities of signalling and regulatory networks in cancer.
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Lundström, Jesper, Johan Björkegren, and Jesper Tegnér. "Evidence of Highly Regulated Genes (in-Hubs) in Gene Networks of Saccharomyces Cerevisiae." Bioinformatics and Biology Insights 2 (January 2008): BBI.S853. http://dx.doi.org/10.4137/bbi.s853.

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Uncovering interactions between genes, gene networks, is important to increase our understanding of intrinsic cellular processes and responses to external stimuli such as drugs. Gene networks can be computationally inferred from repeated measurements of gene expression, using algorithms, which assume that each gene is controlled by only a small number of other proteins. Here, by extending the transcription network with cofactors (defined from protein-protein binding data) as active regulators, we identified the effective gene network, providing evidence of in-hubs in the gene regulatory networks of yeast. Then, using the notion that in-hub genes will be differentially expressed over several experimental conditions, we designed an algorithm, the HubDetector, enabling identification of in-hubs directly from gene expression data. Applying the HubDetector to 488 genome-wide expression profiles from two independent datasets, we identified putative in-hubs overlapping significantly with in-hubs in the effective gene network.
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Wahab Khattak, Fazal, Yousef Salamah Alhwaiti, Amjad Ali, Mohammad Faisal, and Muhammad Hameed Siddiqi. "Protein-Protein Interaction Analysis through Network Topology (Oral Cancer)." Journal of Healthcare Engineering 2021 (January 16, 2021): 1–9. http://dx.doi.org/10.1155/2021/6623904.

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Oral cancer is a complex disorder. Its creation and spreading are due to the interaction of several proteins and genes in different biological thoroughfares. To study biological pathways, many high-yield methods have been used. Efforts to merge several data found at separate levels related to biological thoroughfares and interlinkage networks remain elusive. In our research work, we have proposed a technique known as protein-protein interaction network for analysis and exploring the genes involved in oral cancer disorders. The previous studies have not fully analyzed the proteins or genes involved in oral cancer. Our proposed technique is fully interactive and analyzes the data of oral cancer disorder more accurately and efficiently. The methods used here enabled us to observe the wide network consists of one mighty network comprising of 208 nodes 1572 edges which connect these nodes and various detached small networks. In our study, TP53 is a gene that occupied an important position in the network. TP53 has a 113-degree value and 0.03881821 BC value, indicating that TP53 is centrally localized in the network and is a significant bottleneck protein in the oral cancer protein-protein interaction network. These findings suggested that the pathogenesis of oral cancer variation was organized by means of an integrated PPI network, which is centered on TP53. Furthermore, our identification shows that TP53 is the key role-playing protein in the oral cancer network, and its significance in the cellular networks in the body is determined as well. As TP53 (tumor protein 53) is a vital player in the cell division process, the cells may not grow or divide disorderly; it fulfills the function of at least one of the gene groups in oral cancer. However, the latter progression in the area is any measure; the intention of developing these networks is to transfigure sketch of core disease development, prognosis, and treatment.
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Nguyen, Cao D., Katheleen J. Gardiner, and Krzysztof J. Cios. "Protein annotation from protein interaction networks and Gene Ontology." Journal of Biomedical Informatics 44, no. 5 (October 2011): 824–29. http://dx.doi.org/10.1016/j.jbi.2011.04.010.

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Kim, Yoonbee, Jong-Hoon Park, and Young-Rae Cho. "Network-Based Approaches for Disease-Gene Association Prediction Using Protein-Protein Interaction Networks." International Journal of Molecular Sciences 23, no. 13 (July 3, 2022): 7411. http://dx.doi.org/10.3390/ijms23137411.

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Genome-wide association studies (GWAS) can be used to infer genome intervals that are involved in genetic diseases. However, investigating a large number of putative mutations for GWAS is resource- and time-intensive. Network-based computational approaches are being used for efficient disease-gene association prediction. Network-based methods are based on the underlying assumption that the genes causing the same diseases are located close to each other in a molecular network, such as a protein-protein interaction (PPI) network. In this survey, we provide an overview of network-based disease-gene association prediction methods based on three categories: graph-theoretic algorithms, machine learning algorithms, and an integration of these two. We experimented with six selected methods to compare their prediction performance using a heterogeneous network constructed by combining a genome-wide weighted PPI network, an ontology-based disease network, and disease-gene associations. The experiment was conducted in two different settings according to the presence and absence of known disease-associated genes. The results revealed that HerGePred, an integrative method, outperformed in the presence of known disease-associated genes, whereas PRINCE, which adopted a network propagation algorithm, was the most competitive in the absence of known disease-associated genes. Overall, the results demonstrated that the integrative methods performed better than the methods using graph-theory only, and the methods using a heterogeneous network performed better than those using a homogeneous PPI network only.
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Dissertations / Theses on the topic "Protein and gene networks"

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Gunnarsson, Ida. "Deriving Protein Networks by Combining Gene Expression and Protein Chip Analysis." Thesis, University of Skövde, Department of Computer Science, 2002. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-706.

