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Advances in computer science and technology and in biology over the last several years have opened up the possibility for computing to help answer fundamental questions in biology and for biology to help with new approaches to computing. Making the most of the research opportunities at the interface of computing and biology requires the active participation of people from both fields. While past attempts have been made in this direction, circumstances today appear to be much more favorable for progress. To help take advantage of these opportunities, this study was requested of the NRC by the National Science Foundation, the Department of Defense, the National Institutes of Health, and the Department of Energy. The report provides the basis for establishing cross-disciplinary collaboration between biology and computing including an analysis of potential impediments and strategies for overcoming them. The report also presents a wealth of examples that should encourage students in the biological sciences to look for ways to enable them to be more effective users of computing in their studies.
Systems Biology in Toxicology and Environmental Health uses a systems biological perspective to detail the most recent findings that link environmental exposures to human disease, providing an overview of molecular pathways that are essential for cellular survival after exposure to environmental toxicants, recent findings on gene-environment interactions influencing environmental agent-induced diseases, and the development of computational methods to predict susceptibility to environmental agents. Introductory chapters on molecular and cellular biology, toxicology and computational biology are included as well as an assessment of systems-based tools used to evaluate environmental health risks. Further topics include research on environmental toxicants relevant to human health and disease, various high-throughput technologies and computational methods, along with descriptions of the biological pathways associated with disease and the developmental origins of disease as they relate to environmental contaminants. Systems Biology in Toxicology and Environmental Health is an essential reference for undergraduate students, graduate students, and researchers looking for an introduction in the use of systems biology approaches to assess environmental exposures and their impacts on human health. - Provides the first reference of its kind, demonstrating the application of systems biology in environmental health and toxicology - Includes introductions to the diverse fields of molecular and cellular biology, toxicology, and computational biology - Presents a foundation that helps users understand the connections between the environment and health effects, and the biological mechanisms that link them
Computational biology drives discovery through its use of high-throughput informatics approaches. This book provides a road map of the current drug development process and how computational biology approaches play a critical role across the entire drug discovery pipeline. Through the use of previously unpublished, real-life case studies the impact of a range of computational approaches are discussed at various phases of the pipeline. Additionally, a focus section provides innovative visualisation approaches, from both the drug discovery process as well as from other fields that utilise large datasets, recognising the increasing use of such technology. Serving the needs of early career and more experienced scientists, this up-to-date reference provides an essential introduction to the process and background of drug discovery, highlighting how computational researchers can contribute to that pipeline.
Bacteria, yeast, fungi and microalgae can act as producers (or catalysts for the production) of food ingredients, enzymes and nutraceuticals. With the current trend towards the use of natural ingredients in foods, there is renewed interest in microbial flavours and colours, food bioprocessing using enzymes and food biopreservation using bacteriocins. Microbial production of substances such as organic acids and hydrocolloids also remains an important and fast-changing area of research. Microbial production of food ingredients, enzymes and nutraceuticals provides a comprehensive overview of microbial production of food ingredients, enzymes and nutraceuticals.Part one reviews developments in the metabolic engineering of industrial microorganisms and advances in fermentation technology in the production of fungi, yeasts, enzymes and nutraceuticals. Part two discusses the production and application in food processing of substances such as carotenoids, flavonoids and terponoids, enzymes, probiotics and prebiotics, bacteriocins, microbial polysaccharides, polyols and polyunsaturated fatty acids.Microbial production of food ingredients, enzymes and nutraceuticals is an invaluable guide for professionals in the fermentation industry as well as researchers and practitioners in the areas of biotechnology, microbiology, chemical engineering and food processing. - Provides a comprehensive overview of microbial flavours and colours, food bioprocessing using enzymes and food biopreservation using bacteriocins - Begins with a review of key areas of systems biology and metabolic engineering, including methods and developments for filamentous fungi - Analyses the use of microorganisms for the production of natural molecules for use in foods, including microbial production of food flavours and carotenoids
Introduces readers to the state of the art of omics platforms and all aspects of omics approaches for clinical applications This book presents different high throughput omics platforms used to analyze tissue, plasma, and urine. The reader is introduced to state of the art analytical approaches (sample preparation and instrumentation) related to proteomics, peptidomics, transcriptomics, and metabolomics. In addition, the book highlights innovative approaches using bioinformatics, urine miRNAs, and MALDI tissue imaging in the context of clinical applications. Particular emphasis is put on integration of data generated from these different platforms in order to uncover the molecular landscape of diseases. The relevance of each approach to the clinical setting is explained and future applications for patient monitoring or treatment are discussed. Integration of omics Approaches and Systems Biology for Clinical Applications presents an overview of state of the art omics techniques. These methods are employed in order to obtain the comprehensive molecular profile of biological specimens. In addition, computational tools are used for organizing and integrating these multi-source data towards developing molecular models that reflect the pathophysiology of diseases. Investigation of chronic kidney disease (CKD) and bladder cancer are used as test cases. These represent multi-factorial, highly heterogeneous diseases, and are among the most significant health issues in developed countries with a rapidly aging population. The book presents novel insights on CKD and bladder cancer obtained by omics data integration as an example of the application of systems biology in the clinical setting. Describes a range of state of the art omics analytical platforms Covers all aspects of the systems biology approach—from sample preparation to data integration and bioinformatics analysis Contains specific examples of omics methods applied in the investigation of human diseases (Chronic Kidney Disease, Bladder Cancer) Integration of omics Approaches and Systems Biology for Clinical Applications will appeal to a wide spectrum of scientists including biologists, biotechnologists, biochemists, biophysicists, and bioinformaticians working on the different molecular platforms. It is also an excellent text for students interested in these fields.
