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Academic literature on the topic 'Quantitative analysis of interactomes'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Quantitative analysis of interactomes"

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Kohli, Priyanka, Malte P. Bartram, Sandra Habbig, Caroline Pahmeyer, Tobias Lamkemeyer, Thomas Benzing, Bernhard Schermer, and Markus M. Rinschen. "Label-free quantitative proteomic analysis of the YAP/TAZ interactome." American Journal of Physiology-Cell Physiology 306, no. 9 (May 1, 2014): C805—C818. http://dx.doi.org/10.1152/ajpcell.00339.2013.

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The function of an individual protein is typically defined by protein-protein interactions orchestrating the formation of large complexes critical for a wide variety of biological processes. Over the last decade the analysis of purified protein complexes by mass spectrometry became a key technique to identify protein-protein interactions. We present a fast and straightforward approach for analyses of interacting proteins combining a Flp-in single-copy cellular integration system and single-step affinity purification with single-shot mass spectrometry analysis. We applied this protocol to the analysis of the YAP and TAZ interactome. YAP and TAZ are the downstream effectors of the mammalian Hippo tumor suppressor pathway. Our study provides comprehensive interactomes for both YAP and TAZ and does not only confirm the majority of previously described interactors but, strikingly, revealed uncharacterized interaction partners that affect YAP/TAZ TEAD-dependent transcription. Among these newly identified candidates are Rassf8, thymopoetin, and the transcription factors CCAAT/enhancer-binding protein (C/EBP)β/δ and core-binding factor subunit β (Cbfb). In addition, our data allowed insights into complex stoichiometry and uncovered discrepancies between the YAP and TAZ interactomes. Taken together, the stringent approach presented here could help to significantly sharpen the understanding of protein-protein networks.

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Hannigan, Molly M., Alyson M. Hoffman, J. Will Thompson, Tianli Zheng, and Christopher V. Nicchitta. "Quantitative Proteomics Links the LRRC59 Interactome to mRNA Translation on the ER Membrane." Molecular & Cellular Proteomics 19, no. 11 (August 11, 2020): 1826–49. http://dx.doi.org/10.1074/mcp.ra120.002228.

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Protein synthesis on the endoplasmic reticulum (ER) requires the dynamic coordination of numerous cellular components. Together, resident ER membrane proteins, cytoplasmic translation factors, and both integral membrane and cytosolic RNA-binding proteins operate in concert with membrane-associated ribosomes to facilitate ER-localized translation. Little is known, however, regarding the spatial organization of ER-localized translation. This question is of growing significance as it is now known that ER-bound ribosomes contribute to secretory, integral membrane, and cytosolic protein synthesis alike. To explore this question, we utilized quantitative proximity proteomics to identify neighboring protein networks for the candidate ribosome interactors SEC61β (subunit of the protein translocase), RPN1 (oligosaccharyltransferase subunit), SEC62 (translocation integral membrane protein), and LRRC59 (ribosome binding integral membrane protein). Biotin labeling time course studies of the four BioID reporters revealed distinct labeling patterns that intensified but only modestly diversified as a function of labeling time, suggesting that the ER membrane is organized into discrete protein interaction domains. Whereas SEC61β and RPN1 reporters identified translocon-associated networks, SEC62 and LRRC59 reporters revealed divergent protein interactomes. Notably, the SEC62 interactome is enriched in redox-linked proteins and ER luminal chaperones, with the latter likely representing proximity to an ER luminal chaperone reflux pathway. In contrast, the LRRC59 interactome is highly enriched in SRP pathway components, translation factors, and ER-localized RNA-binding proteins, uncovering a functional link between LRRC59 and mRNA translation regulation. Importantly, analysis of the LRRC59 interactome by native immunoprecipitation identified similar protein and functional enrichments. Moreover, [35S]-methionine incorporation assays revealed that siRNA silencing of LRRC59 expression reduced steady state translation levels on the ER by ca. 50%, and also impacted steady state translation levels in the cytosol compartment. Collectively, these data reveal a functional domain organization for the ER and identify a key role for LRRC59 in the organization and regulation of local translation.

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Chou, Chung-Lin, Gloria Hwang, Daniel J. Hageman, Lichy Han, Prashasti Agrawal, Trairak Pisitkun, and Mark A. Knepper. "Identification of UT-A1- and AQP2-interacting proteins in rat inner medullary collecting duct." American Journal of Physiology-Cell Physiology 314, no. 1 (January 1, 2018): C99—C117. http://dx.doi.org/10.1152/ajpcell.00082.2017.

