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Books in Mathematical biosciences

The Mathematical Biosciences portfolio integrates advanced mathematical modelling, computational biology, systems biology, and data analysis. Featuring interdisciplinary research, these resources support scientists, bioinformaticians, and students in understanding complex biological processes, disease dynamics, and ecological systems. Emphasizing innovation in quantitative methods, the portfolio offers actionable insights for tackling global health issues, environmental challenges, and biotechnological advancements through rigorous, data-driven approaches.

BooksJournals

  • Geometric Morphometrics for Biologists

    A Primer
    • 3rd Edition
    • Miriam Zelditch + 1 more
    • English
    Geometric Morphometrics for Biologists: A Primer, Third Edition explores how these geometric methods allow for the study of biological shape by incorporating the geometry of organisms throughout data analyses. Novel content on phylogenetic comparative methods, evolutionary morphology, analyses of disparity, genetics of shape, and simulating shape data is included. This edition delivers step-by-step guidance on how to effectively utilize updated analytical software while also providing guidance on important methodologies that remain relevant to the field to ensure clear and effective instruction to audiences new to this branch of data analysis.It succeeds the second edition as the primary resource for teaching modern geometric methods of shape analysis to students, researchers, and practicing scientists in the field of organismal biology. Users will find it to be an immensely valuable introduction for students and practicing biologists interested in learning applications of multivariate statistics and matrix algebra to organismal biology.

  • Computational Modelling of Biomechanics and Biotribology in the Musculoskeletal System

    Biomaterials and Tissues
    • 2nd Edition
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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.

  • A Historical Introduction to Mathematical Modeling of Infectious Diseases

    Seminal Papers in Epidemiology
    • 1st Edition
    • Ivo M. Foppa
    • English
    A Historical Introduction to Mathematical Modeling of Infectious Diseases: Seminal Papers in Epidemiology offers step-by-step help on how to navigate the important historical papers on the subject, beginning in the 18th century. The book carefully, and critically, guides the reader through seminal writings that helped revolutionize the field. With pointed questions, prompts, and analysis, this book helps the non-mathematician develop their own perspective, relying purely on a basic knowledge of algebra, calculus, and statistics. By learning from the important moments in the field, from its conception to the 21st century, it enables readers to mature into competent practitioners of epidemiologic modeling.

  • Mathematical Biology

    A Conference on Theoretical Aspects of Molecular Science
    • 1st Edition
    • T. A. Burton
    • English
    Mathematical Biology: A Conference on Theoretical Aspects of Molecular Science is a collection of papers that covers various investigations in mathematical biology. The text tackles a wide range of topics, from biological equation models up to electrical phenomena in biological systems. The coverage of the text includes existence of a periodic solution for a two predator-one prey ecosystem modeled on a chemostat; mathematical treatment of nerve conduction and cardiac purkinje fibers; and models of positional information. The book will be of great interest to students, researchers, and practitioners of biological sciences.

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