Systems Biology Approaches for Host-Pathogen Interaction Analysis

Application and utilization of data science approaches has revolutionized scientific research including host-pathogen interaction analyses. Host-pathogen interactions are generally considered highly specific interactions, resulting in a variety of consequences. Data science approaches coupled with network biology has taken host-pathogen interaction analysis from specific interaction to a new paradigm of understanding the consequences of these interactions within a biological network. Unfortunately, basic biological researchers are mostly unaware of these advancements. Conversely, data scientists are not familiar with biological aspects of such data. Systems Biology Approaches for Host-Pathogen Interaction Analyses benefits biological researchers by expanding the scope of their research and utilization of their accumulated data using recent technological advancements. In addition, the book also opens avenues for bioinformatics and computer science researchers to utilize their expertise in biologically meaningful ways. . Cover approaches to decipher complex multiple host-pathogen interactions. Gives biological researcher an insight into the utilization of technological advancements in the field of host-pathogen interaction analyses in their work. Provides a new paradigm of understanding the consequences of host-pathogen interaction in biological systems INDICE: List of contributorsForewordPrefaceAcknowledgmentsChapter 1: Host-pathogen interactions: a general introductionRabbani Syed, Fahad M. Aldakheel, Shatha A. Alduraywish, Ayesha Mateen, Hadeel Alnajran and Huda Hussain Al-Numan1.1 Introduction1.1.1 Role of pathogen1.1.2 Host-pathogen relationship and mechanisms1.1.3 Classification of host-pathogen interactions1.2 Methods for prediction of host-pathogen interactions1.2.1 Ortholog-based protein interaction detection1.2.2 Domain-based detection of protein interaction1.2.3 Biological reasoning-based prediction of host-pathogen interactions1.2.4 Domain/motif interaction-based predictions1.2.5 Machine learning-based predictions of host-pathogen interactions1.3 Online repositories for host-pathogen interactions1.3.1 Database of fungal virulence factors1.3.2 E-fungi1.3.3 Fungi DB1.3.4 Ensembl genomes1.3.5 EuPathDB1.3.6 HPIDB1.3.7 PLEXdb1.3.8 VFDB1.4 ConclusionAcknowledgmentReferencesChapter 2: Host-pathogen interactions: databases and approaches for data generationYasmin Bano and Abhinav Shrivastava2.1 Introduction2.2 Databases for host-pathogen interactions2.3 Bioinformatic methods to discover HPI networking2.3.1 Biological methods2.3.2 Computational methods2.4 Microscopic imaging techniques as stage of the art2.5 RNA-Seq profiling: tool for determining the HPI network2.5.1 Bacteria-host interactions2.5.2 Virus-host interactions2.5.3 Fungus-host interaction and other pathogenic interactions2.5.4 Technical approach of RNA-seq and data analysis2.6 Artificial intelligence-driven analysis for HPIs2.7 Challenges and opportunities2.7.1 Challenges2.7.2 Opportunities2.8 ConclusionReferencesChapter 3: Generation of host-pathogen interaction data: an overview of recent technological advancementsFatima Noor, Usman Ali Ashfaq, Hafiz Rameez Khalid and Mohsin Khurshid3.1 Introduction3.2 Introduction of bioinformatics in light of NGS3.3 A short glimpse of the “integration of omics”3.4 Why is multiomics study preferred over single omics?3.5 Advancements in the generation of host-pathogen interaction data3.5.1 Biological big data and omics3.5.2 Multiomics approaches to unravel the host-pathogen interactions3.6 Bioinformatics resources and web-based databases for host-pathogen interactions3.7 Challenges in the generation of host-pathogen interaction data3.8 Discussion and future prospects3.9 ConclusionReferencesChapter 4: Molecular omics: a promising