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In order to derive reliable protein networks it has recently been suggested that the combination of information from both gene and protein level is required. In this thesis a combination of gene expression and protein chip analysis was performed when constructing protein networks. Proteins with high affinity to the same substrates and encoded by genes with high correlation is here thought to constitute reliable protein networks. The protein networks derived are unfortunately not as reliable as were hoped for. According to the tests performed, the method derived in this thesis does not perform more than slightly better than chance. However, the poor results can depend on the data used, since mismatching and shortage of data has been evident.

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Lehtinen, S. K. "Gene and protein networks in understanding cellular function." Thesis, University College London (University of London), 2015. http://discovery.ucl.ac.uk/1470874/.

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Over the past decades, networks have emerged as a useful way of representing complex large-scale systems in a variety of fields. In cellular and molecular biology, gene and protein networks have attracted considerable interest as tools for making sense of increasingly large volumes of data. Despite this interest, there is still substantial debate over how to best exploit network models in cellular biology. This thesis explores the use of gene and protein networks in various biological contexts. The first part of the thesis (Chapter 2) examines protein function prediction using network-based ‘guilt-by-association’ approaches. Given the falling costs of genome sequencing and the availability of large volumes of biological data, automated annotation of gene and protein function is becoming increasingly useful. Chapter 2 describes the development of a new network-based protein function prediction method and compares it to a leading algorithm on a number of benchmarks. Biases in benchmarking methods are also explicitly explored. The second part (Chapters 3 and 4) explores network approaches in understanding loss of function variation in the human genome. For a number of genes, homozygous loss of function appears to have no detrimental effect. A possible explanation is that these genes are only necessary in specific genetic backgrounds. Chapter 3 develops methods for identifying these types of relationships between apparently loss of function tolerant genes. Chapter 4 describes the use of networks in predicting the functional effects of loss of function mutations. The third part of the thesis (Chapters 5 and 6) uses network representations to model the effects of cellular stress on yeast cells. Chapter 5 examines stress induced changes in co-expression and protein interaction networks, finding evidence of increased modularisation in both types of network. Chapter 6 explores the effect of stress on resilience to node removal in the co-expression networks.
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Agarwal, Sumeet. "Networks in nature : dynamics, evolution, and modularity." Thesis, University of Oxford, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.564283.

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In this thesis we propose some new approaches to the study of complex networks, and apply them to multiple domains, focusing in particular on protein-protein interaction networks. We begin by examining the roles of individual proteins; specifically, the influential idea of 'date' and 'party' hubs. It was proposed that party hubs are local coordinators whereas date hubs are global connectors. We show that the observations underlying this proposal appear to have been largely illusory, and that topological properties of hubs do not in general correlate with interactor co-expression, thus undermining the primary basis for the categorisation. However, we find significant correlations between interaction centrality and the functional similarity of the interacting proteins, indicating that it might be useful to conceive of roles for protein-protein interactions, as opposed to individual proteins. The observation that examining just one or a few network properties can be misleading motivates us to attempt to develop a more holistic methodology for network investigation. A wide variety of diagnostics of network structure exist, but studies typically employ only small, largely arbitrarily selected subsets of these. Here we simultaneously investigate many networks using many diagnostics in a data-driven fashion, and demonstrate how this approach serves to organise both networks and diagnostics, as well as to relate network structure to functionally relevant characteristics in a variety of settings. These include finding fast estimators for the solution of hard graph problems, discovering evolutionarily significant aspects of metabolic networks, detecting structural constraints on particular network types, and constructing summary statistics for efficient model-fitting to networks. We use the last mentioned to suggest that duplication-divergence is a feasible mechanism for protein-protein interaction evolution, and that interactions may rewire faster in yeast than in larger genomes like human and fruit fly. Our results help to illuminate protein-protein interaction networks in multiple ways, as well as providing some insight into structure-function relationships in other types of networks. We believe the methodology outlined here can serve as a general-purpose, data-driven approach to aid in the understanding of networked systems.
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Yalamanchili, Hari Krishna. "Computational approaches for protein functions and gene association networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2014. http://hdl.handle.net/10722/206477.