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The urea channel UT-A1 and the water channel aquaporin-2 (AQP2) mediate vasopressin-regulated transport in the renal inner medullary collecting duct (IMCD). To identify the proteins that interact with UT-A1 and AQP2 in native rat IMCD cells, we carried out chemical cross-linking followed by detergent solubilization, immunoprecipitation, and LC-MS/MS analysis of the immunoprecipitated material. The analyses revealed 133 UT-A1-interacting proteins and 139 AQP2-interacting proteins, each identified in multiple replicates. Fifty-three proteins that were present in both the UT-A1 and the AQP2 interactomes can be considered as mediators of housekeeping interactions, likely common to all plasma membrane proteins. Among proteins unique to the UT-A1 list were those involved in posttranslational modifications: phosphorylation (protein kinases Cdc42bpb, Phkb, Camk2d, and Mtor), ubiquitylation/deubiquitylation (Uba1, Usp9x), and neddylation (Nae1 and Uba3). Among the proteins unique to the AQP2 list were several Rab proteins (Rab1a, Rab2a, Rab5b, Rab5c, Rab7a, Rab11a, Rab11b, Rab14, Rab17) involved in membrane trafficking. UT-A1 was found to interact with UT-A3, although quantitative proteomics revealed that most UT-A1 molecules in the cell are not bound to UT-A3. In vitro incubation of UT-A1 peptides with the protein kinases identified in the UT-A1 interactome revealed that all except Mtor were capable of phosphorylating known sites in UT-A1. Overall, the UT-A1 and AQP2 interactomes provide a snapshot of a dynamic process in which UT-A1 and AQP2 are produced in the rough endoplasmic reticulum, processed through the Golgi apparatus, delivered to endosomes that move into and out of the plasma membrane, and are regulated in the plasma membrane.

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Hiebel, Christof, Elisabeth Stürner, Meike Hoffmeister, Georg Tascher, Mario Schwarz, Heike Nagel, Christian Behrends, Christian Münch, and Christian Behl. "BAG3 Proteomic Signature under Proteostasis Stress." Cells 9, no. 11 (November 4, 2020): 2416. http://dx.doi.org/10.3390/cells9112416.

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The multifunctional HSP70 co-chaperone BAG3 (BCL-2-associated athanogene 3) represents a key player in the quality control of the cellular proteostasis network. In response to stress, BAG3 specifically targets aggregation-prone proteins to the perinuclear aggresome and promotes their degradation via BAG3-mediated selective macroautophagy. To adapt cellular homeostasis to stress, BAG3 modulates and functions in various cellular processes and signaling pathways. Noteworthy, dysfunction and deregulation of BAG3 and its pathway are pathophysiologically linked to myopathies, cancer, and neurodegenerative disorders. Here, we report a BAG3 proteomic signature under proteostasis stress. To elucidate the dynamic and multifunctional action of BAG3 in response to stress, we established BAG3 interactomes under basal and proteostasis stress conditions by employing affinity purification combined with quantitative mass spectrometry. In addition to the identification of novel potential BAG3 interactors, we defined proteins whose interaction with BAG3 was altered upon stress. By functional annotation and protein-protein interaction enrichment analysis of the identified potential BAG3 interactors, we confirmed the multifunctionality of BAG3 and highlighted its crucial role in diverse cellular signaling pathways and processes, ensuring cellular proteostasis and cell viability. These include protein folding and degradation, gene expression, cytoskeleton dynamics (including cell cycle and transport), as well as granulostasis, in particular.

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Sadeesh, Nithin, Mauro Scaravilli, and Leena Latonen. "Proteomic Landscape of Prostate Cancer: The View Provided by Quantitative Proteomics, Integrative Analyses, and Protein Interactomes." Cancers 13, no. 19 (September 27, 2021): 4829. http://dx.doi.org/10.3390/cancers13194829.

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Prostate cancer is the second most frequent cancer of men worldwide. While the genetic landscapes and heterogeneity of prostate cancer are relatively well-known already, methodological developments now allow for studying basic and dynamic proteomes on a large scale and in a quantitative fashion. This aids in revealing the functional output of cancer genomes. It has become evident that not all aberrations at the genetic and transcriptional level are translated to the proteome. In addition, the proteomic level contains heterogeneity, which increases as the cancer progresses from primary prostate cancer (PCa) to metastatic and castration-resistant prostate cancer (CRPC). While multiple aspects of prostate adenocarcinoma proteomes have been studied, less is known about proteomes of neuroendocrine prostate cancer (NEPC). In this review, we summarize recent developments in prostate cancer proteomics, concentrating on the proteomic landscapes of clinical prostate cancer, cell line and mouse model proteomes interrogating prostate cancer-relevant signaling and alterations, and key prostate cancer regulator interactomes, such as those of the androgen receptor (AR). Compared to genomic and transcriptomic analyses, the view provided by proteomics brings forward changes in prostate cancer metabolism, post-transcriptional RNA regulation, and post-translational protein regulatory pathways, requiring the full attention of studies in the future.