systems biology approach to unravel host-pathogen interactionsSamman Munir, Usman Ali Ashfaq, Muhammad Qasim, Tazeem Fatima, Sehar Aslam, Muhammad Hassan Sarfraz, A.K.M. Humayun Kober and Mohsin Khurshid4.1 Introduction4.2 Genomics approaches4.3 Transcriptomics4.4 Proteomics of host-pathogen interactions4.4.1 Secretomics of host-pathogen interactions4.5 Metabolomics approaches4.5.1 Lipidomics approaches4.5.2 Multiomics integration for the analysis of host-microbe interactions4.5.3 Integrated transcriptomicsgenomics approaches4.5.4 Integrated epigenomics and transcriptomics approaches4.5.5 Integrated proteomics-genomics, transcriptomics, and metabolomics4.6 Future perspectivesReferencesChapter 5: Computational methods for detection of host-pathogen interactionsSamvedna Singh, Himanshi Gupta and Shakti Sahi5.1 Introduction5.2 Computational techniques for prediction of host-pathogen interactions5.2.1 Protein-protein interaction methods5.2.2 RNA-mediated interaction-based method5.2.3 Computational approaches using integrated pipelines5.3 Case studies5.3.1 Case study based on host-parasite interaction5.3.2 Case study based on host-virus interaction5.3.3 Case study based on host-bacteria interactions5.3.4 Case study based on host-fungus interactions5.4 DiscussionReferencesFurther readingChapter 6: Biological interaction networks and their application for microbial pathogenesisNirupma Singh and Sonika Bhatnagar6.1 Introduction: biological networks6.1.1 What is a network?6.1.2 Types of networks6.1.3 Biological networks6.1.4 Properties of biological networks6.1.5 Host-pathogen interaction networks6.2 Tools for construction and analysis of biological networks6.2.1 Cytoscape6.2.2 R studio6.2.3 Important plugins and functions6.3 Functional annotation and biological characterization of host and microbial proteins6.3.1 DAVID6.3.2 KOBAS 3.0 server6.3.3 Biocyc6.4 Ontology and pathway analysis to understand microbial pathogenesis6.4.1 KEGG pathways6.4.2 Wiki pathways6.4.3 NCBI biosystems6.5 Case study: host-pathogen interaction networks for CVD pathways in microbial diseases6.6 ConclusionReferencesChapter 7: Dual transcriptomics data and detection of host-pathogen interactionsVahap Eldem, Yusuf Ula¸s C¸inar, Selahattin Bari¸s C¸ ay, Selim Can Kuralay, O¨zgecan Kayalar, Go¨kmen Zararsiz, Yakup Bakir and Fatih Dikmen7.1 Introduction7.2 Unraveling host-pathogen interactions via genome-wide dual RNA-Seq7.3 Best practices in dual RNA-Seq: from experimental design to a step-wise guide to performing bioinformatic analysis7.4 Challenges of dual RNA-Seq experiments and data analysis7.5 Dual RNA-Seq in the era of third-generation sequencing7.6 Dissecting the role of noncoding RNA in host-pathogen interactions using dual transcriptomic data7.7 Future perspectivesAcknowledgmentsReferencesChapter 8: Functional overrepresentation analysis and their application in microbial pathogenesisShilpa Kumari, Neha Verma, Anil Kumar, Sunita Dalal and Kanu Priya8.1 Introduction8.2 Analysis via different databases8.2.1 GO enrichment analysis/GO functional overrepresentation analysis8.2.2 Functional overrepresentation analysis using DOSE (Disease Ontology Semantic and Enrichment Analysis)8.2.3 Functional overrepresentation analysis using MeSH8.2.4 Functional overrepresentation analysis using Reactome pathway (ReactomePA)8.3 Application of statistical databases in microbial pathogenesisReferencesChapter 9: Advancements in systems biology-based analysis of microbial pathogenesisNeha Verma, Shilpa Kumari, Anil Kumar and Kanu Priya9.1 Introduction of microbial pathogenesis9.1.1 Mechanism of microbial pathogenesis9.2 Systems biology of microbial pathogenesis9.2.1 Host-pathogen interaction9.2.2 Pathogen’s molecular interaction network9.2.3 Host’s reaction to a microbial infection9.3 Systems biology techniques to study microbial pathogenesis9.3.1 