\r\n\tApplied and basic studies - Field studies and lab assays of fungicides can be discussed. We also look for examples of application methods, which may include timing of application, tools for application, fungicide compatibility, phytotoxicity, etc. Field trials have to have at least two years of data;
\r\n\tAdaptation of Integrated Plant Disease Management - How the IPM practice has been adapted in the field. Application of disease risk models, or use of fungicide application aids, which can be hardware or software. The introduction of a new tool for growers can also be included;
\r\n\tNovel fungicides - In addition to the traditional chemical approach, alternative materials (enzymes, oils, extracts, etc.), biological control agents, or plant defense activators can be discussed;
\r\n\tAdaptation of new technologies - Examples will be the use of unmanned vehicles, sensor technologies, advanced sprayers, or disease forecast systems for precision agriculture;
\r\n\tFungicide resistance - Unfortunately, we cannot ignore the fact that fungicide-resistant strains are widespread. Documentation of fungicide-resistant strains, the introduction of new technologies and methods can be discussed.
Biological data can be described as molecular sequence information and “wet-bench” experimented content of genome and gene product analyses [1]. Being an interdisciplinary branch of the life sciences, bioinformatics targets to develop methodology and analysis tools to explore large volumes of biological data, helping to store, organize, systematize, annotate, visualize, query, mine, understand, and interpret complex data volumes. It uses conventional, modern computer science and cloud computing, statistics, and mathematics, as well as pattern recognition, reconstruction, machine learning, simulation and iterative approaches, and molecular modeling/folding algorithms [1, 2]. The emergence and advances of the bioinformatics field, however, are tightly associated with the computerized programming and software developments needed for the handling and structural and functional analysis of large volumes of molecular sequences of DNA, RNA, proteins, and metabolites.
\nPresently, although still core for genomics and genetics field, bioinformatics became an umbrella for wider range of biological studies analyzing variety types of biological data, structuring, systemizing, annotating, querying, mining, and visualizing available biological information and a variety of biomedical text records [1–3]. Although drawing a fine line between bioinformatics and some other related fields is difficult because of increased applications of computers, statistics, and mathematics to scientific problem solving and experiments of life sciences, there should not be a misperception about bioinformatics description and objectives. Bioinformatics should not be mixed with, for example, biometry and biostatistics, development of DNA computers, or computerized generation and filing of data from imaging.
\nBioinformatics also should be differentiated from related scientific fields such as biological computation and computational biology [1, 2]. Biological computation aims to develop biological computers using advances of bioengineering, cybernetics, robotics, and molecular cell biology. In contrast, bioinformatics develops and utilizes computational algorithms to understand and interpret biological processes based on genome-derived molecular sequences and their interactions [2]. Therefore, in many aspects, bioinformatics seems similar to computational biology objectives. A computational biology is concentrated on building and/or developing theoretical models for biological analyses [1, 2], whereas bioinformatics focuses on providing practical tools to organize and analyze basic genomic, proteomic and other “omics” data, including sequence analysis and its visualization [1, 2]. Admittedly, computational biology and bioinformatics both target to use genome data, for example, multiple sequence alignments and/or genome assembly tools. This makes distinctive boundaries of these two fields less distinguishable if their theoretical and practical scales are forgotten [2]. Thus, as mentioned above, the common core aims of bioinformatics are to handle, analyze, and interpret the genome-derived molecular sequence data and its organizational principles in broad scales/spectra of comparative, simulative, and evolutionary/phylogenetics perspectives. These tools are applicable and widely used for studies related to genetics, genomics, biochemistry, physiology, biophysics, all agricultural, medical, and environmental sciences as well as evolution, system biology, and artificial intelligence [1–10].
\nFor instance, bioinformatics tools such as the comparative analysis of genomic and genetic data and/or signal processing help to interpret and understand the molecular and evolutionary processes [9] and interactions from large volumes of raw data in the field of wet-bench experimental molecular biology [1, 2]. In the “omics” fields, it helps to sequence and annotate genomes, and identify distinct patterns, mutation profiles, genetic epistasis, gene/protein expression and regulation, and gene ontologies [1, 2, 4, 8–11] as well as be instrumental in mining and querying the biological data and biomedical literature text [3, 4, 7]. When applied for system biology [2, 6], bioinformatics is a key instrument to analyze and catalogue the biochemical/genetic pathways and networks, which helps to integrate pieces of analyzed information to depict and model a full picture of the life processes. Application of reconstruction, pattern recognition, folding, simulation, and molecular modeling with bioinformatic tools can identify structural peculiarities and interactions of molecular sequences important for structural biology and medicinal drug design [12, 13]. All of these large scale, genome-derived, molecular sequence analyses of raw “Big Data” are impossible to be analyzed manually [1, 2]. This prompted the biology science research community to apply interdisciplinary methods and tools for “Big Data” analysis in combination with modern computing knowledge, which resulted in the emergence of novel interdisciplinary bioinformatics science. Let us, first, take a look the historic developments in the bioinformatics field.
\nBioinformatics term was coined by Paulien Hogeweg and Ben Hesper in 1970 [2, 14]. Its meaning was very different from current description and referred to the study of information processes in biotic systems like biochemistry and biophysics [14–16]. However, the emergence of bioinformatics tracks back to the 1960s. It was appeared in concordance with the development of protein sequencing methods from a variety of organisms and with the availability of protein sequences after Frederick Sanger determined the sequence of insulin in the early 1950s [17, 18]. New computer methods to analyze and compare a large number of protein sequences of different organisms were needed because handling many amino acid sequences manually was impractical. This led in compiling the first “Protein Information Resources” (PIR) [1, 19, 20] by Margaret Oakley Dayhoff and her collaborators at the National Biomedical Research Foundation [1]. Dayhoff\'s team successfully organized the protein sequences into distinct groups and sub-groups based on sequence similarity and percent accepted mutation (PAM) matrices [1]. This was published as protein sequences atlas [21, 22] that has been widely used in performing protein sequence alignments and database similarity searches [1, 2, 23]. This was pioneered methods of protein sequence alignment and molecular evolution [22]. In the 1970s, Elvin A. Kabat further contributed to bioinformatics development by his extended protein sequence analysis of comprehensive volumes of antibody sequences, released in collaboration with Tai Te Wu between 1980 and 1991 [2, 24].
