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Statistical Optimization of Biological Systems

Edition:
1st
Author(s):
Tapobrata Panda, Thomas Theodore, R. Arun Kumar
ISBN:
9781466587083
Format:
Hardback
Publication Date:
November 05, 2015
Content Details:
278 pages
Language:
English

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  • About the Book

    Book Summary

    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.

    Features

      • Examines the complex nature of biological systems, the need for optimization, and the rationale of statistical and non-statistical optimization methods
      • Explains how to successfully apply mathematical and statistical techniques to the optimization of biological systems
      • Employs real-life bioprocess optimization problems and their solutions as examples
  • Contents

    Introduction
    Why and How Biological Systems Differ from Their Counterparts?
    Factors in Biological Systems
    Terminologies
    What Is Optimization?
    Exercises
    References

    Non-Statistical Experimental Design
    Introduction
    Steps in Designing an Experiment
    Exercises
    References
    Further Reading

    Response Surface Experimental Designs
    Introduction
    Principal Objective of Response Surface Method
    Drawback
    Types of Response Surfaces
    Classification of Response Surface Designs
    First-Order Designs
    Non-Geometric Design
    Second-Order Designs
    Exercises
    References

    Statistical Analysis of Experimental Designs and Optimization of Process Variables
    Introduction
    Analysis of Experimental Designs
    To Find Optimal Conditions of Experimental Variables for the Bioprocesses
    Exercises
    References
    Further Reading

    Evolutionary Operation Programmes
    Introduction
    Classification of EVOP
    Specific Terminologies
    Worksheet for EVOP
    Response Surface
    Exercises
    References

    Taguchi’s Design
    Introduction
    Aim of Taguchi’s Design
    Experimental Designs versus Taguchi’s Design
    Basis of Taguchi’s Design Technique
    Classes of Optimization Problems
    Terminologies
    Array in Orthogonal Design
    Signal-to-Noise Ratio
    Orthogonal Array
    Taguchi’s Method
    ANOVA for Optimization of Experimental Parameters Using a Taguchi Design of Experiment
    Limitation in Taguchi’s Design
    Outcome of Taguchi’s Design
    Application of Taguchi’s Design
    Exercise
    References
    Further Reading

    Hybrid Experimental Design Based on a Genetic Algorithm
    Introduction
    Need for Search Algorithms
    Method
    Terminologies
    Limitations of Genetic Algorithm
    How GA Finds Uses in Biological Systems?
    Hybrid Design of Experiments Based on GA
    Relevant Problems and Their Solution
    Exercise
    References
    Further Reading