This book presents a range of current research topics in biological network modeling, as well as its application in studies on human hosts, pathogens, and diseases. Systems biology is a rapidly expanding field that involves the study of biological systems through the mathematical modeling and analysis of large volumes of biological data. Gathering contributions from renowned experts in the field, some of the topics discussed in depth here include networks in systems biology, the computational modeling of multidrug-resistant bacteria, and systems biology of cancer. Given its scope, the book is intended for researchers, advanced students, and practitioners of systems biology. The chapters are research-oriented, and present some of the latest findings on their respective topics.

Bioinformatics is an integrative field of computer science, genetics, genomics, proteomics, and statistics, which has undoubtedly revolutionized the study of biology and medicine in past decades. It mainly assists in modeling, predicting and interpreting large multidimensional biological data by utilizing advanced computational methods. Despite its enormous potential, bioinformatics is not widely integrated into the academic curriculum as most life science students and researchers are still not equipped with the necessary knowledge to take advantage of this powerful tool. Hence, the primary purpose of our book is to supplement this unmet need by providing an easily accessible platform for students and researchers starting their career in life sciences. This book aims to avoid sophisticated computational algorithms and programming. Instead, it focuses on simple DIY analysis and interpretation of biological data with personal computers. Our belief is that once the beginners acquire these basic skillsets, they will be able to handle most of the bioinformatics tools for their research work and to better understand their experimental outcomes. Our second title of this volume set In Silico Life Sciences: Medicine provides hands-on experience in analyzing high throughput molecular data for the diagnosis, prognosis, and treatment of monogenic or polygenic human diseases. The key concepts in this volume include risk factor assessment, genetic tests and result interpretation, personalized medicine, and drug discovery. This volume is expected to train readers in both single and multi-dimensional biological analysis using open data sets, and provides a unique learning experience through clinical scenarios and case studies.

This book provides essential insights into designing a localized DNA circuit to promote the rate of desired hybridization reactions over undesired leak reactions in the bulk solution. The area of dynamic DNA nanotechnology, or DNA circuits, holds great promise as a highly programmable toolbox that can be used in various applications, including molecular computing and biomolecular detection. However, a key bottleneck is the recurring issue of circuit leakage. The assembly of the localized circuit is dynamically driven by the recognition of biomolecules - a different approach from most methods, which are based on a static DNA origami assembly. The design guidelines for individual reaction modules presented here, which focus on minimizing circuit leakage, are established through NUPACK simulation and tested experimentally - which will be useful for researchers interested in adapting the concepts for other contexts. In the closing section, the design concepts are successfully applied to the biomolecular sensing of a broad range of targets including the single nucleotide mutations, proteins, and cell surface receptors.

A guide to understanding the formation of life in the Universe The revised and updated second edition of Astrobiology offers an introductory text that explores the structure of living things, the formation of the elements required for life in the Universe, the biological and geological history of the Earth, and the habitability of other planets. Written by a noted expert on the topic, the book examines many of the major conceptual foundations in astrobiology, which cover a diversity of traditional fields including chemistry, biology, geosciences, physics, and astronomy. The book explores many profound questions such as: How did life originate on Earth? How has life persisted on Earth for over three billion years? Is there life elsewhere in the Universe? What is the future of life on Earth? Astrobiology is centered on investigating the past and future of life on Earth by looking beyond Earth to get the answers. Astrobiology links the diverse scientific fields needed to understand life on our own planet and, potentially, life beyond. This new second edition: Expands on information about the nature of astrobiology and why it is useful Contains a new chapter "What is Life?" that explores the history of attempts to understand life Contains 20% more material on the astrobiology of Mars, icy moons, the structure of life, and the habitability of planets New 'Discussion Boxes' to stimulate debate and thought about key questions in astrobiology New review and reflection questions for each chapter to aid learning New boxes describing the careers of astrobiologists and how they got into the subject Offers revised and updated information throughout to reflect the latest advances in the field Written for students of life sciences, physics, astronomy and related disciplines, the updated edition of Astrobiology is an essential introductory text that includes recent advances to this dynamic field.