\nWith the objective of providing the theoretical background to immunology experiments in 1974, George Bell and colleagues initiated the collection of DNA sequences into GenBank [1]. During 1982–1992, the first version of GenBank was prepared by Walter Goad\'s group [1] and the efforts resulted in the development of presently known and widely used DNA sequence databases of GenBank [25], “The European Molecular Biology Laboratory (EMBL) [26], and DNA DataBank of Japan (DDBJ) [27] in 1979, 1980, and 1984, respectively [1]. Most important development in DNA sequence databases, however, was incorporation of web-based searching algorithms allowing researchers to find and compare the target DNA sequences. Such first developments and resulting computer software called “GENEINFO” and its derivative version of “Entrez” were developed by David Benson and David Lipman and colleagues [1]. This software allowed researchers to rapidly search database-indexed sequences and match them with queried sequence. Software became readily available through web-based interface of the National Center of Biotechnology Information (NCBI) database [28]. Molecular sequence analysis, comparison, and visualization methods have been improved, and many different methodologies have been contributed to bioinformatics advancements in this direction. Such advancements can be exemplified by the development of dot matrix and diagram methods [29], alignment of sequences by dynamic programming [30], finding of local alignments between sequences [31], multiple sequence alignment tools [32–35], predicting the secondary structures of RNAs [36, 37], determination of evolutionary relationships of sequences [38, 39], and assigning the gene function based on sequence similarity of known function from models [40]. Development of FASTA [41, 42], BLAST [43, 44], and their various modifications [45–47] has further powered the bioinformatics field and greatly improved the biological data analysis. Development of tools for predicting the putative protein sequences, structure, and function of proteins/genes based on DNA sequences [48–58], completing full genome sequences, and building web-based genome databases for many prokaryotic and eukaryotic organisms [58] has provided a great advance in the bioinformatics field. In addition, rapid genome-wide gene expression profiling and analysis opportunities [59–62], biological pathway assignment and identification, data storing, and mining and querying for large volume of biological datasets [63–73] have further provided unprecedented popularity of bioinformatics in the scene of world science, which has been briefly reviewed below.
\nDynamics of bioinformatics-related publications over the past four decades. (A) Unquoted and (B) quoted keyword retrieved scientific publications from PubMed [
Since its emergence as an interdisciplinary scientific field in 1970, bioinformatics research has continuously increased over the past four-decade period. Unquoted search of keyword of bioinformatics in the PubMed database [74] has found nearly 181,000 scientific publications covering the period of 1958 to March of 2016. Repeating the search with the quoted keyword found 62,402 scientific publications over the four-decade period, demonstrating the starting point of increased publication efforts in the end of 1990s with its first raise in 2000/2001, following significant peaks in 2003/2004 and after 2013 (Figure 1). In this introductory chapter, I aim to give a brief highlight of these four-decade developments introducing the chapters presented in this book.
\nRapid and reliable determination of DNA molecules, because of the introduction of the sequencing technique of Sanger and Coulson [75] and Maxam and Gilbert [76], provided large-scale DNA sequence data that needed to be analyzed by computerized programming. This prompted the development of efficient bioinformatics methodologies. For example, a seminal effort of the Phage Φ-X174 [2, 77] and the
Therefore, without bioinformatics tools, it is not possible to think about genome sequencing as present bioinformatics programs such as BLAST/sequence alignments not only provide rapid practical tools to handle, analyze, compare, relate, and visualize DNA sequences but also offer help with the sequencing process itself. The development of cost-effective, next generation sequencing (NGS) platforms [79, 80] has helped to completely decode nearly the entire genome of many different organisms including human and many other model and specialty organisms, or crop genomes with complex polyploidy levels within a short period. For example, according to the listings in the Genomes OnLine Database (GOLD) as of March 8, 2016, there were 79,650 genome sequencing projects of which 8018 were completed projects, 33,489 were permanent drafts, 35,609 were incomplete projects, and 1553 were targeted projects [81]. There are 73,000 organism, including archaea (1201), bacteria (55,303), eukaryotes (11,990), and viruses (4473), listed for sequencing. These numbers should be increased if the sequencing of the 100,000 whole-human genomes [82] is added.
\nBioinformatics tools are needed in annotation and prediction of genes from sequenced genomes that requires computerized approaches because genomes are large to be manually annotated as mentioned above. Bioinformatics-based gene finding and annotation including a search for protein-coding genes, RNA transcripts, and other functional sequences within a genome is possible because there are patterns to recognize the start, stop regions, introns, exons, motifs, repeats, and other regulatory and sensory as well as signaling regions with some variations between genes and among organisms. With the availability and need for analysis of
Bioinformatics tools are very important to analyze gene and protein expression profiles. Large-scale sequencing of cDNA libraries has generated large volumes of serial analysis of gene expression (SAGE), expressed sequences tags (ESTs), massively parallel signature sequencing (MPSS), transcriptome profiling, or RNA-Seq, and various applications of multiplexed in-situ hybridization (microarray) profile data [83–95]. All of these gene expression techniques are extremely noise-prone and/or subject to bias in the biological measurement, which requires application of statistical tools to separate signal from noise in high-throughput gene expression studies. In this context, chapter by Zhao et al. in this book reviews and discusses the main tools and algorithms currently available for RNAseq data analyses, discussing rapidly evolving RNAseq technologies such as stranded RNAseq, targeted RNAseq, and single cell RNA-seq. Moreover, Sripathy et al. have comprehensively discussed transcriptome profiling, RNAseq, and micro-RNA expression studies in cotton (
Similarly, protein microarrays and high-throughput mass spectrometry require bioinformatics analysis to identify proteins through the complex sequence similarity searches using protein sequence databases [96–103]. Bioinformatics is a great help for analysis of gene regulation through searching and comparing the sequence motifs related to promoters and other regulatory elements. Using bioinformatics tools and sequence motifs/regulatory elements genes can be clustered by function, and the co-expression characteristics can be determined. Examples of such bioinformatics tools include k-means clustering, hierarchical clustering, and consensus clustering methods such as the Bi-CoPaM, and self-organizing maps (SOMs) that can identify functionally active sequences from very complex microarray datasets [104–107]
\nNot only just these, bioinformatics plays a major role in data collection of the functional elements of sequenced genomes that use the next-generation DNA-sequencing technologies and genomic tiling arrays. This is best exemplified “Encyclopedia of DNA Elements (ENCODE)” [108] project developed by the National Human Genome Research Institute that describes the functional elements of the human genome. Thanks to bioinformatics and applications of its tools, genomes and genes, and protein sequences of different organisms can be rapidly compared, searched, and interpreted. In addition, mutations can be identified that help to judge and diagnose many complex human and plant diseases, crop traits, and interpret complex evolutionary process, such as genome duplications, polyploidization, adaptation, and speciation.
