Modeling Methodology for Physiology and Medicine, 2nd Edition

  • Published By:
  • ISBN-10: 0124095259
  • ISBN-13: 9780124095250
  • DDC: 571.015118
  • Grade Level Range: College Freshman - College Senior
  • 588 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Preface to the Second Edition.
List of Contributors.
1: An Introduction to Modelling Methodology.
2: Control in Physiology and Medicine.
3: Deconvolution.
4: Structural Identifiability of Biological and Physiological Systems.
5: Parameter Estimation.
6: New Trends in Nonparametric Linear System Identification1.
7: Population Modelling.
8: Systems Biology.
9: Reverse Engineering of High-Throughput Genomic and Genetic Data.
10: Tracer Experiment Design for Metabolic Fluxes Estimation in Steady and Nonsteady State.
11: Stochastic Models of Physiology.
12: Probabilistic Modelling with Bayesian Networks.
13: Mathematical Modelling of Pulmonary Gas Exchange.
14: Mathematical Models for Computational Neuroscience.
15: Insulin Modelling.
16: Glucose Modelling.
17: Blood–Tissue Exchange Modelling.
18: Physiological Modelling of Positron Emission Tomography Images.
19: Tumor Growth Modelling for Drug Development.
20: Computational Modelling of Cardiac Biomechanics.
21: Downstream from the Heart Left Ventricle: Aortic Impedance Interpretation by Lumped and Tube-Load Models.
22: Finite Element Modelling in Musculoskeletal Biomechanics.
23: Modelling for Synthetic Biology.