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Scifo, Enzo, Agnieszka Szwajda, Rabah Soliymani, Francesco Pezzini, Marzia Bianchi, Arvydas Dapkunas, Janusz Dębski, et al. "Quantitative analysis of PPT1 interactome in human neuroblastoma cells." Data in Brief 4 (September 2015): 207–16. http://dx.doi.org/10.1016/j.dib.2015.05.016.

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Buneeva, Olga, Arthur Kopylov, Oksana Gnedenko, Marina Medvedeva, Alexander Veselovsky, Alexis Ivanov, Victor Zgoda, and Alexei Medvedev. "Proteomic Profiling of Mouse Brain Pyruvate Kinase Binding Proteins: A Hint for Moonlighting Functions of PKM1?" International Journal of Molecular Sciences 24, no. 8 (April 21, 2023): 7634. http://dx.doi.org/10.3390/ijms24087634.

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Affinity-based proteomic profiling is widely used for the identification of proteins involved in the formation of various interactomes. Since protein–protein interactions (PPIs) reflect the role of particular proteins in the cell, identification of interaction partners for a protein of interest can reveal its function. The latter is especially important for the characterization of multifunctional proteins, which can play different roles in the cell. Pyruvate kinase (PK), a classical glycolytic enzyme catalyzing the last step of glycolysis, exists in four isoforms: PKM1, PKM2, PKL, and PKR. The enzyme isoform expressed in actively dividing cells, PKM2, exhibits many moonlighting (noncanonical) functions. In contrast to PKM2, PKM1, predominantly expressed in adult differentiated tissues, lacks well-documented moonlighting functions. However, certain evidence exists that it can also perform some functions unrelated to glycolysis. In order to evaluate protein partners, bound to PKM1, in this study we have combined affinity-based separation of mouse brain proteins with mass spectrometry identification. The highly purified PKM1 and a 32-mer synthetic peptide (PK peptide), sharing high sequence homology with the interface contact region of all PK isoforms, were used as the affinity ligands. This proteomic profiling resulted in the identification of specific and common proteins bound to both affinity ligands. Quantitative affinity binding to the affinity ligands of selected identified proteins was validated using a surface plasmon resonance (SPR) biosensor. Bioinformatic analysis has shown that the identified proteins, bound to both full-length PKM1 and the PK peptide, form a protein network (interactome). Some of these interactions are relevant for the moonlighting functions of PKM1. The proteomic dataset is available via ProteomeXchange with the identifier PXD041321.

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Velásquez-Zapata, Valeria, J. Mitch Elmore, Sagnik Banerjee, Karin S. Dorman, and Roger P. Wise. "Next-generation yeast-two-hybrid analysis with Y2H-SCORES identifies novel interactors of the MLA immune receptor." PLOS Computational Biology 17, no. 4 (April 2, 2021): e1008890. http://dx.doi.org/10.1371/journal.pcbi.1008890.

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Protein-protein interaction networks are one of the most effective representations of cellular behavior. In order to build these models, high-throughput techniques are required. Next-generation interaction screening (NGIS) protocols that combine yeast two-hybrid (Y2H) with deep sequencing are promising approaches to generate interactome networks in any organism. However, challenges remain to mining reliable information from these screens and thus, limit its broader implementation. Here, we present a computational framework, designated Y2H-SCORES, for analyzing high-throughput Y2H screens. Y2H-SCORES considers key aspects of NGIS experimental design and important characteristics of the resulting data that distinguish it from RNA-seq expression datasets. Three quantitative ranking scores were implemented to identify interacting partners, comprising: 1) significant enrichment under selection for positive interactions, 2) degree of interaction specificity among multi-bait comparisons, and 3) selection of in-frame interactors. Using simulation and an empirical dataset, we provide a quantitative assessment to predict interacting partners under a wide range of experimental scenarios, facilitating independent confirmation by one-to-one bait-prey tests. Simulation of Y2H-NGIS enabled us to identify conditions that maximize detection of true interactors, which can be achieved with protocols such as prey library normalization, maintenance of larger culture volumes and replication of experimental treatments. Y2H-SCORES can be implemented in different yeast-based interaction screenings, with an equivalent or superior performance than existing methods. Proof-of-concept was demonstrated by discovery and validation of novel interactions between the barley nucleotide-binding leucine-rich repeat (NLR) immune receptor MLA6, and fourteen proteins, including those that function in signaling, transcriptional regulation, and intracellular trafficking.

