Claudio Cobelli – författare

Modelling Methodology for Physiology and Medicine, Second Edition, offers a unique approach and an unprecedented range of coverage of the state-of-the-art, advanced modeling methodology that is widely applicable to physiology and medicine. The second edition, which is completely updated and expanded, opens with a clear and integrated treatment of advanced methodology for developing mathematical models of physiology and medical systems. Readers are then shown how to apply this methodology beneficially to real-world problems in physiology and medicine, such as circulation and respiration.

The focus of Modelling Methodology for Physiology and Medicine, Second Edition, is the methodology that underpins good modeling practice. It builds upon the idea of an integrated methodology for the development and testing of mathematical models. It covers many specific areas of methodology in which important advances have taken place over recent years and illustrates the application of good methodological practice in key areas of physiology and medicine. It builds on work that the editors have carried out over the past 30 years, working in cooperation with leading practitioners in the field.

Builds upon and enhances the reader's existing knowledge of modeling methodology and practice Editors are internationally renowned leaders in their respective fields Provides an understanding of modeling methodologies that can address real problems in physiology and medicine and achieve results that are beneficial either in advancing research or in providing solutions to clinical problems

Introduction to Modeling in Physiology and Medicine, Second Edition, develops a clear understanding of the fundamental principles of good modeling methodology. Sections show how to create valid mathematical models that are fit for a range of purposes. These models are supported by detailed explanation, extensive case studies, examples and applications. This updated edition includes clearer guidance on the mathematical prerequisites needed to achieve the maximum benefit from the material, a greater detail regarding basic approaches to modeling, and discussions on non-linear and stochastic modeling. The range of case study material has been substantially extended, with examples drawn from recent research experience.

Key examples include a cellular model of insulin secretion and its extension to the whole-body level, a model of insulin action during a meal/oral glucose tolerance test, a large-scale simulation model of type 1 diabetes and its use in in silico clinical trials and drug trials.

Covers the underlying principles of good quantitative modeling methodology, with applied biomedical engineering and bioscience examples to ensure relevance to students, current research and clinical practice Includes modeling data, modeling systems, linear and non-linear systems, model identification, parametric and non-parametric models, and model validation Presents clear, step-by-step working plus examples and extensive case studies that relate concepts to real world applications Provides end-of-chapter exercises and assignments to reinforce learning

The use of mathematical modeling te chniques in biomedical research is playing an increasingly important role aso ne seeks tounderstand the physiopathology of disease processes. This includes not only understand ing mechanisms ofphysiological processes, but diagnosis and treatment. In addition, its introduction in the study of genomics andproteomics is key in understanding the functional ch aracteristics of gene expression and protein assembly and secretion. Finally, with the increasing com plexity and associated cost of drug development, modeling techniques are being used to streamline the process. We have workedin c lose collaboration with colleagues in biomedical and pharmaceutical research for a number of years applying and refining mathematical modeling techniques to avariety o f problems. In addition, we haveworked incollaboration with colleagues in applied m athematics and statistics to develop new algorithms to solve new setsof problems as theyemerge ino ur research efforts. Finally, we have worked with colleagues in computer sciencet o develop new software tools that bring the power of mathematical modeling to a broad research community.This books brings together mucho f what we have learned over the years, and presents the material in a format that should be accessible both to the novice reader and those desiring more detailed information about specific techniques.