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Entire molecular biology revolves primarily around proteins and genes (DNA and RNA). They collaborate with each other facilitating various biomolecular systems. Thus, to comprehend any biological phenomenon from very basic cell division to most complex cancer, it is fundamental to decode the functional dynamics of proteins and genes. Recently, computational approaches are being widely used to supplement traditional experimental approaches. However, each automated approach has its own advantages and limitations. In this thesis, major shortcomings of existing computational approaches are identified and alternative fast yet precise methods are proposed. First, a strong need for reliable automated protein function prediction is identified. Almost half of protein functional interpretations are enigmatic. Lack of universal functional vocabulary further elevates the problem. NRProF, a novel neural response based method is proposed for protein functional annotation. Neural response algorithm simulates human brain in classifying images; the same is applied here for classifying proteins. Considering Gene Ontology (GO) hierarchical structure as background, NRProF classifies a protein of interest to a specific GO category and thus assigns the corresponding function. Having established reliable protein functional annotations, protein and gene collaborations are studied next. Interactions amongst transcription factors (TFs) and transcription factor binding sites (TFBSs) are fundamental for gene regulation and are highly specific, even in evolution background. To explain this binding specificity a Co-Evo (co-evolutionary) relationship is hypothesized. Pearson correlation and Mutual Information (MI) metrics are used to validate the hypothesis. Residue level MI is used to infer specific binding residues of TFs and corresponding TFBSs, assisting a thorough understanding of gene regulatory mechanism and aid targeted gene therapies. After comprehending TF and TFBS associations, interplay between genes is abstracted as Gene Regulatory Networks. Several methods using expression correlations are proposed to infer gene networks. However, most of them ignore the embedded dynamic delay induced by complex molecular interactions and other riotous cellular mechanisms, involved in gene regulation. The delay is rather obvious in high frequency time series expression data. DDGni, a novel network inference strategy is proposed by adopting gapped smith-waterman algorithm. Gaps attune expression delays and local alignment unveils short regulatory windows, which traditional methods overlook. In addition to gene level expression data, recent studies demonstrated the merits of exon-level RNA-Seq data in profiling splice variants and constructing gene networks. However, the large number of exons versus small sample size limits their practical application. SpliceNet, a novel method based on Large Dimensional Trace is proposed to infer isoform specific co-expression networks from exon-level RNA-Seq data. It provides a more comprehensive picture to our understanding of complex diseases by inferring network rewiring between normal and diseased samples at isoform resolution. It can be applied to any exon level RNA-Seq data and exon array data. In summary, this thesis first identifies major shortcomings of existing computational approaches to functional association of proteins and genes, and develops several tools viz. NRProF, Co-Evo, DDGni and SpliceNet. Collectively, they offer a comprehensive picture of the biomolecular system under study.
published_or_final_version
Biochemistry
Doctoral
Doctor of Philosophy
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Shelton, Rebecca Kay. "Parameter Identifiability and Estimation in Gene and Protein Interaction Networks." Thesis, Virginia Tech, 2008. http://hdl.handle.net/10919/32702.

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The collection of biological data has been limited by instrumentation, the complexity of the systems themselves, and even the ability of graduate students to stay awake and record the data. However, increasing measurement capabilities and decreasing costs may soon enable the collection of reasonably sampled time course data characterizing biological systems, though in general only a subset of the systemâ s species would be measured. This increase in data volume requires a corresponding increase in the use and interpretation of such data, specifically in the development of system identification techniques to identify parameter sets in proposed models. In this paper, we present the results of identifiability analysis on a small test system, including the identifiability of parameters with respect to different measurements (proteins and mRNA), and propose a working definition for â biologically meaningful estimationâ . We also analyze the correlations between parameters, and use this analysis to consider effective approaches to determining parameters with biological meaning. In addition, we look at other methods for determining relationships between parameters and their possible significance. Finally, we present potential biologically meaningful parameter groupings from the test system and present the results of our attempt to estimate the value of select groupings.
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King, James Lowell. "Gene Ontology-Guided Force-Directed Visualization of Protein Interaction Networks." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1066.

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Protein interaction data is being generated at unprecedented rates thanks to advancements made in high throughput techniques such as mass spectrometry and DNA microarrays. Biomedical researchers, operating under budgetary constraints, have found it difficult to scale their efforts to keep up with the ever-increasing amount of available data. They often lack the resources and manpower required to analyze the data using existing methodologies. These research deficiencies impede our ability to understand diseases, delay the advancement of clinical therapeutics, and ultimately costs lives. One of the most commonly used techniques to analyze protein interaction data is the construction and visualization of protein interaction networks. This research investigated the effectiveness and efficiency of novel domain-specific algorithms for visualizing protein interaction networks. The existing domain-agnostic algorithms were compared to the novel algorithms using several performance, aesthetic, and biological relevance metrics. The graph drawing algorithms proposed here introduced novel domain-specific forces to the existing force-directed graph drawing algorithms. The innovations include an attractive force and graph coarsening policy based on semantic similarity, and a novel graph refinement algorithm. These experiments have demonstrated that the novel graph drawing algorithms consistently produce more biologically meaningful layouts than the existing methods. Aggregated over the 480 tests performed, and quantified using the Biological Evaluation Percentage metric defined in the Methodology chapter, the novel graph drawing algorithms created layouts that are 237 percent more biologically meaningful than the next best algorithm. This improvement came at the cost of additional edge crossings and smaller minimum angles between adjacent edges, both of which are undesirable aesthetics. The aesthetic and performance tradeoffs are experimentally quantified in this study, and dozens of algorithmically generated graph drawings are presented to visually illustrate the benefits of the novel algorithms. The graph drawing algorithms proposed in this study will help biomedical researchers to more efficiently produce high quality interactive protein interaction network drawings for improved discovery and communication.
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Yasar, Sevgi. "Multi-resolution Visualization Of Large Scale Protein Networks Enriched With Gene Ontology Annotations." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611132/index.pdf.

