Main description:

A number of books written by statisticians address the mathematical optimization of biological systems, but do not directly address statistical optimization. Statistical Optimization of Biological Systems covers the optimization of bioprocess systems in its entirety, devoting much-needed attention to the experimental optimization of biological systems using statistical techniques. Employing real-life bioprocess optimization problems and their solutions as examples, this book:

Describes experimental design from identifying process variables to selecting a screening design, applying response surface methodology, and conducting regression modeling
Demonstrates the statistical analysis and optimization of different experimental designs, the results of which are used to establish important variables and optimum settings
Details the optimization techniques employed to determine optimum levels of the process variables for both single- and multiple-response systems
Discusses important experimental designs, such as evolutionary operation programs and Taguchi's designs
Delineates the concept of hybrid experimental design using the essence of a genetic algorithm

Statistical Optimization of Biological Systems examines the complex nature of biological systems, the need for optimization, and the rationale of statistical and non-statistical optimization methods. More importantly, the book explains how to successfully apply mathematical and statistical techniques to the optimization of biological systems.

Contents:

Introduction. Non-Statistical Experimental Design. Response Surface Experimental Designs. Statistical Analysis of Experimental Designs and Optimization of Process Variables. Evolutionary Operation Programmes. Taguchi's Design. Hybrid Experimental Design Based on a Genetic Algorithm.