\nOne of the widely used applications of bioinformatics is identification of three-dimensional protein structures, molecular modeling, and folding to predict the possible function of proteins or other molecular structures, model behavior of molecules, fold the molecule to its native biologically functional three-dimensional structure, and design biomedical drugs for many complex human diseases. It helps
From the coding DNA sequences, the primary structure of proteins can be easily determined that is vital in understanding the function of the protein(s). Further, based on homology patterns in primary structure of proteins and using homology modeling, important structural formations and interaction sites with other proteins can be determined. This helps to predict reliably the structure of a protein based on known structure of a homologous protein(s). Moreover, the identification of secondary, tertiary, and quaternary structures of proteins is very important to understand the function of proteins. The exact three-dimensional structure is essential for correct function, and a failure to fold into native structure generally produces inactive proteins or misfolded proteins that can be toxic [108]. Bioinformatics of protein folding includes (1) energy landscape of protein folding and (2) modeling of protein folding approaches [12, 13, 109].
\nOne of the freely available and leading web server/stand-alone software tools for automated protein structure prediction and structure-based functional annotation can be exemplified by the “Iterative Threading ASSEmbly Refinement”(I-TASSER), which “first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement” [110]. Using the I-TASSER, all above-mentioned functional and structural characteristics of proteins, including ligand-binding sites, enzyme commission number, and gene ontology terms can be explored in a comparative scale [110, 111].
\nMolecular modeling through molecular mechanistic and/or the quantum chemistry approaches is the key bioinformatics approaches to study the behavior of molecules. These are routinely used to investigate the structure, dynamics, surface properties, and thermodynamics of inorganic, biological and polymeric systems. It helps to explore conformational changes associated with biomolecular function, and molecular recognition of proteins, and membrane complexes. The protein folding, identification of catalysis sites of enzymes, and protein stability can be studied using molecular modeling. Vast different bioinformatics tools for modeling of biomolecules and designing are available [110–112]. In this book, the chapter by Leong et al. presents bioinformatics modeling and tools for biological membranes using molecular dynamic simulations, all-atom, united-atom, and coarse-grained membrane models of lipids and proteins. In addition, in this book, by Filntisi et al. a computational method for the generation of antibody-drug through site-specific cysteine conjugation using structural prediction methods based on PDB files of a drug, linker, and antibody. Moreover, Bórquez and González-Billault have presented an interesting chapter on computational algorithms of predicting kinase-substrate relationships in protein kinases; this chapter compares prediction tools and methods and discusses improving substrate prediction with contextual information.
\nWatts and Strogatz in 1998 [113, 114] and Barabási and Albert in 1999 [115–117] fueled the opinion that complex systems can be viewed as networks where components can be represented as nodes and they are linked through their interactions (i.e., edges). The properties of nodes and edges form the network topology. This approach has widely been applied to many scientific fields including bioinformatics that resulted in construction of large-scale biological networks denoted as “omes” like biome, interactome, microbiome [2, 6].
\nAbove highlighted molecular sequence analysis, prediction and annotation, and molecular modeling-related bioinformatics approaches are also the core for building, organizing, and systematizing biological networks of molecules (e.g., metabolic, protein-protein interactions, etc.), and genetic and biochemical pathways of complex cellular processes. These include reception, signal transduction, and gene regulation and gene co-expression. Such molecular networks integrate many different data types including DNA sequences, regulatory RNA, proteins, secondary metabolites, gene expression data, and other small molecules, which may be all connected physically and functionally. The construction and organization of such physically and functionally connected molecular networks of cellular processes can be achieved only by applying the combination of simulative, iterative, and model-oriented bioinformatics approaches. Such biological networks are useful to analyze and visualize the complex connections of these cellular processes, helping understand other biological networks such as neuronal networks, food webs, between/within-species interaction networks, which are the central component of modern system biology [2, 6]. Examples of “omes”-related networks are the Kyoto Encyclopedia of Genes and Genomes (KEGG), BioCyc database collection, BRaunschweig ENzyme DAtabase (BRENDA), Reactome, Comparative Toxicogenomics Database, and many other [118] biological networks. Some biological network databases and their utilization in plant genomics/epigenomics have been discussed by the chapters of Sripathi et al. and Rahman et al. in this book.
\nAn organized collection of data is referred to as database that aims to collect schemes, tables, queries, reports, images, and other objects. An access to information in the databases is provided by an integrated set of computer software, which is referred to as a “database management system” (DBMS) [119]. The DBMS allows users to access all of the data contained in the databases. It has general functions for data definition, entry, storage, update, administration, and retrieval of large quantities of information in an organized way that requires modeling (hierarchical and network models), clustering, query languages and query optimization, and visualization algorithms [1, 2, 119].
\nDevelopment of databases, therefore, is significantly dependent on bioinformatics tools, advances, research, and applications. There is a large number of different types of databases available, which cover all aspects of biological data storage and organization. Some aforementioned databases such as GenBank, EMBL, DDJB belong to primary nucleotide sequence databases. There are meta-databases that incorporate data compiled from multiple other databases such as Entrez, mGen, Metascape, etc. Some others are specialized databases such as those specific to an organism, for example, TAIR, the p53 Knowledgebase (p53), the plant alternative splicing database (PASD); the plant secretome, and subcellular proteome knowledgebase (PlantSecKB) [119]. All databases vary in their data definition, usage, format, and access types. In this book, the chapter by Kadam et al. specifically describes databases and bioinformatics algorithms related to allergen informatics, discussing the concepts of allergen bioinformatics and the key areas for potential development in the allergology, whereas Bell and Kramvis highlight public sequence database for Hepatitis B virus. In this book, readers can find a comprehensive discussion for bioinformatics resources, including databases for plant “omics,” written by Rahman et al.
\nAs mentioned above, astronomical accumulation of genomic and proteomic as well as metabolomic data, and their expression profiles and annotation, storage, organization, systematization, and integration into biological networks as well as database systems and their wide utilization by the science research community
The main driving forces for the current and future development of bioinformatics software and tools have been made on the past-decade advances of genome decoding technologies, accumulation of large volume biological data, consequent need for their analyses, as well as advancements of computer technologies, graphics, visualization, and molecular modeling and networking techniques. Moreover, the availability of various open-source codes, shared object models, and community-supported plug-ins has facilitated gathering innovative ideas from the community and performing innovative
Development of sharing models and web access tools is also an important bioinformatics objective that allows users to utilize and access bioinformatics tools over the internet and from their computer systems to the main computing resources via servers in other parts of the world. Simple Object Access Protocol (SOAP) [123] and Representational State Transfer (REST) [124–126] are two bioinformatics tools to provide web services. SOAP is a standard-based web service access protocol, originally developed by Microsoft. REST, providing very simple web service access, has been developed to fix the problems with SOAP [127]. Both tools share similarities over the HTTP protocol and have its own issues and challenges, differ in messaging patterns, rules, architecture style, and flexibility. The main advantages derive from the fact that end users do not have to deal with software and database maintenance overheads [127].