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Serrao, Simone, Cristina Contini, Giulia Guadalupi, Alessandra Olianas, Greca Lai, Irene Messana, Massimo Castagnola, et al. "Salivary Cystatin D Interactome in Patients with Systemic Mastocytosis: An Exploratory Study." International Journal of Molecular Sciences 24, no. 19 (September 27, 2023): 14613. http://dx.doi.org/10.3390/ijms241914613.

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Mastocytosis, a rare blood disorder characterized by the proliferation of clonal abnormal mast cells, has a variegated clinical spectrum and diagnosis is often difficult and delayed. Recently we proposed the cathepsin inhibitor cystatin D-R26 as a salivary candidate biomarker of systemic mastocytosis (SM). Its C26 variant is able to form multiprotein complexes (mPCs) and since protein–protein interactions (PPIs) are crucial for studying disease pathogenesis, potential markers, and therapeutic targets, we aimed to define the protein composition of the salivary cystatin D-C26 interactome associated with SM. An exploratory affinity purification-mass spectrometry method was applied on pooled salivary samples from SM patients, SM patient subgroups with and without cutaneous symptoms (SM+C and SM−C), and healthy controls (Ctrls). Interactors specifically detected in Ctrls were found to be implicated in networks associated with cell and tissue homeostasis, innate system, endopeptidase regulation, and antimicrobial protection. Interactors distinctive of SM−C patients participate to PPI networks related to glucose metabolism, protein S-nitrosylation, antibacterial humoral response, and neutrophil degranulation, while interactors specific to SM+C were mainly associated with epithelial and keratinocyte differentiation, cytoskeleton rearrangement, and immune response pathways. Proteins sensitive to redox changes, as well as proteins with immunomodulatory properties and activating mast cells, were identified in patients; many of them were involved directly in cytoskeleton rearrangement, a process crucial for mast cell activation. Although preliminary, these results demonstrate that PPI alterations of the cystatin D-C26 interactome are associated with SM and provide a basis for future investigations based on quantitative proteomic analysis and immune validation.

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Narushima, Yuta, Hiroko Kozuka-Hata, Kouhei Tsumoto, Jun-Ichiro Inoue, and Masaaki Oyama. "Quantitative phosphoproteomics-based molecular network description for high-resolution kinase-substrate interactome analysis." Bioinformatics 32, no. 14 (March 24, 2016): 2083–88. http://dx.doi.org/10.1093/bioinformatics/btw164.

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Dissertations / Theses on the topic "Quantitative analysis of interactomes"

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Puchalska, Monika [Verfasser], and Gerhard [Akademischer Betreuer] Mittler. "Quantitative proteomic analysis of the interactome of mammalian S/MAR (scaffold/matrix attachment region) elements." Freiburg : Universität, 2018. http://d-nb.info/1216826447/34.

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Jané, Palli Pau. "Quantification des affinités PBM/PDZ et de leurs sites modulateurs par des approches expérimentales et informatiques à haut débit." Electronic Thesis or Diss., Strasbourg, 2020. http://www.theses.fr/2020STRAJ051.

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Ce travail a porté sur les domaines PDZ, une famille de domaines globulaires reconnaissant des motifs de liaison aux PDZ (appelés PBM, pour ‘PDZ-Binding Motifs’) généralement situés à l'extrémité C-terminale de leurs protéines partenaires. Les réseaux domaines-motifs sont souvent modulés par des modifications post-traductionnelles réversibles (PTM). Nous avons utilisé des PBM synthétiques simulant différentes conditions: motifs sauvages de diverses longueurs, acétylés, phosphorylés ou portant des mutations ‘imitant’ les PTM. Ces peptides ont été utilisés pour des études d'interaction à l'aide du test ‘Hold-Up’, un test développé à l'origine dans notre laboratoire. Nous avons évalué l'impact de diverses modifications des complexes PBM/PDZ, qui conduisent à un changement global de leur capacité de liaison du PDZ. Ces résultats fournissent des informations quantitatives sur l'effet biologique que de telles modifications pourraient avoir dans le contexte des protéines entières
This thesis focuses on PDZ domains, a family of globular domains that bind to conserved PDZ-Binding Motifs (called henceforth PBMs) generally situated at the extreme C-terminus of their partner proteins. Domain-motif networks are often modulated by reversible post-translational modifications (PTMs). We used synthetized PBMs to reproduce different conditions, such as a wild-type, acetylation or phosphorylation, addition of extra exosites or residue mimication of PTM in the literature. These peptides were used for interaction studies using the holdup assay, an assay originally developed in our laboratory. We evaluated the impact of diverse modifications of the PBM/PDZ interactions, which led to a global change of the PDZ-binding capability. These results provided quantitative information on the biological effects that such modifications may have in the context of full-length proteins