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Genome scale protein-protein interactions (PPIs) are interpreted as networks or graphs with thousands of nodes from the perspective of computer science. PPI networks represent various types of possible interactions among proteins or genes of a genome. PPI data is vital in protein function prediction since functions of the cells are performed by groups of proteins interacting with each other and main complexes of the cell are made of proteins interacting with each other. Recent increase in protein interaction prediction techniques have made great amount of protein-protein interaction data available for genomes. As a consequence, a systematic visualization and analysis technique has become crucial. To the best of our knowledge, no PPI visualization tool consider multi-resolution viewing of PPI network. In this thesis, we implemented a new approach for PPI network visualization which supports multi-resolution viewing of compound graphs. We construct compound nodes and label them by using gene set enrichment methods based on Gene Ontology annotations. This thesis further suggests new methods for PPI network visualization.
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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 multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.
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Brettner, Leandra M., and Joanna Masel. "Protein stickiness, rather than number of functional protein-protein interactions, predicts expression noise and plasticity in yeast." BioMed Central, 2012. http://hdl.handle.net/10150/610103.

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BACKGROUND:A hub protein is one that interacts with many functional partners. The annotation of hub proteins, or more generally the protein-protein interaction "degree" of each gene, requires quality genome-wide data. Data obtained using yeast two-hybrid methods contain many false positive interactions between proteins that rarely encounter each other in living cells, and such data have fallen out of favor.RESULTS:We find that protein "stickiness", measured as network degree in ostensibly low quality yeast two-hybrid data, is a more predictive genomic metric than the number of functional protein-protein interactions, as assessed by supposedly higher quality high throughput affinity capture mass spectrometry data. In the yeast Saccharomyces cerevisiae, a protein's high stickiness, but not its high number of functional interactions, predicts low stochastic noise in gene expression, low plasticity of gene expression across different environments, and high probability of forming a homo-oligomer. Our results are robust to a multiple regression analysis correcting for other known predictors including protein abundance, presence of a TATA box and whether a gene is essential. Once the higher stickiness of homo-oligomers is controlled for, we find that homo-oligomers have noisier and more plastic gene expression than other proteins, consistent with a role for homo-oligomerization in mediating robustness.CONCLUSIONS:Our work validates use of the number of yeast two-hybrid interactions as a metric for protein stickiness. Sticky proteins exhibit low stochastic noise in gene expression, and low plasticity in expression across different environments.
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Aragüés, Peleato Ramón. "Protein Interaction networks and their applications to protein characterization and cancer genes prediction." Doctoral thesis, Universitat Pompeu Fabra, 2007. http://hdl.handle.net/10803/7148.

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La importancia de comprender los procesos biológicos ha estimulado el desarrollo de métodos para la detección de interacciones proteína-proteína. Esta tesis presenta PIANA (Protein Interactions And Network Analysis), un programa informático para la integración y el análisis de redes de interacción proteicas. Además, describimos un método que identifica motivos de interacción basándose en que las proteínas con parejas de interacción comunes tienden a interaccionar con esas parejas a través del mismo motivo de interacción. Encontramos que las proteínas altamente conectadas (i.e., hubs) con múltiples motivos tienen mayor probabilidad de ser esenciales para la viabilidad de la célula que los hubs con uno o dos motivos. Finalmente, presentamos un método que predice genes relacionados con cáncer mediante la integración de redes de interacción proteicas, datos de expresión diferenciada y propiedades estructurales, funcionales y evolutivas. El valor de predicción positiva es 71% con sensitividad del 1%, superando a otros métodos usados independientemente.
The importance of understanding cellular processes prompted the development of experimental approaches that detect protein-protein interactions. Here, we describe a software platform called PIANA (Protein Interactions And Network Analysis) that integrates interaction data from multiple sources and automates the analysis of protein interaction networks. Moreover, we describe a method that delineates interacting motifs by relying on the observation that proteins with common interaction partners tend to interact with these partners through the same interacting motif. We find that highly connected proteins (i.e., hubs) with multiple interacting motifs are more likely to be essential for cellular viability than hubs with one or two interacting motifs. Furthermore, we present a method that predicts cancer genes by integrating protein interaction networks, differential expression studies and structural, functional and evolutionary properties. For a sensitivity of 1%, the positive predictive value is 71%, which outperforms the use of any of the methods independently.
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Books on the topic "Protein and gene networks"

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Protein networks and pathway analysis. Dordrecht: Humana Press, 2009.

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Frishman, Dmitrij. Modern genome annotation: The BioSapiens Network. New York: Springer, 2009.

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Modern genome annotation: The BioSapiens Network. New York: Springer, 2009.

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

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Canzar, Stefan, and Francisca Rojas Ringeling, eds. Protein-Protein Interaction Networks. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-4939-9873-9.