\nThere are several basic bioinformatics services, for example, “Sequence Search Services” (SSS), “Multiple Sequence Alignment” (MSA), and “Biological Sequence Analysis” (BSA) [2, 128]. These web service-based bioinformatics analysis resources represent a collection of standalone or web-based interface data analysis tools as well as integrative, distributed, and extensible bioinformatics workflow management systems (BWMS). The BWMSs are designed specifically to compose and execute a series of interactive computational or data manipulation steps (i.e., a workflow) in a bioinformatics analyses. Such systems provide interactive analysis of biological data, build the specific workflows for the analysis, enable the visualization of the analysis outputs in real time, and simplify the process of sharing and reusing workflows between scientists. Some of the platforms giving this service: Galaxy, UGENE, Taverna, etc. [2, 121]. Several chapters of this book cover bioinformatics software, web-based analysis tools, and bioinformatics services for membrane analysis (see Leong et al.), in plant science and crop genomics (see chapters by Rahman et al. and Sripathi et al.), medicine, viral genome analysis and drug design (see chapters by Younis et al., Bell and Kramvis, and Filntisi et al.).
\nPart of objectives in bioinformatics research and application is the utilization of computational algorithms and bioinformatics tools to collect, organize, and structure the growing body of biomedical literature allowing scientists to query, mine, read, and synthesize the specific literature and published articles of their research interest [2–4, 7, 129, 130]. Biomedical literature and text mining, therefore, are very important for scientific development, innovations, and integration and application of discoveries to society through extracting information (EI) and assessing the relationships of publications [3, 4]. Analysis of world literature demonstrates that more than 80% of text data remain unstructured that what makes it challenging to read every paper, resulting in disjointed sub-fields of research [3]. Biomedical literature text mining uses a variety of “text mining & data mining” tools, applying techniques such as data clustering, visualization and navigation, information retrieval, and extraction, and text categorization and summarization [3]. The use of IE and “Natural Language Generation and Understanding” (NLG and NLU) that have tokenizing, morphological or lexical, and syntactic analysis components helps to build structured text, and extract, collect, organize structured information [129, 130]. Pattern recognition and matching such as the recognition of biological abbreviations, terms, and interactions are important methods in text mining [2–4].
\nAdvances of life sciences and high-throughput biology fields in particular “omics” disciplines, the scale, and complexity of “Big Data,” and growing demand for specialists with multilingual and cross-field expertise to understand and solve multidisciplinary scientific concepts and tasks underlie a great need for training and education in the field of bioinformatics. Bioinformatics training and education aim to create, collect, deliver, and share educational and training materials and techniques as well as develop university degree-program curricula on bioinformatics. This is to prepare scientists and specialists, who can utilize modern bioinformatics tools with the sophisticated operating systems, software and algorithms, and database/networking technologies to handle, analyze, interpret, and publish high-throughput complex biological data. This is a great bottleneck and critical need of current life sciences and bioinformatics field, especially in all developing countries, for example, analyzed by some recent reports for African [82] and Central American [131] countries.
\nTo address this, bioinformatics research community has put specific efforts to develop local and global platforms for bioinformatics training and education. Such examples include “Bioinformatics Training Network” (BTN) [132] and “The Global Organization for Bioinformatics Learning, Education, and Training” (GOBLET) [133] that provide a community educational and training resource for bioinformatics trainers and trainees. As an outcome of European 7th Framework grant, BTN targeted to develop and share educational materials, short courses, and training delivery methods as well as discuss the challenge, issues, and needed requirements for bioinformatics training [132]. Furthermore, GOBLET continues similar efforts beyond Europe, aiming to coordinate efforts at the global scales with concentrated strategy and within the frame of single, dedicated foundation although it requires much time, focused strategic efforts, and modern innovative approaches [133].
\n“The Swiss Institute of Bioinformatics” training portal [134] also provides online courses for software platforms designed to teach bioinformatics concepts and methods including Rosalind [135]. There are open-access website videos and slides from the “Canadian Bioinformatics Workshops” [136]. Similarly, many different, large bioinformatics conferences, and seminars contribute for training and education on bioinformatics such as Intelligent Systems for Molecular Biology (ISMB), European Conference on Computational Biology (ECCB), Research in Computational Molecular Biology (RECOMB), and the annual Bioinformatics Open Source Conference (BOSC) of the non-profit Open Bioinformatics Foundation [2, 128]. As public bioinformatics databases, the MediaWiki engine with the WikiOpener extension, extensively referenced in this chapter, also contributes for training and education of bioinformatics through gathering research materials and descriptions of tools that can be accessed and updated by all experts in the field [128].
\nWith the specific objectives to develop bioinformatics research and application, its integration to genomics research, and training and education as well as to prepare well-qualified new generation scientists to life sciences, we established a dedicated organization—Center of Genomics and Bioinformatics in the developing country Uzbekistan [137]. As in other developing countries, there are many challenges and limitations in funding and in accessing to sophisticated bioinformatics tools and computer operating systems as well as lack of sufficient experience to carry bioinformatics research and resource development. However, our first step goal is to integrate genomics and bioinformatics curricula to the higher education system of Uzbekistan, develop training and educational materials, provide basic training and research practices to the university students and biology field specialists, and establish international collaborations on this direction. The long-term objective is to efficiently and broadly apply genomics and bioinformatics approaches to all areas of life sciences in national and regional levels that would contribute the development of biological sciences in Central Asia. Some efforts are ongoing regarding the establishment of international collaborations [138] and providing training and education in both national and regional levels.
\nBioinformatics has become an essential interdisciplinary scientific field to the life science helping to “omics” field and technologies and mainly handling and analyzing “omes” data. Accumulation of high-throughput biological data due to the technological advances in “omics” fields required and prioritized the use of bioinformatics resources, and research and application for the analysis of complex and even further enlarging “Big Data” volumes, which would be impractical and useless without bioinformatics. Therefore, as highlighted herein, there is a critical need for the preparation of well-qualified, new generation scientists with integrated knowledge, multilingual ability, and cross-field experience who are capable of using sophisticated operating systems, software and algorithms, and database/networking technologies to handle, analyze, and interpret high-throughput and increasing volume of complex biological data.
\nCommunity resources and a globally coordinated foundation of bioinformatics training and education platforms as well as research conferences, workshops, short online training, and web-based educational courses and materials are available to accomplish toward this goal. However, there is an urgent need for the development of bioinformatics education and training, in particular in developing countries, which requires innovative platforms, training techniques, better funding, web and network access, and high-performance computing systems.