This book reviews the advances and challenges of structure-based drug design in the preclinical drug discovery process, addressing various diseases, including malaria, tuberculosis and cancer. Written by internationally recognized researchers, this edited book discusses how the application of the various in-silico techniques, such as molecular docking, virtual screening, pharmacophore modeling, molecular dynamics simulations, and residue interaction networks offers insights into pharmacologically active novel molecular entities. It presents a clear concept of the molecular mechanism of different drug targets and explores methods to help understand drug resistance. In addition, it includes chapters dedicated to natural-product- derived medicines, combinatorial drug discovery, the CryoEM technique for structure-based drug design and big data in drug discovery. The book offers an invaluable resource for graduate and postgraduate students, as well as for researchers in academic and industrial laboratories working in the areas of chemoinformatics, medicinal and pharmaceutical chemistry and pharmacoinformatics.

This monograph sketches out a broad spectrum of problems (from evolution and metabolism to morphogenesis and biogeographical dynamics) whose solution has been impacted by mathematical models. Each of the selected examples has led to the recognition-and set direction to further study-of certain fundamental but unintuitive properties of biological systems, such as the making and breaking of specific symmetries that underlie morphogenesis. Whether they are long-established or only recently accepted, these models are selected for being thought-provoking and illuminating both the achievements and the gaps in our current understanding of the given area of biology. The selection of models is also meant to bring to the fore the existing degree of unity in the quantitative approach to diverse general-biological questions and in the systems-level properties that are discovered across the levels of biological organization. It is the thesis of this book that further cultivation of such unity is a way forward as we progress toward a general theory of living matter. This is an ideal book for students (in the broadest sense) of biology who wish to learn from this attempt to present the exemplary models, their methodological lessons, and the outline of a unified theory of living matter that is now beginning to emerge. In addition to a doctoral student preparing for quantitative biology research, this reader could also be an interdisciplinary scientist transitioning to biology. The latter-for example, a physicist or an engineer-may be comfortable with the mathematical apparatus and prepared to quickly enter the intended area of work, but desires a broader foundation in biology from the quantitative perspective.

E=mc(2) is known as the most famous but least understood equation in physics. This two-volume textbook illuminates this equation and much more through clear and detailed explanations, new demonstrations, a more physical approach, and a deep analysis of the concepts and postulates of Relativity. The first part of Volume I contains the whole Special Relativity theory with rigorous and complete demonstrations. The second part presents the main principles of General Relativity, including detailed explanations of the bending of light in the neighborhood of great masses, the gravitational time dilatation, and the principles leading to the famous equation of General Relativity: D(g) = k .T. The most important cosmological predictions are then described: the Big Bang theory, black holes, and gravitational waves. Plentiful historical information is contained throughout the book, particularly in an ending chapter depicting the scientific and epistemological revolution brought about by the theory of Relativity. Volume II progresses into further depth than Volume I, and its scope is more extended than most introductory books on Relativity. It includes the affine connection, the geodesic equation, and an introduction to cosmological models. The mathematical tools dedicated to Relativity are carefully explained for those without an advanced mathematical background (tensors, Lagrangians, covariant derivative). Both volumes place an emphasis on the physical aspects of Relativity to aid the reader's understanding and contain numerous questions and problems (147 in total). Solutions are given in a highly detailed manner to provide the maximum benefit to students. This textbook fills a gap in the literature by drawing out the physical aspects and consequences of Relativity, which are otherwise often second place to the mathematical aspects. Its concrete focus on physics allows students to gain a full understanding of the underlying concepts and cornerstones of Relativity.

This book examines statistical methods and models used in the fields of global health and epidemiology. It includes methods such as innovative probability sampling, data harmonization and encryption, and advanced descriptive, analytical and monitory methods. Program codes using R are included as well as real data examples. Contemporary global health and epidemiology involves a myriad of medical and health challenges, including inequality of treatment, the HIV/AIDS epidemic and its subsequent control, the flu, cancer, tobacco control, drug use, and environmental pollution. In addition to its vast scales and telescopic perspective; addressing global health concerns often involves examining resource-limited populations with large geographic, socioeconomic diversities. Therefore, advancing global health requires new epidemiological design, new data, and new methods for sampling, data processing, and statistical analysis. This book provides global health researchers with methods that will enable access to and utilization of existing data. Featuring contributions from both epidemiological and biostatistical scholars, this book is a practical resource for researchers, practitioners, and students in solving global health problems in research, education, training, and consultation.