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Panchenko, Anna, and Teresa Przytycka, eds. Protein-protein Interactions and Networks. London: Springer London, 2008. http://dx.doi.org/10.1007/978-1-84800-125-1.

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Kasid, Usha, and Robert Clarke, eds. Cancer Gene Networks. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6539-7.

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Deplancke, Bart, and Nele Gheldof, eds. Gene Regulatory Networks. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-292-2.

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Weber, Wilfried, and Martin Fussenegger, eds. Synthetic Gene Networks. Totowa, NJ: Humana Press, 2012. http://dx.doi.org/10.1007/978-1-61779-412-4.

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Sanguinetti, Guido, and Vân Anh Huynh-Thu, eds. Gene Regulatory Networks. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8882-2.

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Book chapters on the topic "Protein and gene networks"

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Bharathavikru, Ruthrothaselvi, and Alex von Kriegsheim. "WT1-Associated Protein–Protein Interaction Networks." In The Wilms' Tumor (WT1) Gene, 189–96. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-4023-3_16.

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Benuskova, Lubica, and Nikola Kasabov. "Gene/Protein Interactions — Modeling Gene Regulatory Networks (GRN)." In Computational Neurogenetic Modeling, 137–53. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-48355-9_7.

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Westra, Ronald L., Goele Hollanders, Geert Jan Bex, Marc Gyssens, and Karl Tuyls. "The Identification of Dynamic Gene-Protein Networks." In Knowledge Discovery and Emergent Complexity in Bioinformatics, 157–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71037-0_11.

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Choi, Yunkyu, Seok Kim, Gwan-Su Yi, and Jinah Park. "Protein-Protein Interaction Network and Gene Ontology." In Future Application and Middleware Technology on e-Science, 159–69. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-1-4419-1719-5_16.

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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), 142–55. Dordrecht: 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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Chen, Gang, and Jianxin Wang. "Biological Networks-Based Analysis of Gene Expression Signatures." In Algorithmic and Artificial Intelligence Methods for Protein Bioinformatics, 495–506. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118567869.ch26.

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Erten, Sinan, Gurkan Bebek, and Mehmet Koyutürk. "Disease Gene Prioritization Based on Topological Similarity in Protein-Protein Interaction Networks." In Lecture Notes in Computer Science, 54–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20036-6_7.

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Shamir, Ron. "Gene Regulation, Protein Networks and Disease: A Computational Perspective." In Combinatorial Pattern Matching, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31265-6_1.

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Morshed, Nizamul, Madhu Chetty, and Nguyen Xuan Vinh. "FusGP: Bayesian Co-learning of Gene Regulatory Networks and Protein Interaction Networks." In Neural Information Processing, 369–77. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34500-5_44.

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Theofilatos, Konstantinos, Christos Dimitrakopoulos, Maria Antoniou, Efstratios Georgopoulos, Stergios Papadimitriou, Spiros Likothanassis, and Seferina Mavroudi. "Efficient Computational Prediction and Scoring of Human Protein-Protein Interactions Using a Novel Gene Expression Programming Methodology." In Engineering Applications of Neural Networks, 472–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32909-8_48.

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Conference papers on the topic "Protein and gene networks"

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Towfic, Fadi, M. Heather West Greenlee, and Vasant Honavar. "Detection of Gene Orthology Based on Protein-Protein Interaction Networks." In 2009 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2009. http://dx.doi.org/10.1109/bibm.2009.85.

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Yang, Shu, Lisa Pham, Lisa M. Christadore, Scott Schaus, and Eric D. Kolaczyk. "Multiscale gene sets from protein interaction networks." In 2013 IEEE Global Conference on Signal and Information Processing (GlobalSIP). IEEE, 2013. http://dx.doi.org/10.1109/globalsip.2013.6736908.

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NARIAI, N., S. KIM, S. IMOTO, and S. MIYANO. "USING PROTEIN-PROTEIN INTERACTIONS FOR REFINING GENE NETWORKS ESTIMATED FROM MICROARRAY DATA BY BAYESIAN NETWORKS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2003. http://dx.doi.org/10.1142/9789812704856_0032.

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Yao, Huaxiong, Mengxiao Cui, Wei Li, Ziwei Wang, and Yuxiang Zhu. "Correlating interactions with gene expressions to detect protein complexes in protein interaction networks." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999279.

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Young-Rae Cho, Woochang Hwang, and Aidong Zhang. "Modularization of Protein Interaction Networks by Incorporating Gene Ontology Annotations." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221228.

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Cerri, Ricardo, Rodrigo C. Barros, and Andre C. P. L. F. de Carvalho. "Hierarchical classification of Gene Ontology-based protein functions with neural networks." In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015. http://dx.doi.org/10.1109/ijcnn.2015.7280474.

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Wang, Sheng, Jianzhu Ma, Michael Ku Yu, Fan Zheng, Edward W. Huang, Jiawei Han, Jian Peng, and Trey Ideker. "Annotating gene sets by mining large literature collections with protein networks." In Pacific Symposium on Biocomputing 2018. WORLD SCIENTIFIC, 2017. http://dx.doi.org/10.1142/9789813235533_0055.