\nIn the research side, bioinformatics tools need to be improved for analysis of the growing body of high-throughput pangenomics, metagenomics, proteomics, and metabolomics data. There are needs for “effective tools” to perform better genome assembly and annotation with high accuracy; however, it requires the improvement of quality of sequenced genomes without gaps, and sequencing of more genome representatives, sub-genomes, polyploidy species, genomes of single cells, and specific tissues that would generate information to work, modify, and correct bioinformatics algorithms and programming approaches.
\nThe use of third generation sequencing approaches and platforms as well as efforts on whole genome sequencing of, for example, 1000 or 100,000 human genome representatives [82] or transcriptome/exon sequencing of 1000 distinct plant species (e.g., 1KP) [139] will ultimately improve and advance the bioinformatics analysis tools. These efforts also help to improve orthologous gene identification tools that currently need attention [120]. There is a great need for sampling and handling diverse strains in pangenomic analysis, integration of prokaryotic genome-organization frameworks (GOFs) as well as integration of non-coding RNAs, pseudogenes, and epigenetics elements into the bioinformatics annotation and ontology tools and software [120]. There is a need to make sequenced genome data more functional and integrated through the construction of more organized, user friendly, cell-wide biological networks, and metabolic pathways [140] with better visualization effects, graphics outputs [120], and knowledge base construction (KB) [141]. This, however, requires the development of real-time imaging systems and high throughput phenotyping (referred to as “phenomics”) tools that would help for efficiently determining biologically meaningful associations between genomic and phenotypic data, advancing the translational sciences, personal genomics, and personalized medicine [7] and/or agriculture [142].
\nI thank Academy of Sciences of Uzbekistan and Committee for Coordination Science and Technology Development of Uzbekistan, the Office of International Research Programs (OIRP) of the United States Department of Agriculture (USDA)—Agricultural Research Service (ARS) and U.S. Civilian Research & Development Foundation (CRDF) for research Grants FA-F5-T030, FA-A6-T081, FA-A6-T085, I-2015-6-15/2, I5-FQ-0-89-870, P120, P120A, P121, P121B, UZB-TA-31016, UZB-TA-31017, and UZB-TA-2992, which have been the key factors for development of plant genomics and bioinformatics in Uzbekistan. I greatly acknowledge the Uzbekistan government support and investments/guide from Academy of Sciences of Uzbekistan, Ministry of Agriculture and Water Resources of Uzbekistan, Cotton Industry Joint Stock Company of Uzbekistan, Ministry Foreign Economic Relations, Investments and Trade of Uzbekistan, USDA-ARS, and Texas A&M University for establishment of Center of Genomics and Bioinformatics in Uzbekistan. I also thank Prof. Gilbert S. Omenn, Center for Computational Medicine & Bioinformatics, University of Michigan, USA for critical reading of this introductory chapter, and Mr. Mirzakamol Ayubov and Mr. Muhammad Mirzahmedov, Center of Genomics and Bioinformatics, Uzbekistan, for their technical assistance while preparing this chapter material.
\nSleep is fundamental for sports performance, as well as for emotional regulation and development of the physical and mental health of athletes. In fact, inadequate sleep (e.g., reduced sleep duration and quality) may lead to an increased risk of injury and illness in athletes.
In recent years, growing interest in understanding the sleep of athletes has seen an increase in published studies [1]. In fact, athletes and coaches have ranked sleep as the most important recovery strategy [2]. Interestingly, the fundamental difference between recovery interventions with established protocols (e.g., cold water immersion, compression garments, electrical stimulation) [3] and sleeping lies in the fact that sleep initiation does not depend entirely on the willingness of the athlete [4].
During sleep, anabolic metabolism is upregulated [5], procedural memories are consolidated [6], and immune responses are augmented [7]. However, sleep loss or deprivation can have significant effects on performance, motivation, perception of effort, and cognition as well as numerous other biological functions [8]. Furthermore, sleep is associated with many physiological processes that may facilitate recovery from, and adaptation to, athletic training and competition [9]. Studies have analyzed the importance of sleep to regulate key molecular mechanisms (i.e., transcriptional regulatory proteins [10, 11, 12]), demonstrating that sleep has an integral role in metabolic homeostasis [13]. The capacity of humans to cope with physiological and psychological stressors is fundamental to athletic performance outcomes [14] and may be influenced by numerous factors, such as experience, fitness, motivation, and the normal fluctuation of physiological and behavioral procedures across a 24-h period (i.e., sleep–wake cycle, body temperature, hormone regulation) [15].
Importantly, the circadian rhythms are mainly controlled by the suprachiasmatic nucleus within the hypothalamus [16]. However, the suprachiasmatic nucleus is unable to continuously sustain control over these patterns (i.e., between the suprachiasmatic nucleus within the hypothalamus), as humans are extremely sensitive to changes in their normal environment [16, 17], most notably through the light–dark cycle [18]. When athletes face disturbances to their environments (e.g., training and/or competing close to bedtime sleep and travel), endogenous circadian rhythms and normal sleep-wake cycles can become desynchronized [16, 19]. These perturbations in sleeping patterns can cause an increase in homeostatic pressure and affect emotional regulation, core temperature, and circulating levels of melatonin, causing a delay in sleep onset [20].
Additionally, there is potential for sleep loss and neurocognitive and physiological performance to be compromised [9, 21, 22, 23]. Emerging research suggests that there are differences in sleep duration and quality between athletes and healthy controls. In contrast to non-athletes, athletes are often exposed to conditions that can interfere with sleep duration and quality, such as jet lag, unfamiliar sleeping environments, evening training, and/or competition and underlying fatigue [24].
In this sense, sleep monitoring has become a common practice in sport, and, in athletes, it may be useful to identify those who may need an intervention in terms of sleep disorders. Consequently, it is necessary to identify atypical patterns in the sleep and wakefulness of athletes and provide adequate sleep hygiene strategies to avoid disturbances in sleep duration and quality. Efficient and noninvasive methods and equipment, such as actigraphy and other alternatives to polysomnography, can provide detailed information about sleep and wakefulness during the sporting season.
Although there is high availability of information regarding the duration and quality of sleep in different age groups in the general population, information available in the scientific literature about sleep in athletes is still scarce. However, sleep is currently recognized as one of the essential components in the recovery from fatigue and, consequently, in the performance of athletes. Thus, it is essential that athletes, coaches, and clinicians understand the factors that can affect sleep, as well as realizing the usefulness of methods and equipment for assessing the duration and quality of sleep, as this process can result in better health and performance for the athlete.