Toxicogenomics was established as a merger of toxicology with genomics approaches and methodologies more than 15 years ago, and considered of major value for studying toxic mechanisms-of-action in greater depth and for classification of toxic agents for predicting adverse human health risks. While the original focus was on technological validation of in particular microarray-based whole genome expression analysis (transcriptomics), mainly through cross-comparing different platforms for data generation (MAQC-I), it was soon appreciated that actually the wide variety of data analysis approaches represents the major source of inter-study variation. This led to early attempts towards harmonizing data analysis protocols focusing on microarray-based models for predicting toxicological and clinical end-points and on different methods for GWAS data (MAQC-II). Simultaneously, further technological developments, geared by increasing insights into the complexity of cellular regulation, enabled analyzing molecular perturbations across multiple genomics scales (epigenomics and microRNAs, metabolomics). While these were initially still based on microarray technology, this is currently being phased out and replaced by a variety of next generation sequencing-based methods enabling exploration of genomic responses to toxicants at even greater depth (SEQC-I). This raises the demand for reliable and robust data analysis approaches, ranging from harmonized bioinformatics concepts for preprocessing raw data to non-supervised and supervised methods for capturing and integrating the dynamic perturbations of cell function across dose and time, and thus retrieving mechanistic insights across multiple regulation scales. Traditional toxicology focused on dose-dependently determining apical endpoints of toxicity. With the advent of toxicogenomics, efforts towards better understanding underlying molecular mechanisms has led to the development of the concept of Adverse Outcome Pathways, which are basically presented as a structural network of linearly related gene-gene interactions regulating key events for inducing apical toxic endpoints of interest. Impulse challenges from exposure of biological systems to toxic agents will however induce a cascade-type of events, presenting both adverse and adaptive processes, thus requiring bioinformatics approaches and methods for complex dynamic data, generated not only across dose, but clearly also across time. Currently, time-resolved toxicogenomics data sets are increasingly being assembled in the course of large-scaled research projects, for instance devoted towards developing toxicogenomics-based predictive assays for evaluating chemical safety which are no longer animal-based.
Computational Biology for Stem Cell Research is an invaluable guide for researchers as they explore HSCs and MSCs in computational biology. With the growing advancement of technology in the field of biomedical sciences, computational approaches have reduced the financial and experimental burden of the experimental process. In the shortest span, it has established itself as an integral component of any biological research activity. HSC informatics (in silico) techniques such as machine learning, genome network analysis, data mining, complex genome structures, docking, system biology, mathematical modeling, programming (R, Python, Perl, etc.) help to analyze, visualize, network constructions, and protein-ligand or protein-protein interactions. This book is aimed at beginners with an exact correlation between the biomedical sciences and in silico computational methods for HSCs transplantation and translational research and provides insights into methods targeting HSCs properties like proliferation, self-renewal, differentiation, and apoptosis. - Modeling Stem Cell Behavior: Explore stem cell behavior through animal models, bridging laboratory studies to real-world clinical allogeneic HSC transplantation (HSCT) scenarios. - Bioinformatics-Driven Translational Research: Navigate a path from bench to bedside with cutting-edge bioinformatics approaches, translating computational insights into tangible advancements in stem cell research and medical applications. - Interdisciplinary Resource: Discover a single comprehensive resource catering to biomedical sciences, life sciences, and chemistry fields, offering essential insights into computational tools vital for modern research.
Systems biology is the inevitable outcome of long years of knowledge acquisition and data accumulation. The aim of systems biology is to integrate in a seamless way all existing knowledge in interconnected disciplines, stretching from modern biomedical research to physics, chemistry, and mathematics. The main integration tool of such complex biomedical systems is via computational and mathematical modeling. In this direction, a series of state-of-the-art computer science techniques are used, namely, data mining and fusion, machine learning, and deep learning all under the prism of big data. All in all, systems biology is at the arrowhead of modern and state-of-the-art biomedical research by attempting to address key biological questions describing holistically complex biological systems.