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Massanet-Vila, R., Francesc Fernandez Albert, P. Caminal, and A. Perera. "Network-based enrichment analysis of gene expression through protein-protein interaction data." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6347438.

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Smalter, Aaron, Seak Fei Lei, and Xue-wen Chen. "Human Disease-Gene Classification with Integrative Sequence-Based and Topological Features of Protein-Protein Interaction Networks." In 2007 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2007). IEEE, 2007. http://dx.doi.org/10.1109/bibm.2007.47.

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Zeng, Min, Min Li, Zhihui Fei, Fang-Xiang Wu, Yaohang Li, and Yi Pan. "A Deep Learning Framework for Identifying Essential Proteins Based on Protein-Protein Interaction Network and Gene Expression Data." In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2018. http://dx.doi.org/10.1109/bibm.2018.8621551.

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Reports on the topic "Protein and gene networks"

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Fromm, A., Avihai Danon, and Jian-Kang Zhu. Genes Controlling Calcium-Enhanced Tolerance to Salinity in Plants. United States Department of Agriculture, March 2003. http://dx.doi.org/10.32747/2003.7585201.bard.

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The specific objectives of the proposed research were to identify, clone and characterize downstream cellular target(s) of SOS3 in Arabidopsis thaliana, to analyze the Ca2+-binding characteristics of SOS3 and the sos3-1 mutant and their interactions with SOS3 cellular targets to analyze the SOS3 cell-specific expression patterns, and its subcellular localization, and to assess the in vivo role of SOS3 target protein(s) in plant tolerance to salinity stress. In the course of the study, in view of recent opportunities in identifying Ca2+ - responsive genes using microarrays, the group at Weizmann has moved into identifying Ca2+-responsive stress genes by using a combination of aqeuorin-based measurements of cytosolic Ca and analysis by DNA microarrays of early Ca-responsive genes at the whole genome level. Analysis of SOS3 (University of Arizona) revealed its expression in both roots and shoots. However, the expression of this gene is not induced by stress. This is reminiscent of other stress proteins that are regulated by post-transcriptional mechanisms such as the activation by second messengers like Ca. Further analysis of the expression of the gene using promoter - GUS fusions revealed expression in lateral root primordial. Studies at the Weizmann Institute identified a large number of genes whose expression is up-regulated by a specific cytosolic Ca burst evoked by CaM antagonists. Fewer genes were found to be down-regulated by the Ca burst. Among the up-regulated genes many are associated with early stress responses. Moreover, this study revealed a large number of newly identified Ca-responsive genes. These genes could be useful to investigate yet unknown Ca-responsive gene networks involved in plant response to stress.
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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, April 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 chromoplasts in cauliflower. Preliminary genetic study from the Israeli group revealed that the melon Or gene (CmOr) completely co-segregated with fruit flesh color in a segregating mapping population and in a wide melon germplasm collection, which set the stage for the funded research. Major conclusions and achievements include: 1). CmOris proved to be the gene that controls melon fruit flesh color and represents the previously described gflocus in melon. 2). Genetic and molecular analyses of CmOridentify and confirm a single SNP that is responsible for the orange and non-orange phenotypes in melon fruit. 3). Alteration of the evolutionarily conserved arginine in an OR protein to both histidine or alanine greatly enhances its ability to promote carotenoid accumulation. 4). OR promotes massive carotenoid accumulation due to its dual functions in regulating both chromoplast biogenesis and carotenoid biosynthesis. 5). A bulk segregant transcriptome (BSRseq) analysis identifies a list of genes associated with the CmOrregulatory network. 6). BSRseq is proved to be an effective approach for gene discovery. 7). Screening of an EMS mutation library identifies a low β mutant, which contains low level of carotenoids due to a mutation in CmOrto produce a truncated form of OR protein. 8). low β exhibits lower germination rate and slow growth under salt stress condition. 9). Postharvest storage of fruit enhances carotenoid accumulation, which is associated with chromoplast development. Our research uncovers the molecular mechanisms underlying the Or-regulated high level of carotenoid accumulation via regulating carotenoidbiosynthetic capacity and storage sink strength. The findings provide mechanistic insights into how carotenoid accumulation is controlled in plants. Our research also provides general and reliable molecular markers for melon-breeding programs to select orange varieties, and offers effective genetic tools for pro-vitamin A enrichment in other important crops via the rapidly developed genome editing technology. The newly discovered low β mutant could lead to a better understanding of the Or gene function and its association with stress response, which may explain the high conservation of the Or gene among various plant species.
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Wolf, Shmuel, and William J. Lucas. Involvement of the TMV-MP in the Control of Carbon Metabolism and Partitioning in Transgenic Plants. United States Department of Agriculture, October 1999. http://dx.doi.org/10.32747/1999.7570560.bard.