Sleep is an essential component for athletes’ recovery from fatigue, due especially to its physiological and psychological restorative effects [25]. In fact, it seems important that athletes learn to manage their sleeping and waking times, given the influence on circadian rhythm, since alterations in the biological clock may affect not only the duration and quality of sleep, but, mainly, sports performance [17].
Athletes and coaches recognize the importance of sleep as one of the most important strategies for recovering from fatigue and improving an athlete’s performance [2]. However, during the competitive period, it is common for athletes to follow strict training and competition schedules, which, associated with intense training loads and the physical and emotional demands of competitions, may interfere and reduce the duration and quality of their sleep [26] and, consequently, decrease the fatigue recovery process [27]. This potential imbalance can actually occur when training and competitions are held close to bedtime [28]. Furthermore, exercise, when performed close to bedtime, may alter circadian rhythms [29] and sleep patterns (e.g., reducing sleep duration) [28, 30]. In fact, it seems important that athletes learn to manage their sleeping and waking times, given the influence on circadian rhythm, since alterations in the biological clock may affect not only the duration and quality of sleep, but, mainly, sports performance [2].
In the general population, less than 8 h of sleep per night may be associated with alterations in cognitive performance, mood, and wakefulness, as well as with increases in daytime sleepiness episodes [31]. This theme extends to younger athletes, who are expected to have a greater physiological need for sleep (8–10 h per night) compared with adults (7–9 h per night) and who often experience delays in sleep onset and awakening [32, 33]. Similarly, compared with adult athletes, young athletes have different daily commitments, such as school and social activities (including time spent online during the night), which can further alter sleep habits and/or wakefulness [34]. As an example, in an epidemiological study [35], significant reductions in neurocognitive performance (assessed through visual tests of memory and speed of response to a given visual stimulus) were observed in 7150 young athletes from different sports, who had a sleep duration of less than 5 h per night.
However, despite the high availability of information regarding the duration and quality of sleep in different age groups in the general population, in the scientific literature, the information available regarding the duration and quality of sleep in athletes is still scarce [36]. In fact, this seems contradictory given that sleep is currently recognized as one of the essential components in athletes’ recovery [25]. Thus, there is a need to investigate, through sensitive and noninvasive methods, the monitoring of sleep patterns and wakefulness in athletes, in order to promote better sleep hygiene and, consequently, better recovery and performance.
The current training and competition demands are topics with the greatest interest and discussion in the fields of sports science and sports medicine. This theme is commonly associated with the problem of sports injuries that affect athletes. In this sense, it is essential that clubs create ideal conditions for the training and development of athletes, integrating strategies and best practices for the prevention, treatment, and rehabilitation of injuries in an integrated perspective for athletes’ health and performance.
Sleep can influence the risk of injury and illness. In a study of 122 athletes, it was observed that the risk of injury increased by 65% when athletes slept less than 8 h per night [37]. In another more recent study, it was possible to observe that 23 athletes with reduced sleep durations (<8 h) demonstrated a high association with the increase in musculoskeletal injuries. However, evidence in the literature is still very limited about this association. It is also important to note that sports injury is an emergent complex phenomenon, and the risk factors of injury comprise nonlinear associations between various factors such as the biomechanics, training and competitions workloads, as well as psychological and physiological characteristics. For example, according to Laux et al. [38] results, the highest risk for injury appears to occur from a synchronized growth in training and competitions workloads and loss in total sleep time; nonetheless, prospective randomized trials determining that decreased sleep quality leads an injury could require a more decisive response. Research on this topic may provide important information for coaches and practitioners in identifying potential strategies to maintain and improve athlete well-being.
Effects of inadequate sleep duration and quality on performance are likely to be seen specifically in competitive athletes, because of their high-performance demands being more likely to show the harmful effects of suboptimal sleep. Research studies have found negative results of sleep deficiency on athletic performance and well-being, specifically relative to time to exhaustion, muscle strength, and mood state [39, 40]. In a study of a sleep banking (i.e., sleep extension) for college basketball players (
Considering the importance of examining sleep habits and wakefulness in athletes, the impact of training and competition schedules and loads on sleep indices has recently been explored [43, 44, 45]. In these studies, it was observed that sleep habits (i.e., the duration and quality of sleep) can be affected by schedule variations and by training and competition loads, especially when sessions are held at night, close to bedtime.
It should also be noted that the sleep habits and wakefulness of athletes may depend on the type of sport practiced [26]. For instance, Lastella et al. [26] investigated sleep/wake behavior of elite athletes, including young female and male athletes, and compared differences between athletes from individual (cycling, mountain bike, racewalking, swimming, and triathlon) and team sports (Australian football, basketball, soccer, and rugby union). Sleep/wake behaviors of elite athletes (
That said, and although the duration and quality of an athlete’s sleep may be associated with the schedules and loads of training and competition, it is also important to consider other factors that can influence sleep indices and wakefulness, namely age, sex, and chronotype [46]. For example, sex was identified as a risk factor for lifetime sleep problems in elite French athletes, with a greater incidence of sleep problems in female athletes [47]. Age has been shown to relate to the prevalence of poor sleep quality, with athletes >25 years of age reporting greater Pittsburgh Sleep Quality Index (PSQI) scores compared with ages <20 [48]; early fatherhood and/or motherhood could be a causal factor [49]. The age of the athletes was also classified as a risk factor for sleep disturbance previous to a competition; however, habitual sleep quality was not [50]. These findings may indicate that athletes who normally report good sleep quality are not necessarily resilient against sleep disturbance during, for instance, a major competition.
To detect and control sleep disorders, it is important to monitor sleep habits and perceptions of sleep through subjective and objective measures [51].
In general, the main recommendations on sleep monitoring point to polysomnography, which uses surface electrodes to monitor physiological parameters such as brain, muscle, cardiac, and respiratory activity [52]. Polysomnography is particularly useful for investigating sleep pathologies, including sleep-disordered breathing [53] and sleep disorders caused by concussion [54]. However, polysomnography is an expensive technique and requires specialized laboratory equipment, so its use in athletes in the real context is impractical [55].
On the other hand, actigraphy uses accelerometers placed in portable devices to record movements that, analyzed using algorithms, estimate the quality and duration of sleep [56]. Actigraphy is less expensive, noninvasive, and can be used in training and competition routines, ideally requiring two consecutive weeks of monitoring [57]. Thus, actigraphy emerges as the most accessible method to objectively monitor the sleep of athletes during the night [55]. Overall, wrist-worn accelerometers allow estimation of total sleep time (the total amount of sleep obtained during a sleep period), time in bed (the amount of time spent in bed attempting to sleep between bedtime and get-up time), wake up time (time at which a athlete got out of bed and stopped attempting to sleep), sleep onset time (transition from wakefulness into sleep), wake after sleep onset (number of min awake after sleep onset), latency (the period of time between bedtime and sleep onset time), and sleep efficiency (percentage of time in bed that was spent asleep) [55]. However, it is imperative to highlight that activity monitors tend to underestimate sleep in people who exhibit high levels of movement during light sleep [58]. In fact, some works showed that (elite) athletes obtain less sleep than the general population [59, 60] and present larger movement and fragmentation during sleep [61, 62]. Thus, and given the sleep characteristics of (elite) athletes, it is important to determine how well activity monitors are sensitive to recognize moments of sleep and vigilance in this type of population. This raises a potential issue with the use of activity monitors for measuring sleep in (elite) athletes.