Social pressure to minimize the use of animal testing, the ever-increasing concern on animal welfare, and the need for more human-relevant and more predictive toxicity tests are some of the drivers for new approaches to chemical screening. This book focuses on The Adverse Outcome Pathway, an analytical construct that describes a sequential chain of causally linked events at different levels of biological organization that lead to an adverse health or ecotoxicological effect. While past efforts have focused on toxicological pathway-based vision for human and ecological health assessment relying on in vitro systems and predictive models, The Adverse Outcome Pathway framework provides a simplified and structured way to organize toxicological information. Within the book, a systems biology approach supplies the tools to infer, link, and quantify the molecular initiating events and the key events and key event relationships leading to adverse outcomes. The advancement of these tools is crucial for the successful implementation of AOPs for regulatory purposes.

The purpose of this book is to thoroughly prepare the reader for applied research in clustering. Cluster analysis comprises a class of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used in several domains of research, such as social sciences, psychology, and marketing, highlighting its multidisciplinary nature. This book provides an accessible and comprehensive introduction to clustering and offers practical guidelines for applying clustering tools by carefully chosen real-life datasets and extensive data analyses. The procedures addressed in this book include traditional hard clustering methods and up-to-date developments in soft clustering. Attention is paid to practical examples and applications through the open source statistical software R. Commented R code and output for conducting, step by step, complete cluster analyses are available. The book is intended for researchers interested in applying clustering methods. Basic notions on theoretical issues and on R are provided so that professionals as well as novices with little or no background in the subject will benefit from the book.

This book introduces research presented at the "International Conference on Artificial Intelligence: Advances and Applications-2019 (ICAIAA 2019)," a two-day conference and workshop bringing together leading academicians, researchers as well as students to share their experiences and findings on all aspects of engineering applications of artificial intelligence. The book covers research in the areas of artificial intelligence, machine learning, and deep learning applications in health care, agriculture, business and security. It also includes research in core concepts of computer networks, intelligent system design and deployment, real-time systems, WSN, sensors and sensor nodes, SDN and NFV. As such it is a valuable resource for students, academics and practitioners in industry working on AI applications.

Climb a mountain and experience the landscape. Try to grasp its holistic nature. Do not climb alone, but with others and share your experience. Be sure the ways of seeing the landscape will be very different. We experience the landscape with all senses as a complex, dynamic and hierarchically structured whole. The landscape is tangible out there and simultaneously a mental reality. Several perspectives are obvious because of language, culture and background. Many disciplines developed to study the landscape focussing on specific interest groups and applications. Gradually the holistic way of seeing became lost. This book explores the different perspectives on the landscape in relation to its holistic nature. We start from its multiple linguistic meanings and a comprehensive overview of the development of landscape research from its geographical origins to the wide variety of today's specialised disciplines and interest groups. Understanding the different perspectives on the landscapes and bringing them together is essential in transdisciplinary approaches where the landscape is the integrating concept.

This book provides a full account of the concept of fiber and fiber theory in eighteenth-century British medicine. It explores the pivotal role fiber played as a defining, underlying concept in anatomy, physiology, pathology, therapeutics, psychology, and the life sciences. With the gradual demise of ancient humoralism, the solid fibers appeared on the medical scene both as the basic building unit of the body and as a dynamic agent of life. As such, fiber stands at the heart of eighteenth-century medicine, both iatromechanism and iatro-vitalism. Touching on the cultural aspects of fiber, the Baroque, and the culture of sensibility, this book also challenges the widely held assumption that the eighteenth century was the age of the nerve and instead offers an alternative model of fiber.

This book offers a comprehensive introduction to using Mathematica and the Wolfram Language for Bioinformatics. The chapters build gradually from basic concepts and the introduction of the Wolfram Language and coding paradigms in Mathematica, to detailed worked examples derived from typical research applications using Wolfram Language code. The coding examples range from basic sequence analysis, accessing genomic databases, differential gene expression, and machine learning implementations to time series analysis of longitudinal omics experiments, multi-omics integration and building dynamic interactive bioinformatics tools using the Wolfram Language. The topics address the daily bioinformatics needs of a broad audience: experimental users looking to understand and visualize their data, beginner bioinformaticians acquiring coding expertise in providing biological research solutions, and practicing expert bioinformaticians working on omics who wish to expand their toolset to include the Wolfram Language.