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The function of the 30-kilodalton movement protein (MP) of tobacco mosaic virus (TMV) is to facilitate cell-to-cell movement of viral progeny in infected plants. Our earlier findings have indicated that this protein has a direct effect on plasmodesmal function. In addition, these studies demonstrated that constitutive expression of the TMV MP gene (under the control of the CaMV 35S promoter) in transgenic tobacco plants significantly affects carbon metabolism in source leaves and alters the biomass distribution between the various plant organs. The long-term goal of the proposed research was to better understand the factors controlling carbon translocation in plants. The specific objectives were: A) To introduce into tobacco and potato plants a virally-encoded (TMV-MP) gene that affects plasmodesmal functioning and photosynthate partitioning under tissue-specific promoters. B) To introduce into tobacco and potato plants the TMV-MP gene under the control of promoters which are tightly repressed by the Tn10-encoded Tet repressor, to enable the expression of the protein by external application of tetracycline. C) To explore the mechanism by which the TMV-MP interacts with the endogenous control o~ carbon allocation. Data obtained in our previous project together with the results of this current study established that the TMV-MP has pleiotropic effects when expressed in transgenic tobacco plants. In addition to its ability to increase the plasmodesmal size exclusion limit, it alters carbohydrate metabolism in source leaves and dry matter partitioning between the various plant organs, Expression of the TMV-MP in various tissues of transgenic potato plants indicated that sugars and starch levels in source leaves are reduced below those of control plants when the TMV-MP is expressed in green tissue only. However, when the TMV-MP was expressed predominantly in PP and CC, sugar and starch levels were raised above those of control plants. Perhaps the most significant result obtained from experiments performed on transgenic potato plants was the discovery that the influence of the TMV-MP on carbohydrate allocation within source leaves was under developmental control and was exerted only during tuber development. The complexity of the mode by which the TMV-MP exerts its effect on the process of carbohydrate allocation was further demonstrated when transgenic tobacco plants were subjected to environmental stresses such as drought stress and nutrients deficiencies, Collectively, these studies indicated that the influence of the TMV-MP on carbon allocation L the result of protein-protein interaction within the source tissue. Based on these results, together with the findings that plasmodesmata potentiate the cell-to-cell trafficking of viral and endogenous proteins and nucleoproteins complexes, we developed the theme that at the whole plant level, the phloem serves as an information superhighway. Such a long-distance communication system may utilize a new class of signaling molecules (proteins and/or RNA) to co-ordinate photosynthesis and carbon/nitrogen metabolism in source leaves with the complex growth requirements of the plant under the prevailing environmental conditions. The discovery that expression of viral MP in plants can induce precise changes in carbon metabolism and photoassimilate allocation, now provide a conceptual foundation for future studies aimed at elucidating the communication network responsible for integrating photosynthetic productivity with resource allocation at the whole-plant level. Such information will surely provide an understanding of how plants coordinate the essential physiological functions performed by distantly-separated organs. Identification of the proteins involved in mediating and controlling cell-to-cell transport, especially at the companion cell-sieve element boundary, will provide an important first step towards achieving this goal.
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Siuzdak, Gary. Ecosystems and Networks Integrated with Genes and Molecular Assemblies (ENIGMA): Component 5: Imaging Protein Conformations, Shapes & Assemblies in Solution & Administration project (Final Scientific/Technical Report, Subcontract No. 6974584). Office of Scientific and Technical Information (OSTI), June 2021. http://dx.doi.org/10.2172/1797991.

