Skip to main content
View Cart

Books in Computational biology

  • Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System

    Biomaterials and Tissues
    • 2nd Edition
    • September 29, 2020
    • Z Jin + 2 more
    • English
    Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System: Biomaterials and Tissues, Second Edition reviews how a wide range of materials are modeled and applied. Chapters cover basic concepts for modeling of biomechanics and biotribology, the fundamentals of computational modeling of biomechanics in the musculoskeletal system, finite element modeling in the musculoskeletal system, computational modeling from a cells and tissues perspective, and computational modeling of the biomechanics and biotribology interactions, looking at complex joint structures. This book is a comprehensive resource for professionals in the biomedical market, materials scientists and biomechanical engineers, and academics in related fields. This important new edition provides an up-to-date overview of the most recent research and developments involving hydroxyapatite as a key material in medicine and its application, including new content on novel technologies, biomorphic hydroxyapatite and more.

  • Computational Learning Approaches to Data Analytics in Biomedical Applications

    • 1st Edition
    • November 20, 2019
    • Khalid Al-Jabery + 3 more
    • English
    Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis using varied machine learning and statistical techniques. It presents insights on biomedical data processing, innovative clustering algorithms and techniques, and connections between statistical analysis and clustering. The book introduces and discusses the major problems relating to data analytics, provides a review of influential and state-of-the-art learning algorithms for biomedical applications, reviews cluster validity indices and how to select the appropriate index, and includes an overview of statistical methods that can be applied to increase confidence in the clustering framework and analysis of the results obtained.

  • A Comprehensive Physically Based Approach to Modeling in Bioengineering and Life Sciences

    • 1st Edition
    • July 18, 2019
    • Riccardo Sacco + 2 more
    • English
    A Comprehensive Physically Based Approach to Modeling in Bioengineering and Life Sciences provides a systematic methodology to the formulation of problems in biomedical engineering and the life sciences through the adoption of mathematical models based on physical principles, such as the conservation of mass, electric charge, momentum, and energy. It then teaches how to translate the mathematical formulation into a numerical algorithm that is implementable on a computer. The book employs computational models as synthesized tools for the investigation, quantification, verification, and comparison of different conjectures or scenarios of the behavior of a given compartment of the human body under physiological and pathological conditions.

  • Modeling and Control of Infectious Diseases in the Host

    With MATLAB and R
    • 1st Edition
    • February 14, 2019
    • Esteban A. Hernandez-Vargas
    • English
    Modeling and Control of Infectious Diseases in the Host: With MATLAB and R provides a holistic understanding of health and disease by presenting topics on quantitative decision-making that influence the development of drugs. The book presents modeling advances in different viral infections, dissecting detailed contributions of key players, along with their respective interactions. By combining tailored in vivo experiments and mathematical modeling approaches, the book clarifies the relative contributions of different underlying mechanisms within hosts of the most lethal viral infections, including HIV, influenza and Ebola. Illustrative examples for parameter fitting, modeling and control applications are explained using MATLAB and R.

  • Bioinformatics Algorithms

    Design and Implementation in Python
    • 1st Edition
    • June 8, 2018
    • Miguel Rocha + 1 more
    • English
    Bioinformatics Algorithms: Design and Implementation in Python provides a comprehensive book on many of the most important bioinformatics problems, putting forward the best algorithms and showing how to implement them. The book focuses on the use of the Python programming language and its algorithms, which is quickly becoming the most popular language in the bioinformatics field. Readers will find the tools they need to improve their knowledge and skills with regard to algorithm development and implementation, and will also uncover prototypes of bioinformatics applications that demonstrate the main principles underlying real world applications.

  • Personalized Predictive Modeling in Type 1 Diabetes

    • 1st Edition
    • November 29, 2017
    • Eleni I. Georga + 2 more
    • English
    Personalized Predictive Modeling in Diabetes features state-of-the-art methodologies and algorithmic approaches which have been applied to predictive modeling of glucose concentration, ranging from simple autoregressive models of the CGM time series to multivariate nonlinear regression techniques of machine learning. Developments in the field have been analyzed with respect to: (i) feature set (univariate or multivariate), (ii) regression technique (linear or non-linear), (iii) learning mechanism (batch or sequential), (iv) development and testing procedure and (v) scaling properties. In addition, simulation models of meal-derived glucose absorption and insulin dynamics and kinetics are covered, as an integral part of glucose predictive models. This book will help engineers and clinicians to: select a regression technique which can capture both linear and non-linear dynamics in glucose metabolism in diabetes, and which exhibits good generalization performance under stationary and non-stationary conditions; ensure the scalability of the optimization algorithm (learning mechanism) with respect to the size of the dataset, provided that multiple days of patient monitoring are needed to obtain a reliable predictive model; select a features set which efficiently represents both spatial and temporal dependencies between the input variables and the glucose concentration; select simulation models of subcutaneous insulin absorption and meal absorption; identify an appropriate validation procedure, and identify realistic performance measures.