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Franchin, Cinzia. "Mass Spectrometry-Based Quantitative Proteomics to Study Proteomes, Phosphoproteomes and Interactomes." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422169.

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Over the past few years, mass spectrometry-based proteomics has been widely applied to the most diverse fields of biochemistry, biomedicine and biology, and several approaches have been developed to allow absolute and relative quantification of proteins in very complex mixtures. During my PhD, I have conducted three main studies, taking advantage of different quantitative proteomics techniques. In particular SILAC and iTRAQ approaches have been exploited to investigate the calcification process induced by endotoxin in clonal interstitial aortic valve cells, while SILAC and label-free quantitative approaches have been exploited to identify new potential interacting partners and substrates of the protein kinase CK2. Calcific aortic valve disease represents the most common type of valvular disease and the first cause of surgical valve replacement in the industrialized world. No medical therapies are available to prevent or slow down calcium deposition within the valve leaflets, therefore surgery is the only possible treatment. To investigate the molecular mechanisms underlying the calcification process, SILAC and iTRAQ proteomics approaches have been applied to bovine interstitial aortic valve cells, a cellular model that is able to acquire a pro-calcific profile and drive matrix mineralization upon treatment with an inflammatory stimulus like the endotoxin lipopolysaccharide (LPS). The application to the same cellular model of two different quantitative technologies, led to the identification and relative quantification of hundreds of proteins, among which many showed a significant alteration in response to LPS. The acquired data suggest that cellular oxidoreductase activity, cytoskeletal and spliceosome regulation, glycolisis/gluconeogenesis, and arginine metabolism are altered during the acquisition of the pro-calcific profile. These results represent a starting point to investigate more in detail the molecular mechanisms that seem to be strongly involved in the calcification process induced by LPS. The other projects described in this thesis focus on CK2, an essential, constitutively active and highly pleiotropic protein kinase. CK2, like many other kinases, is strongly involved in several cellular processes, and in particular it has been hypothesized that this enzyme plays a crucial role in the transduction of survival signals. However, a clear comprehension of the multiple roles played by this kinase within the cell has not been achieved. The aim of these projects was the identification of interacting partners and substrates of CK2 by means of proteomics approaches to try to shed some light on the functions performed by this kinase. In particular a combination of immunoprecipitation experiments and label-free quantitative analyses has been performed to identify new potential interacting partners of CK2, while the SILAC technology, in combination with the use of a specific and potent inhibitor of CK2, was exploited to identify new putative substrates of this kinase directly in a cellular system. The results obtained confirm the notion that CK2 plays a role in many fundamental cellular functions and clearly indicate a strong involvement of this kinase in the biological processes of protein biosynthesis and degradation. Moreover interesting aspects linked to phosphorylation/dephosphorylation turnover rates emerged from these analyses. A detailed discussion, from a technical and biological point of view, of the data collected is presented. Finally, during my PhD I also collaborated to a project aiming at the identification of the primary molecular targets of antimicrobial photodynamic therapy. This work, not discussed in the thesis, has recently been submitted to the “Journal of Proteomics” with the title: “Molecular Targets of Antimicrobial Photodynamic Therapy Identified by a Proteomic Approach”.