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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, January 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 a role in enhancement of postharvest leaf senescence and for getting an insight into its regulatory mechanism/s we have applied molecular genetics and functional genomics approaches. The original objectives were: 1. Identification of dark-induced SAGs in Arabidopsis using enhancer/promoter trap lines and microarray approaches; 2. Molecular and functional characterization of the identified genes by analyzing their expression and examining the phenotypes in related knockout mutant plants; 3. Initial studies of promoter sequences for selected early dark-induced SAGs. Since genomic studies of senescence, with emphasis on dark-induced senescence, were early-on published which included information on potential regulatory genes we decided to use this new information. This is instead of using the uncharacterized enhancer/promoter trap lines as originally planned. We have also focused on specific relevant genes identified in the two laboratories. Based on the available genomic analyses of leaf senescence 10 candidate genes hypothesized to have a regulatory role in dark-induced senescence were subjected to both expression as well as functional analyses. For most of these genes senescence-specific regulation was confirmed, however, functional analyses using knock-out mutants indicated no consequence to senescence progression. The transcription factor WARK75 was found to be specifically expressed during natural and dark-induced leaf senescence. Functional analysis demonstrated that in detached leaves senescence under darkness was significantly delayed while no phenotypic consequences could be observed on growth and development, including no effect on natural leaf senescence,. Thus, WARKY75 is suggested to have a role in dark-induced senescence, but not in natural senescence. Another regulatory gene identified to have a role in senescence is MKK9 encoding for a Mitogen-Activated Protein Kinase Kinase 9 which is upregulated during senescence in harvested leaves as well as in naturally senescing leaves. MKK9 can specifically phosphorylate another kinase, MPK6. Both knockouts of MKK9 and MPK6 displayed a significantly senescence delay in harvested leaves and possibly function as a phosphorelay that regulates senescence. To our knowledge, this is the first report that clearly demonstrates the involvement of a MAP kinase pathway in senescence. This research not only revealed a new signal transduction pathway, but more important provided significant insights into the regulatory mechanisms underlying senescence in harvested leaves. In an additional line of research we have employed the promoter of the senescence-induced BFN1 gene as a handle for identifying components of the regulatory mechanism. This gene was shown to be activated during darkinduced senescence of detached leaves, as well as natural senescence. This was shown by following protein accumulation and promoter activity which demonstrated that this promoter is activated during dark-induced senescence. Analysis of the promoter established that, at least some of the regulatory sequences reside in an 80 bps long fragment of the promoter. Overall, progress was made in identification of components with a role in dark-induced senescence in this project. Further studies should be done in order to better understand the function of these components and develop approaches for modulating the progress of senescence in crop plants for the benefit of agriculture.
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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, January 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, we found that low ectopic over-expression (OEX) of SlFSM1 leads to a significant decrease in the final size of the cells in mature leaves and fruits, and the outer pericarp is substantially narrower, suggesting a role in determining cell size and shape. We also found the interacting partners of the Arabidopsis homologs of FSM1 (two, belonging to the same family), and cloned their tomato single homolog, which we named SlFSB1 (Fruit SANT/MYB–Binding1). SlFSB1 is a novel plant specific single MYB-like protein, which function was unknown. The present project aimed at elucidating the function and mode of action of these two single MYB proteins in regulating tomato fruit development. The specific objectives were: 1. Functional analysis of SlFSM1 and its interacting protein SlFSB1 in relation to fruit development. 2. Identification of the SlFSM1 and/or SlFSB1 cellular targets. The plan of work included: 1) Detailed phenotypic, histological and cellular analyses of plants ectopically expressing FSM1, and plants either ectopically over-expressing or silenced for FSB1. 2) Extensive SELEX analysis, which did not reveal any specific DNA target of SlFSM1 binding, hence the originally offered ChIP analysis was omitted. 3) Genome-wide transcriptional impact of gain- and loss- of SlFSM1 and SlFSB1 function by Affymetrix microarray analyses. This part is still in progress and therefore results are not reported, 4) Search for additional candidate partners of SlFSB1 revealed SlMYBI to be an alternative partner of FSB1, and 5) Study of the physical basis of the interaction between SlFSM1 and SlFSB1 and between FSB1 and MYBI. Major conclusions, solutions, achievements: We established that FSM1 negatively affects cell expansion, particularly of those cells with the highest potential to expand, such as the ones residing inner to the vascular bundles in the fruit pericarp. On the other hand, FSB1 which is expressed throughout fruit development acts as a positive regulator of cell expansion. It was also established that besides interacting with FSM1, FSB1 interacts also with the transcription factor MYBI, and that the formation of the FSB1-MYBI complex is competed by FSM1, which recognizes in FSB1 the same region as MYBI does. Based on these findings a model was developed explaining the role of this novel network of the three different MYB containing proteins FSM1/FSB1/MYBI in the control of tomato cell expansion, particularly during fruit development. In short, during early stages of fruit development (Phase II), the formation of the FSM1-FSB1 complex serves to restrict the expansion of the cells with the greatest expansion potential, those non-dividing cells residing in the inner mesocarp layers of the pericarp. Alternatively, during growth phase III, after transcription of FSM1 sharply declines, FSB1, possibly through complexing with the transcription factor MYBI serves as a positive regulator of the differential cell expansion which drives fruit enlargement during this phase. Additionally, a novel mechanism was revealed by which competing MYB-MYB interactions could participate in the control of gene expression. Implications, both scientific and agricultural: The demonstrated role of the FSM1/FSB1/MYBI complex in controlling differential cell growth in the developing tomato fruit highlights potential exploitations of these genes for improving fruit quality characteristics. Modulation of expression of these genes or their paralogs in other organs could serve to modify leaf and canopy architecture in various crops.
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Somerville, Ronald L. Novel Approaches to the Characterization of Specific Protein-Protein Interactions Important in Gene Expression. Fort Belvoir, VA: Defense Technical Information Center, October 1994. http://dx.doi.org/10.21236/ada300480.

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Somerville, Ronald L. Novel Approaches to the Characterization of Specific Protein-Protein Interactions Important in Gene Expression. Fort Belvoir, VA: Defense Technical Information Center, October 1995. http://dx.doi.org/10.21236/ada300572.

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Greenspan, Ralph J. Gene Networks Underlying Chronic Sleep Deprivation in Drosophila. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada610340.

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Sanders, Michael M. Regulation of the Multidrug Resistance-Associated Protein Gene by Estrogen. Fort Belvoir, VA: Defense Technical Information Center, March 2001. http://dx.doi.org/10.21236/ada410077.

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