  • Multi-Scale Approaches in Drug Discovery

    From Empirical Knowledge to In silico Experiments and Back
    • 1st Edition
    • February 14, 2017
    • Alejandro Speck-Planche
    • English
    Drug discovery is an expensive, time-consuming process and the modern drug discovery community is constantly challenged not only with discovering novel bioactive agents to combat resistance from known diseases and fight against new ones, but to do so in a way that is economically effective. Advances in both experimental and theoretical/computat... methods envisage that the greatest challenges in drug discovery can be most successfully addressed by using a multi-scale approach, drawing on the specialties of a whole host of different disciplines. Multi-Scale Approaches to Drug Discovery furnishes chemists with the detail they need to identify drug leads with the highest potential before isolating and synthesizing them to produce effective drugs with greater swiftness than classical methods may allow. This significantly speeds up the search for more efficient therapeutic agents. After an introduction to multi-scale approaches outlining the need for and benefits of their use, the book goes on to explore a range of useful techniques and research areas, and their potential applications to this process. Profiling drug binding by thermodynamics, machine learning for predicting enzyme sub-classes, and multitasking models for computer-aided design and virtual compound screening are discussed, before the book goes on to review Alkaloid Menispermaceae leads, natural chemotherapeutic agents and methods for speeding up the design and virtual screening of therapeutic peptides. Flavonoids as multi-target compounds are then explored, before the book concludes with a review of Quasi-SMILES as a novel tool. Collecting together reviews and original research contributions written by leading experts in the field, Multi-Scale Approaches to Drug Discovery highlights cutting-edge approaches and practical examples of their implementation for those involved in the drug discovery process at many different levels. Using the combined knowledge of medicinal, computational, pharmaceutical and bio- chemists, it aims to support growth in the multi-scale approach to promote greater success in the development of new drugs.

  • Translational Systems Biology

    Concepts and Practice for the Future of Biomedical Research
    • 1st Edition
    • October 8, 2014
    • Yoram Vodovotz + 1 more
    • English
    Are we satisfied with the rate of drug development? Are we happy with the drugs that come to market? Are we getting our money’s worth in spending for basic biomedical research? In Translational Systems Biology, Drs. Yoram Vodovotz and Gary An address these questions by providing a foundational description the barriers facing biomedical research today and the immediate future, and how these barriers could be overcome through the adoption of a robust and scalable approach that will form the underpinning of biomedical research for the future. By using a combination of essays providing the intellectual basis of the Translational Dilemma and reports of examples in the study of inflammation, the content of Translational Systems Biology will remain relevant as technology and knowledge advances bring broad translational applicability to other diseases. Translational systems biology is an integrated, multi-scale, evidence-based approach that combines laboratory, clinical and computational methods with an explicit goal of developing effective means of control of biological processes for improving human health and rapid clinical application. This comprehensive approach to date has been utilized for in silico studies of sepsis, trauma, hemorrhage, and traumatic brain injury, acute liver failure, wound healing, and inflammation.

  • Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System

    Biomaterials and Tissues
    • 1st Edition
    • April 8, 2014
    • Z Jin
    • English
    Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System reviews how a wide range of materials are modelled and how this modelling is applied. Computational modelling is increasingly important in the design and manufacture of biomedical materials, as it makes it possible to predict certain implant-tissue reactions, degradation, and wear, and allows more accurate tailoring of materials' properties for the in vivo environment. Part I introduces generic modelling of biomechanics and biotribology with a chapter on the fundamentals of computational modelling of biomechanics in the musculoskeletal system, and a further chapter on finite element modelling in the musculoskeletal system. Chapters in Part II focus on computational modelling of musculoskeletal cells and tissues, including cell mechanics, soft tissues and ligaments, muscle biomechanics, articular cartilage, bone and bone remodelling, and fracture processes in bones. Part III highlights computational modelling of orthopedic biomaterials and interfaces, including fatigue of bone cement, fracture processes in orthopedic implants, and cementless cup fixation in total hip arthroplasty (THA). Finally, chapters in Part IV discuss applications of computational modelling for joint replacements and tissue scaffolds, specifically hip implants, knee implants, and spinal implants; and computer aided design and finite element modelling of bone tissue scaffolds. This book is a comprehensive resource for professionals in the biomedical market, materials scientists and mechanical engineers, and those in academia.

  • Computational Neuroscience

    • 1st Edition
    • Volume 123
    • February 18, 2014
    • English
    Progress in Molecular Biology and Translational Science provides a forum for discussion of new discoveries, approaches, and ideas in molecular biology. It contains contributions from leaders in their fields and abundant references. This volume brings together different aspects of, and approaches to, molecular and multi-scale modeling, with applications to a diverse range of neurological diseases. Mathematical and computational modeling offers a powerful approach for examining the interaction between molecular pathways and ionic channels in producing neuron electrical activity. It is well accepted that non-linear interactions among diverse ionic channels can produce unexpected neuron behavior and hinder a deep understanding of how ion channel mutations bring about abnormal behavior and disease. Interactions with the diverse signaling pathways activated by G protein coupled receptors or calcium influx adds an additional level of complexity. Modeling is an approach to integrate myriad data sources into a cohesive and quantitative model in order to evaluate hypotheses about neuron function. In particular, a validated model developed using in vitro data allows simulations of the response to in vivo like spatio-temporal patterns of synaptic input. Incorporating molecular signaling pathways into an electrical model, allows a greater range of models to be developed, ones that can predict the response to pharmaceuticals, many of which target neuromodulator pathways.