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.