This books provides up-to-date reviews on current advances of the role of HSP in veterinary medicine and research. Key basic and clinical research laboratories from major universities, veterinary hospitals and pharmaceutical companies around the world have contributed chapters that review present research activity and importantly project this field into the future. For easy readability, the book is sub divided into sections on HSP in the following aspects of Veterinary Medicine, including, I - Domestic Animals, II - Poultry, III - Aquatic and IV - Parasites. The book is a must read for heat shock protein researchers in general and specifically those involved in clinical and research in veterinary medicine.

This book presents the theoretical foundations of Systems Biology, as well as its application in studies on human hosts, pathogens and associated diseases. This book presents several chapters written by renowned experts in the field. Some topics discussed in depth in this book include: computational modeling of multiresistant bacteria, systems biology of cancer, systems immunology, networks in systems biology.

Progress in plant biology relies on the quantification, analysis and mathematical modeling of data over different time and length scales. This book describes common mathematical and computational approaches as well as some carefully chosen case studies that demonstrate the use of these techniques to solve problems at the forefront of plant biology. Each chapter is written by an expert in field with the goal of conveying concepts whilst at the same time providing sufficient background and links to available software for readers to rapidly build their own models and run their own simulations. This book is aimed at postgraduate students and researchers working the field of plant systems biology and synthetic biology, but will also be a useful reference for anyone wanting to get into quantitative plant biology.

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM.

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

This book presents cutting-edge research on the use of physical and mathematical formalisms to model and quantitatively analyze biological phenomena ranging from microscopic to macroscopic systems. The systems discussed in this compilation cover protein folding pathways, gene regulation in prostate cancer, quorum sensing in bacteria to mathematical and physical descriptions to analyze anomalous diffusion in patchy environments and the physical mechanisms that drive active motion in large sets of particles, both fundamental descriptions that can be applied to different phenomena in biology. All chapters are written by well-known experts on their respective research fields with a vast amount of scientific discussion and references in order the interested reader can pursue a further reading. Given these features, we consider Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues as an excellent and up-to-date resource and reference for advanced undergraduate students, graduate students and junior researchers interested in the latest developments at the intersection of physics, mathematics, molecular biology, and computational sciences. Such research field, without hesitation, is one of the most interesting, challenging and active of this century and the next.

This book presents an exciting collection of contributions based on the workshop "Bringing Maths to Life" held October 27-29, 2014 in Naples, Italy. The state-of-the art research in biology and the statistical and analytical challenges facing huge masses of data collection are treated in this Work. Specific topics explored in depth surround the sessions and special invited sessions of the workshop and include genetic variability via differential expression, molecular dynamics and modeling, complex biological systems viewed from quantitative models, and microscopy images processing, to name several. In depth discussions of the mathematical analysis required to extract insights from complex bodies of biological datasets, to aid development in the field novel algorithms, methods and software tools for genetic variability, molecular dynamics, and complex biological systems are presented in this book. Researchers and graduate students in biology, life science, and mathematics/statistics will find the content useful as it addresses existing challenges in identifying the gaps between mathematical modeling and biological research. The shared solutions will aid and promote further collaboration between life sciences and mathematics.

Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: 1. Modeling establishment under uncertainty 2. Model selection and parameter fitting 3. Sensitivity analysis and model adaptation 4. Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate students and researchers active in the field of computational modeling of biomedical processes who seek to acquaint themselves with the different ways in which to study the parameter space of their model as well as its overall behavior.

Technology maturity: What is it, and why is it important? For more than ten years, the Government Accountability Office (GAO) has criticized federal agencies for a history of cost and schedule overruns on a significant portion of their procurement programs. GAO has repeatedly reported that the use of immature technologies in programs is a primary cause for these overruns. In spite of these repeated reports, the problems in government procurement have not improved. In fact, recent reports indicate that the problems are getting worse. One cause of this worsening situation might be that, while GAO identified lack of technology maturity as a problem, they did not tell how to measure technology maturity, or conversely, its lack. This groundbreaking work attempts to fill this gap by examining the current state of technology maturity measurement, pointing out strengths and weaknesses of available measures, and proposing a complete technology maturity assessment as a potential solution. The book also includes a discussion of risk during technology development.