Negli ultimi anni, la ricerca proteomica basata sulla spettrometria di massa è stata applicata in modo esponenziale ai più diversi campi della biochimica, biomedicina e biologia, permettendo il parallelo sviluppo di nuovi approcci per la quantificazione relativa e assoluta delle proteine. Nel corso del mio dottorato, ho seguito lo sviluppo di tre progetti principali, sfruttando diverse tecniche di spettrometria quantitativa. In particolare, le tecnologie SILAC e iTRAQ sono state applicate allo studio del processo di calcificazione delle cellule interstiziali delle valvole aortiche, mentre i metodi SILAC e di quantificazione label-free sono stati sfruttati per l’identificazione di potenziali interattori e substrati della protein chinasi CK2. La calcificazione delle valvole aortiche è una delle più comuni patologie valvolari e prima causa di sostituzione valvolare nei paesi industrializzati. A oggi sfortunatamente non esistono terapie che possano prevenire o curare la deposizione di calcio nelle valvole aortiche, e l’unica soluzione è l’intervento chirurgico. Per chiarire le basi molecolari di questo processo, abbiamo applicato le metodiche SILAC e iTRAQ ad un modello cellulare basato su cellule valvolari cardiache bovine (BVIC), in grado di acquisire un profilo pro-calcifico e favorire la mineralizzazione della matrice extra-cellulare in risposta ad uno stimolo infiammatorio come l’endotossina lipopolisaccaride (LPS). L’utilizzo di due diverse tecnologie allo stesso modello cellulare ha permesso l’identificazione, e la relativa quantificazione, di centinaia di proteine, parecchie delle quali mostrano una significativa alterazione in risposta al trattamento con LPS. L’analisi dei dati ha infatti rivelato l’alterazione di proteine appartenenti a diversi processi cellulari, quali la regolazione del citoscheletro, dei meccanismi ossidoriduttivi e dello spliceosoma, la via metabolica della glicolisi/gluconeogenesi, e il metabolismo dell’arginina, suggerendo il coinvolgimento di queste vie nel fenomeno della calcificazione delle valvole aortiche. Questi risultati rappresentano perciò un punto di partenza per nuovi dettagliati studi dei meccanismi molecolari alla base della calcificazione valvolare indotta da LPS. Gli altri progetti descritti in questa tesi sono focalizzato su CK2, una protein chinasi essenziale, altamente pleiotroica e costitutivamente attiva, fortemente implicata in una moltitudine di processi cellulari, in particolare nella trasduzione dei segnali di sopravvivenza, per la quale sembra giocare un ruolo chiave. Tuttavia una completa comprensione del ruolo che CK2 ricopre nei vari processi cellulari in cui è implicata non è ancora stata raggiunta, perciò questo lavoro ha come scopo l’identificazione di nuovi potenziali interattori e substrati di CK2, allo scopo di chiarire maggiormente la sua funzione all’interno della cellula. Nello specifico, abbiamo abbinato esperimenti d’immunoprecipitazione e analisi quantitativa label-free per lo studio delle proteine che interagiscono con CK2, mentre la tecnologia SILAC combinata con l’uso di un inibitore potente e specifico di CK2 è stata applicata alla ricerca di nuovi potenziali substrati di questa chinasi direttamente in un sistema cellulare. I risultati ottenuti confermano le conoscenze già note riguardo al coinvolgimento di CK2 in diversi processi essenziali per la vita cellulare, e fanno emergere chiaramente un coinvolgimento di primo piano di CK2 nei processi di biosintesi e degradazione proteica. Inoltre, l’analisi dei dati ha anche rivelato interessanti ed inattesi aspetti del turnover di fosforilazione/defosforilazione di proteine fosforilate da CK2. I dati ottenuti sono dettagliatamente presentati in questa tesi, da un punto di vista sia tecnico che biologico. Infine, durante il dottorato ho anche collaborato alla realizzazione di un progetto volto all’identificazione di bersagli molecolari nella terapia fotodinamica antimicrobica, utilizzando un approccio proteomico. Da questa collaborazione, è nato un lavoro (non descritto in questa tesi) che è stato recentemente sottoposto a “Journal of Proteomics” con il titolo: “Molecular Targets of Antimicrobial Photodynamic Therapy Identified by a Proteomic Approach”.

