01040nam 2200265la 450 991048255990332120221108051508.0(UK-CbPIL)2090323353(CKB)5500000000093575(EXLCZ)99550000000009357520210618d1578 uy |laturcn||||a|bb|De iridis generatione, loco, coloribus, numero et figura inqvisitio optica, autore Georgio Christophoro Dibuadio[electronic resource]Copenhagen Andreas Gutterwitz1578Online resource ([20] bl.)Reproduction of original in Det Kongelige Bibliotek / The Royal Library (Copenhagen).Dybvad Georg Christoph1549-1612.854819Dybvad Georg Christoph1549-1612.854819Uk-CbPILUk-CbPILBOOK9910482559903321De iridis generatione, loco, coloribus, numero et figura inqvisitio optica, autore Georgio Christophoro Dibuadio2107348UNINA04554nam 2200433z- 450 991016164840332120231214132932.0(CKB)3710000001041980(oapen)https://directory.doabooks.org/handle/20.500.12854/43710(EXLCZ)99371000000104198020202102d2016 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierComputational Systems Biology of Pathogen-Host InteractionsFrontiers Media SA20161 electronic resource (198 p.)Frontiers Research Topics2-88919-821-9 A thorough understanding of pathogenic microorganisms and their interactions with host organisms is crucial to prevent infectious threats due to the fact that Pathogen-Host Interactions (PHIs) have critical roles in initiating and sustaining infections. Therefore, the analysis of infection mechanisms through PHIs is indispensable to identify diagnostic biomarkers and next-generation drug targets and then to develop strategic novel solutions against drug-resistance and for personalized therapy. Traditional approaches are limited in capturing mechanisms of infection since they investigate hosts or pathogens individually. On the other hand, the systems biology approach focuses on the whole PHI system, and is more promising in capturing infection mechanisms. Here, we bring together studies on the below listed sections to present the current picture of the research on Computational Systems Biology of Pathogen-Host Interactions: - Computational Inference of PHI Networks using Omics Data - Computational Prediction of PHIs - Text Mining of PHI Data from the Literature - Mathematical Modeling and Bioinformatic Analysis of PHIs Computational Inference of PHI Networks using Omics Data Gene regulatory, metabolic and protein-protein networks of PHI systems are crucial for a thorough understanding of infection mechanisms. Great advances in molecular biology and biotechnology have allowed the production of related omics data experimentally. Many computational methods are emerging to infer molecular interaction networks of PHI systems from the corresponding omics data. Computational Prediction of PHIs Due to the lack of experimentally-found PHI data, many computational methods have been developed for the prediction of pathogen-host protein-protein interactions. Despite being emerging, currently available experimental PHI data are far from complete for a systems view of infection mechanisms through PHIs. Therefore, computational methods are the main tools to predict new PHIs. To this end, the development of new computational methods is of great interest. Text Mining of PHI Data from Literature Despite the recent development of many PHI-specific databases, most data relevant to PHIs are still buried in the biomedical literature, which demands for the use of text mining techniques to unravel PHIs hidden in the literature. Only some rare efforts have been performed to achieve this aim. Therefore, the development of novel text mining methods specific for PHI data retrieval is of key importance for efficient use of the available literature. Mathematical Modeling and Bioinformatic Analysis of PHIs After the reconstruction of PHI networks experimentally and/or computationally, their mathematical modeling and detailed computational analysis is required using bioinformatics tools to get insights on infection mechanisms. Bioinformatics methods are increasingly applied to analyze the increasing amount of experimentally-found and computationally-predicted PHI data. Acknowledgements: We, editors of this e-book, acknowledge Emrah Nikerel (Yeditepe University, Turkey) and Arzucan Özgür (Bogaaziçi University, Turkey) for their contributions during the initiation of the Research Topic.Image-based Systems BiologyNetwork InferenceOMICS dataComputational Biologybioinformaticsprotein-protein interactiontext miningConstraint-based modelinggene regulatory networkpathogen-host interactionReinhard Guthkeauth1291859Saliha DurmusauthTunahan Cak?rauthBOOK9910161648403321Computational Systems Biology of Pathogen-Host Interactions3022009UNINA