Population Balances in Biomedical Engineering

The process of separating and classifying individual cell populations is a difficult task faced by engineers and scientists when creating biomaterials. This resource focuses on overcoming obstacles to help researchers and scientists build computer models that accurately approximate properties and classification of different populations. A complete mathematical guide to creating models for the distribution of properties of cell populations Population Balances in Biomedical Engineering: Segregation through the Distribution of Cell States offers a framework for developing and solving simple models that are essential to determining the distribution of properties of cell populations and analyzing their underlying dynamic behavior. This rigorous resource takes a quantitative approach to overcoming obstacles that researchers face in building models that accurately approximate properties and classification of various cell populations. Coverage includes: Population balancing techniques for filamentous organisms Control points of steady-state and transient solutions Methods for analysis of the underlying behavior of cellular systems A Powerful Statistical Approach to Population Balance Modeling: * Population balance equations * Unstructured population balance equations * PBEs with control points: steady-state solutions * PBEs with control points: transient solutions * PBEs for filamentous organisms * Distributed breakage functions * Alternate formulations Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. The population balance modeling is a statistical approach for achieving accurate counts of any populations. It is an efficient way of counting traffic on roadways as well as to bacteria in lakes. In the biomedical world, it is used to count cell populations for the creation of biomaterials. Despite their undisputed accuracy, they have been underutilized for design and control purposes due to two main reasons: a) they are hard to solve and b) the functions that describe single-cell mechanisms and appear as parameters in these models are typically unknown.

A.Martin Hjortso (Auteur) - Verschenen op 01/09/2005 bij Mc Graw-Hill Gb