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Kühnle, Tim. "Quantitative Analysis of Human Chronotypes." Diss., lmu, 2006. http://nbn-resolving.de/urn:nbn:de:bvb:19-51686.

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Bhabuta, Madhu Darshan Kumar. "Quantitative analysis of ATM networks." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299444.

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Owen, Christopher Grant. "Quantitative analysis of conjunctival vasculature." Thesis, Queen's University Belfast, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287628.

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Doupé, David Patrick. "Quantitative analysis of epithelial homeostasis." Thesis, University of Cambridge, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.611770.

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Zeng, Wen. "Quantitative analysis of distributed systems." Thesis, University of Newcastle upon Tyne, 2014. http://hdl.handle.net/10443/2638.

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Computing Science addresses the security of real-life systems by using various security-oriented technologies (e.g., access control solutions and resource allocation strategies). These security technologies signficantly increase the operational costs of the organizations in which systems are deployed, due to the highly dynamic, mobile and resource-constrained environments. As a result, the problem of designing user-friendly, secure and high efficiency information systems in such complex environment has become a major challenge for the developers. In this thesis, firstly, new formal models are proposed to analyse the secure information flow in cloud computing systems. Then, the opacity of work flows in cloud computing systems is investigated, a threat model is built for cloud computing systems, and the information leakage in such system is analysed. This study can help cloud service providers and cloud subscribers to analyse the risks they take with the security of their assets and to make security related decision. Secondly, a procedure is established to quantitatively evaluate the costs and benefits of implementing information security technologies. In this study, a formal system model for data resources in a dynamic environment is proposed, which focuses on the location of different classes of data resources as well as the users. Using such a model, the concurrent and probabilistic behaviour of the system can be analysed. Furthermore, efficient solutions are provided for the implementation of information security system based on queueing theory and stochastic Petri nets. This part of research can help information security officers to make well judged information security investment decisions.

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Dong, Zhiyuan. "Three Essays in Quantitative Analysis." University of Cincinnati / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1282048935.

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Narendra, Koneru. "Quantitative analysis of domain testing effectiveness /." Adobe Acrobat .pdf file, requires Adobe Acrobat Reader software, 2001. http://etd-submit.etsu.edu/etd/theses/available/etd-0404101-011933/unrestricted/koneru0427.pdf.

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Books on the topic "Quantitative analysis of interactomes"

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Alekseev, V. Quantitative analysis. 2nd ed. Moscow: Mir, 1985.

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R, Stansfield, and Chartered Association of Certified Accountants., eds. Quantitative analysis. London: Longman Group UK in co-operation with the Chartered Association of Certified Accountants, 1989.

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Emile, Woolf, Tanna Suresh, and Karam Singh, eds. Quantitative analysis. Plymouth, [Eng.]: Macdonald & Evans, 1985.

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Day, R. A. Quantitative analysis. 6th ed. Englewood Cliffs, N.J: Prentice-Hall, 1991.

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Ltd, Brierley Price Prior, and Chartered Association of Certified Accountants., eds. Quantitative analysis. London: Brierley Price Prior, 1988.

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Accountants, Chartered Associationof Certified, and Brierley Price Prior Ltd, eds. Quantitative analysis. London: BPP Publishing, 1987.

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1924-, Underwood A. L., ed. Quantitative analysis. 6th ed. Englewood Cliffs, N.J: Prentice Hall, 1991.

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1924-, Underwood A. L., ed. Quantitative analysis. 5th ed. Englewood Cliffs, N.J: Prentice-Hall, 1986.

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Li, Na. Quantitative chemical analysis. Beiing: Peking University Press, 2009.

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Harris, Daniel C. Quantitative chemical analysis. 3rd ed. New York: W.H. Freeman, 1991.

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Book chapters on the topic "Quantitative analysis of interactomes"

1

Farage, Enoir, and Patrick T. Caswell. "Quantitative Analysis of Integrin Trafficking." In The Integrin Interactome, 251–63. New York, NY: Springer US, 2020. http://dx.doi.org/10.1007/978-1-0716-0962-0_14.

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Dani, Diksha, and Norbert A. Dencher. "Native DIGE for Quantitative and Functional Analysis of Protein Interactomes." In Methods in Molecular Biology, 53–69. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-2831-7_4.

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Friedel, Caroline C. "Computational Analysis of Virus–Host Interactomes." In Methods in Molecular Biology, 115–30. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-601-6_8.

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Nabieva, Elena, and Mona Singh. "Protein Function Prediction via Analysis of Interactomes." In Prediction of Protein Structures, Functions, and Interactions, 231–58. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/9780470741894.ch10.

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Zhao, Yang, and Kunhong Xiao. "Proteomic Analysis of the β-Arrestin Interactomes." In Beta-Arrestins, 217–32. New York, NY: Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9158-7_14.

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Bandemer, Hans, and Wolfgang NÄther. "Quantitative analysis." In Fuzzy Data Analysis, 185–239. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-011-2506-2_6.

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Wilberg, Jörg. "Quantitative Analysis." In Codesign for Real-Time Video Applications, 43–92. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6081-4_4.

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Krieg, Andreas. "Quantitative Analysis." In Motivations for Humanitarian intervention, 123–32. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-5374-7_4.

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Recker, Jan. "Quantitative Analysis." In Evaluations of Process Modeling Grammars, 92–145. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18360-7_5.

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Sarantakos, Sotirios. "Quantitative analysis." In Social Research, 404–38. London: Macmillan Education UK, 2013. http://dx.doi.org/10.1007/978-1-137-29247-6_16.