Questionnaires and in particular “sleep diaries” are also used to record the start and end times for all sleep periods (i.e., night sleep and daily naps) [57]. Nevertheless, subjective reports (e.g., PSQI) might deviate from objective measures [63], especially with regard to mood and memory biases, while personality characteristics may also affect self-reported sleep ratings [64]. Indeed, some discrepancies have been detected when comparing subjective parameters with objective measures [65].
Additionally, and considering the ability of monitoring (objectively or subjectively) sleep duration and quality obtained by an (elite) athlete as a useful tool for evaluating recovery from training and competition [55], it is crucial to highlight the importance of individualized monitoring.
Although it is conventional to focus monitoring on group mean responses following a particular training intervention or competition, sport settings frequently produce diverse results with high and low responders being often lost in the averaged data reports [66, 67]. As a consequence, an increased attention for individualization of monitoring in sport settings has growth to a variety of athlete-monitoring approaches, allowing coaches to better manage fatigue and planning training prescription on an individual basis [68].
Nevertheless, research examining the sleep of athletes has typically averaged data across several nights, providing a mean estimate of usual sleep [26, 48, 61]. While such approaches are useful to allow basic insight into sleep (to better understand fatigue and recovery in athletes), they lack the sophistication to provide understanding of how sleep may vary across multiple nights at the individual level [69, 70, 71]. Moreover, individual variability can reflect differences within individuals over time [72], with high intra-individual variability in the athletes’ sleep indicating the need for individualized sleep education strategies and interventions to promote appropriate sleep [69].
Although identifying the optimal amount of sleep on an individual basis may be difficult [73], young and adult athletes who exhibit average sleep of less than 8 or 7 h, respectively, likely warrant additional assessment to classify their sleep difficulties. Hence, those athletes that reveal deleterious effects of inadequate total sleep time should be stimulated to use sleep hygiene strategies to increase sleep during night and vigilance during the day [74]. Longitudinal monitoring of training and match load, sleep, fatigue (e.g., through heart rate variability), stress, and mood may not only help identify individuals at risk, but also monitor improvements in sleep, well-being, and performance after interventions [75].
Overall, it might be important to include sleep monitoring in (elite) athletes encompassing individual responses, in addition to group means [69]. Also, special attention should be given to the sleep behavior of (elite) athletes (e.g., total sleep time) during periods of congested fixtures, such as international competitions, since sleep deficits can impair performance [17], as already mention above (point 3).
The implementation of strategies that promote sleep quality should be a priority for athletes. In fact, during sleep, fundamental physiological and psychological processes take place for the recovery from fatigue, so the optimization of sleep hygiene strategies increasingly assumes an important role in the routines and planning of those dedicated to improving sport performance.
A recent study [76] evaluated the effect of education on sleep hygiene in athletes. It was found that sleep hygiene education had a considerable positive impact on sleep indices. Educational programs on sleep hygiene in athletes provided a significant improvement in sleep duration and quality and reduced daytime sleepiness. Furthermore, research into the effects of sleep hygiene education on athletes, especially young people, is quite limited [31].
As mentioned before, there are several factors that can influence the duration and quality of sleep in athletes. Calendars congested with competitions and regular trips, competitions of great physical and emotional demand that take place at night, or constant changes in the morning time to wake up because of training and travel are examples of common factors that can negatively influence the duration and quality of sleep in athletes.
In this context, the management of light exposure emerges as fundamental, as this factor has a significant impact on sleep. Exposure to light influences the production of melatonin, so managing the times of exposure to artificial light throughout the day can be used as a sleep management and hygiene strategy. Additionally, in competitions that take place at night, athletes are exposed to immense artificial light: lighting in sports facilities, the projectors used by the media in interviews at the end of competitions, light from busses, airports, and planes.
On the other hand, social contexts may also be decisive. In recent studies carried out with female soccer players in Portugal, who usually start training very late, close to bedtime, due to their daily commitments (e.g., work, studies) that have to be reconciled with the training and match schedules, it was found that the athletes showed a reduction in total sleep time and length of time to fall asleep on training days performed at night, compared with training days performed during the day or on rest days (i.e., days without exercise) [28, 44]. It was pointed out that one of the additional explanations for the observed results could have been in the athletes’ exposure to the light emitted in the stadium. In fact, these data are little studied in sport, but during the training days, the athletes were exposed to >1200 lux and 5600 K, with the bright polychromatic light ≥1000 lux, which could be enough to stimulate wakefulness effects during sleep [77]. However, it should be borne in mind that, currently, one of the main sources of exposure to light results from the use of electronic devices (especially smartphones and tablets) and that their use around bedtime is possibly the factor that most influences the sleep latency of athletes.
Thus, the term sleep hygiene, which refers to the recommendations, strategies, behaviors, and conditions developed to promote quality and duration of sleep, has been appearing more and more often in the list of sports planning tasks for athletes [25]. It is important to be aware that, unlike other possible recovery strategies used in sport (e.g., cryotherapy, massage, nutrition, nutritional supplementation), sleep has particularities that are not always controlled by the athlete themselves. Thus, bearing in mind the importance that sleep can have on sports performance, this is a subject that deserves the greatest attention of all those dedicated to promoting health and performance in athletes.
Athletes, coaches, and supporting staff should adopt a scientific approach to both designing and monitoring training programs. Appropriate health and load monitoring is crucial for determining whether a player is adapting to a training program and minimizing the risk of developing nonfunctional overreaching, illness, or injury. To gain understanding of the training and match demands and their effects on the player, several potential markers are available. However, very few of them have strong scientific evidence supporting their use. Moreover, it is important to note that athletes, from different types of sports, normally obtain inadequate sleep duration and quality. From an athletic point of view, reductions in performance, decision-making ability, learning, and cognition can occur alongside reductions in immune function and an increased susceptibility to injury gain.