OMICS data contributing to microbial pathogenesis (including genomics, transcriptomics, metabolomics, and proteomics)9.3.2 Computational biology of host-pathogen interaction in microbial pathogenesis9.3.3 High-throughput techniques9.4 ConclusionReferencesChapter 10: Host-pathogen interactions with special reference to microbiota analysis and integration of systems biology approachesFahad M. Aldakheel, Dalia Mohsen and Barkha Singhal10.1 Introduction10.2 Methods for identifying the microbiota: a brief account10.3 Factors to be considered before identifying microbiota10.3.1 Geographical factors and diet10.4 Role of next-generation sequencing technologies for microbial community analysis in understanding host-pathogen interactions10.5 Microbial community analysis for understanding the antibiotic resistance phenomenon through 16S sequencing10.6 Gut microbiota analysis in COVID-19 through 16S metagenomic sequencing10.7 Challenges and advantages of using systems biology in microbiota analysis10.8 Pathogen-host interactions in bioinformatics10.9 Systems biology and omics data10.10 PHI and systems biology10.11 ConclusionAcknowledgmentReferencesChapter 11: Role of noncoding RNAs in host-pathogen interactions: a systems biology approachKartavya Mathur, Ananya Gupta, Varun Rawat, Vineet Sharma and Shailendra Shakya11.1 Introduction11.2 Exploring different forms of noncoding RNAs11.2.1 microRNAs11.2.2 Long noncoding RNAs11.2.3 Piwi-like RNAs11.2.4 Small interfering RNA11.2.5 Small nuclear RNA11.2.6 Small nucleolar RNA11.2.7 Ribonucleic acid enzymes (or ribozymes)11.2.8 Circular RNAs11.2.9 Competing endogenous RNA11.3 Comprehending the role of ncRNAs in pathogen-host interplay11.3.1 Role of ncRNAs in bacterial pathogenesis11.3.2 Function of noncoding RNAs in viral infection11.3.3 Role of ncRNAs in fungal pathogenesis11.3.4 Role of ncRNAs in protozoan pathogenesis11.3.5 Role of ncRNAs in helminth pathogenesis11.4 Why is it important to study the function of ncRNAs?11.5 RNA systems biology11.6 Computational resources for identifying ncRNAs in host pathogenesis11.6.1 ncRNA expression profiling11.6.2 Functional annotation and interpretation of ncRNA transcriptome11.6.3 ncRNA web resources11.6.4 Predicting ncRNA function11.6.5 Methods for predicting and investigating microRNA targets11.6.6 Estimating interaction events of ncRNAs11.6.7 Predicting ncRNA structure11.6.8 Graph-based approaches for ncRNA structure and function prediction11.7 How to explore the significance of miRNAs in infection development?11.7.1 Mathematical modeling for comprehending host-pathogen interaction11.8 Why network analysis is important for studying ncRNAs?11.8.1 Network analysis to study the regulation of host-pathogen interaction11.8.2 Predicting ncRNA-disease association and tripartite network11.8.3 Corelational network analysis for ncRNAs11.8.4 Competing endogenous RNA network analysisReferencesChapter 12: Systems biology in food industry: applications in food production, engineering, and pathogen detectionAnanya Srivastava and Anuradha Mishra12.1 Introduction12.2 Networks used in systems biology12.2.1 Gene regulatory networks12.2.2 Signal transduction networks12.2.3 Protein-protein interaction networks12.2.4 Metabolic networks12.3 Systems biology benefits for food production12.3.1 Applied systems biology in nutrition and health12.3.2 Systems biology in food production and processing12.3.3 Biofortification and development of nutraceuticals12.3.4 Systems biology in food safety and quality12.4 Systems biology in foodborne pathogen detection12.4.1 Pathogen detection techniques used in food sectors12.4.2 Limitations12.5 Future scope12.6 ConclusionReferencesAuthor indexSubject index

• ISBN: 978-0-323-95890-5
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 316
• Fecha Publicación: 12/02/2024
• Nº Volúmenes: 1
• Idioma: Inglés