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Conference papers on the topic "Quantitative analysis of interactomes"

1

Zaheer, Saad, Sabaoon Zeb, and Faisal F. Khan. "Intelligent analysis of methylation data in Head and Neck Squamous Cell Carcinoma (HNSCC) interactomes." In 2021 International Conference on Artificial Intelligence (ICAI). IEEE, 2021. http://dx.doi.org/10.1109/icai52203.2021.9445265.

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Chatterjee, Krishnendu, Andreas Pavlogiannis, and Yaron Velner. "Quantitative Interprocedural Analysis." In POPL '15: The 42nd Annual ACM SIGPLAN-SIGACT Symposium on Principles of Programming Languages. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2676726.2676968.

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Kelly, Michael C., Charles M. Skidmore, and Raymond D. Cotton. "Quantitative AVO analysis." In SEG Technical Program Expanded Abstracts 2005. Society of Exploration Geophysicists, 2005. http://dx.doi.org/10.1190/1.2144319.

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Dogandžić, Aleksandar. "Bayesian Defect Signal Analysis." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2006. http://dx.doi.org/10.1063/1.2184584.

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Klovning, Jorunn, and Espen Fyhn Nilsen. "Quantitative Environmental Risk Analysis." In SPE Annual Technical Conference and Exhibition. Society of Petroleum Engineers, 1995. http://dx.doi.org/10.2118/30686-ms.

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Rauch, B. J., Shun-Tien Lin, and Robert E. Rowlands. "Quantitative thermoelastic stress analysis." In SPIE's 1993 International Symposium on Optics, Imaging, and Instrumentation, edited by Michael T. Valley, Nancy K. Del Grande, and Albert S. Kobayashi. SPIE, 1993. http://dx.doi.org/10.1117/12.163840.

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Lazaridis, Kosmas. "Pulsar Nulling Quantitative Analysis." In RECENT ADVANCES IN ASTRONOMY AND ASTROPHYSICS: 7th International Conference of the Hellenic Astronomical Society. AIP, 2006. http://dx.doi.org/10.1063/1.2347995.

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Sarker, Laboni. "Quantitative Symbolic Similarity Analysis." In ISSTA '23: 32nd ACM SIGSOFT International Symposium on Software Testing and Analysis. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3597926.3605238.

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Cronk, R. Jason, and Stuart S. Shapiro. "Quantitative Privacy Risk Analysis." In 2021 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). IEEE, 2021. http://dx.doi.org/10.1109/eurospw54576.2021.00043.

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Chiang, Chih-Hung. "Statistical analysis of ultrasonic measurements in concrete." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2002. http://dx.doi.org/10.1063/1.1472938.

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Reports on the topic "Quantitative analysis of interactomes"

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Helms, J. Quantitative Risk Analysis. Office of Scientific and Technical Information (OSTI), February 2017. http://dx.doi.org/10.2172/1345342.

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Cooper, Russell, and Alok Johri. Dynamic Complementarities: A Quantitative Analysis. Cambridge, MA: National Bureau of Economic Research, July 1996. http://dx.doi.org/10.3386/w5691.

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Nastev, M., M. M. Savard, D. Paradis, R. Lefebvre, and M. Ross. Quantitative analysis of groundwater resources. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2013. http://dx.doi.org/10.4095/292547.

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Waller, Christopher J., Randall Wright, and S. Borağan Aruoba. Money and Capital: A Quantitative Analysis. Federal Reserve Bank of St. Louis, 2009. http://dx.doi.org/10.20955/wp.2009.031.

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Sunderland, Daniel, Eric D. Vugrin, and Russell Chris Camphouse. Quantitative resilience analysis through control design. Office of Scientific and Technical Information (OSTI), September 2009. http://dx.doi.org/10.2172/993905.

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Bocola, Luigi, and Alessandro Dovis. Self-Fulfilling Debt Crises: A Quantitative Analysis. Cambridge, MA: National Bureau of Economic Research, September 2016. http://dx.doi.org/10.3386/w22694.

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Bagwell, Kyle, Robert Staiger, and Ali Yurukoglu. Quantitative Analysis of Multi-Party Tariff Negotiations. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24273.

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Rousseau, R. M. Quantitative XRF analysis using the fundamental algorithm. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/193288.

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J. Philliber, B. Antoun, B. Somerday, and N. Yang. Materials characterization through quantitative digital image analysis. Office of Scientific and Technical Information (OSTI), July 2000. http://dx.doi.org/10.2172/758329.

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e Castro, Miguel Faria. A Quantitative Analysis of the Countercyclical Capital Buffer. Federal Reserve Bank of St. Louis, 2019. http://dx.doi.org/10.20955/wp.2019.008.

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