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Experimentalists tend to revel in the complexity and multidimensionality of biological processes. Modelers, on the other hand, generally look towards parsimony as a guiding prin ciple in their approach to understanding physiological systems. It is therefore not surprising that a substantial degree of miscommunication and misunderstanding still exists between the two groups of truth-seekers. However, there have been numerous instances in physiology where the marriage of mathematical modeling and experimentation has led to powerful in sights into the mechanisms being studied. Respiratory control represents one area in which this kind of cross-pollination has proven particularly fruitful. While earlier modeling ef forts were directed primarily at the chemical control of ventilation, more recent studies have extended the scope of modeling to include the neural and mechanical aspects pertinent to respiratory control. As well, there has been a greater awareness of the need to incorpo rate interactions with other organ systems. Nevertheless, it is necessary from time to time to remind experimentalists of the existence of modelers, and vice versa. The 4th Annual Biomedical Simulations Resource (BMSR) Short Course was held in Marina Del Rey on May 21-22,1989, to acquaint respiratory physiologists and clinical researchers with state-of-the art methodologies in mathematical modeling, experiment design and data analysis, as well as to provide an opportunity for experimentalists to challenge modelers with their more recent findings.
This volume synthesizes theoretical and practical aspects of both the mathematical and life science viewpoints needed for modeling of the cardiovascular-respiratory system specifically and physiological systems generally. Theoretical points include model design, model complexity and validation in the light of available data, as well as control theory approaches to feedback delay and Kalman filter applications to parameter identification. State of the art approaches using parameter sensitivity are discussed for enhancing model identifiability through joint analysis of model structure and data. Practical examples illustrate model development at various levels of complexity based on given physiological information. The sensitivity-based approaches for examining model identifiability are illustrated by means of specific modeling examples. The themes presented address the current problem of patient-specific model adaptation in the clinical setting, where data is typically limited.
In recent years capnography has gained a foothold in the medical field and is fast becoming a standard of care in anaesthesiology and critical care medicine. In addition, newer applications have emerged which have expanded the utility of capnographs in a number of medical disciplines. This new edition of the definitive text on capnography reviews every aspect of this valuable diagnostic technique. An introductory section summarises the basic physiology of carbon dioxide generation and transport in the body. A technical section describes how the instruments work, and a comprehensive clinical section reviews the use of capnography to diagnose a wide range of clinical disorders. Edited by the world experts in the technique, and with over 40 specialist contributors, Capnography, second edition, is the most comprehensive review available on the application of capnography in health care.
Eleven chapters, written by experts in their respective fields, on topics ranging from control of the Navier-Stokes equations to nondestructive evaluation - all of which are modeled by distributed parameter systems.
This must-read text/reference provides a practical guide to processes involved in the development and application of dynamic simulation models, covering a wide range of issues relating to testing, verification and validation. Illustrative example problems in continuous system simulation are presented throughout the book, supported by extended case studies from a number of interdisciplinary applications. Topics and features: provides an emphasis on practical issues of model quality and validation, along with questions concerning the management of simulation models, the use of model libraries, and generic models; contains numerous step-by-step examples; presents detailed case studies, often with accompanying datasets; includes discussion of hybrid models, which involve a combination of continuous system and discrete-event descriptions; examines experimental modeling approaches that involve system identification and parameter estimation; offers supplementary material at an associated website.