In this respect, monitoring sleep in athletes can be useful for early detection and intervention before significant performance and health decrements are observed. Noninvasive and time-efficient methods/equipment such as wearable actigraphy monitors can provide detailed information about positive and negative adaptions over short and long periods throughout the competitive season. In addition, each athlete can perform the recordings at home and/or training facilities, adopting a “real world scenario” to grant high ecological validity to the research and/or practical interventions. The accumulated knowledge regarding the importance of sleep has sleep monitoring to become a popular strategy among (elite) athletes, coaches, and supporting staff. However, given the complexity of analyzing sleep patterns and the limited availability of athletes to participate in sleep studies, those indicators are yet poorly documented.
Overall, factors related to training and competition can alter sleep patterns in athletes. Therefore, topics such as: (1) sleep patterns and disorders among athletes; (2) sleep and optimal functioning among athletes; (3) screening, tracking, and assessment of athletes’ sleep; and (4) interventions (i.e., sleep hygiene) to improve sleep must be further investigated.
The authors declare no conflict of interest.
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Dr. Şentürk currently works as an professor of Biochemistry in the Department of Basic Pharmacy Sciences, Faculty of Pharmacy, Ağri Ibrahim Cecen University, Turkey. \nDr. Şentürk published over 120 scientific papers, reviews, and book chapters and presented several conferences to scientists. \nHis research interests span enzyme inhibitor or activator, protein expression, purification and characterization, drug design and synthesis, toxicology, and pharmacology. \nHis research work has focused on neurodegenerative diseases and cancer treatment. 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He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. She is an author of about 90 publications (According to Scopus: H-Index: 23; According to WOS: H-Index: 20) on peer-reviewed journals, a member of the “Società Italiana di Biochimica e Biologia Molecolare,“ and a Consultant Reviewer for International Journal of Molecular Science, Journal of Chromatography A, COPD, Plos ONE and Nutritional Neuroscience.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"0",institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,series:{id:"11",title:"Biochemistry"}}},seriesLanding:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfOpenTopics:4,numberOfPublishedChapters:318,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},subseries:[{id:"14",title:"Cell and Molecular Biology",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression",scope:"The Cell and Molecular Biology topic within the IntechOpen Biochemistry Series aims to rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",annualVolume:11410,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"79367",title:"Dr.",name:"Ana Isabel",middleName:null,surname:"Flores",fullName:"Ana Isabel Flores",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRpIOQA0/Profile_Picture_1632418099564",institutionString:null,institution:{name:"Hospital Universitario 12 De Octubre",institutionURL:null,country:{name:"Spain"}}},{id:"328234",title:"Ph.D.",name:"Christian",middleName:null,surname:"Palavecino",fullName:"Christian Palavecino",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y000030DhEhQAK/Profile_Picture_1628835318625",institutionString:null,institution:{name:"Central University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"186585",title:"Dr.",name:"Francisco Javier",middleName:null,surname:"Martin-Romero",fullName:"Francisco Javier Martin-Romero",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSB3HQAW/Profile_Picture_1631258137641",institutionString:null,institution:{name:"University of Extremadura",institutionURL:null,country:{name:"Spain"}}}]},{id:"15",title:"Chemical Biology",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",annualVolume:11411,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null,editorialBoard:[{id:"219081",title:"Dr.",name:"Abdulsamed",middleName:null,surname:"Kükürt",fullName:"Abdulsamed Kükürt",profilePictureURL:"https://mts.intechopen.com/storage/users/219081/images/system/219081.png",institutionString:null,institution:{name:"Kafkas University",institutionURL:null,country:{name:"Turkey"}}},{id:"241413",title:"Dr.",name:"Azhar",middleName:null,surname:"Rasul",fullName:"Azhar Rasul",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRT1oQAG/Profile_Picture_1635251978933",institutionString:null,institution:{name:"Government College University, Faisalabad",institutionURL:null,country:{name:"Pakistan"}}},{id:"178316",title:"Ph.D.",name:"Sergey",middleName:null,surname:"Sedykh",fullName:"Sergey Sedykh",profilePictureURL:"https://mts.intechopen.com/storage/users/178316/images/system/178316.jfif",institutionString:null,institution:{name:"Novosibirsk State University",institutionURL:null,country:{name:"Russia"}}}]},{id:"17",title:"Metabolism",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",annualVolume:11413,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null,editorialBoard:[{id:"243049",title:"Dr.",name:"Anca",middleName:null,surname:"Pantea Stoian",fullName:"Anca Pantea Stoian",profilePictureURL:"https://mts.intechopen.com/storage/users/243049/images/system/243049.jpg",institutionString:null,institution:{name:"Carol Davila University of Medicine and Pharmacy",institutionURL:null,country:{name:"Romania"}}},{id:"203824",title:"Dr.",name:"Attilio",middleName:null,surname:"Rigotti",fullName:"Attilio Rigotti",profilePictureURL:"//cdnintech.com/web/frontend/www/assets/author.svg",institutionString:null,institution:{name:"Pontifical Catholic University of Chile",institutionURL:null,country:{name:"Chile"}}},{id:"300470",title:"Dr.",name:"Yanfei (Jacob)",middleName:null,surname:"Qi",fullName:"Yanfei (Jacob) Qi",profilePictureURL:"https://mts.intechopen.com/storage/users/300470/images/system/300470.jpg",institutionString:null,institution:{name:"Centenary Institute of Cancer Medicine and Cell Biology",institutionURL:null,country:{name:"Australia"}}}]},{id:"18",title:"Proteomics",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",annualVolume:11414,isOpenForSubmission:!0,coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorThree:null,editorialBoard:[{id:"72288",title:"Dr.",name:"Arli Aditya",middleName:null,surname:"Parikesit",fullName:"Arli Aditya Parikesit",profilePictureURL:"https://mts.intechopen.com/storage/users/72288/images/system/72288.jpg",institutionString:null,institution:{name:"Indonesia International Institute for Life Sciences",institutionURL:null,country:{name:"Indonesia"}}},{id:"40928",title:"Dr.",name:"Cesar",middleName:null,surname:"Lopez-Camarillo",fullName:"Cesar Lopez-Camarillo",profilePictureURL:"https://mts.intechopen.com/storage/users/40928/images/3884_n.png",institutionString:null,institution:{name:"Universidad Autónoma de la Ciudad de México",institutionURL:null,country:{name:"Mexico"}}},{id:"81926",title:"Dr.",name:"Shymaa",middleName:null,surname:"Enany",fullName:"Shymaa Enany",profilePictureURL:"https://mts.intechopen.com/storage/users/81926/images/system/81926.png",institutionString:"Suez Canal University",institution:{name:"Suez Canal University",institutionURL:null,country:{name:"Egypt"}}}]}]}},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/290766",hash:"",query:{},params:{id:"290766"},fullPath:"/profiles/290766",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()