1st Edition

Statistical Optimization of Biological Systems





ISBN 9781138893139
Published July 26, 2017 by CRC Press

USD $75.95

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Book 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.

Table of 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

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Author(s)

Biography

Tapobrata Panda is a Professor at the Indian Institute of Technology Madras, Chennai, India. He received a BSc (honors) in Chemistry from the University of Calcutta, Kolkata, India; a BTech and MTech in Food Technology and Biochemical Engineering from Jadavpur University, Kolkata, India; and a PhD in Biochemical Engineering from the Indian Institute of Technology Delhi, New Delhi. Professor Panda is widely published and a member of several journals’ editorial boards. His papers have an ‘h’-index (Google Scholar) of 30 and ‘i-10’ value of 64. His areas of interest include hybrid experimental design, bio-MEMS, biological synthesis of nanoparticles, and design of therapeutic molecules and enzymes.

R. Arun Kumar is currently working with an oil and gas super major in liquefied natural gas business as a Process Engineer. Previously, he worked for an international oil and gas service company. He received a BTech in Chemical Engineering from the Indian Institute of Technology Madras, Chennai, India; and was in the top 1% of the National Astronomy and Physics Olympiad. His areas of interest include biochemical engineering, genetic algorithms applied to biological systems, and design of experiments.

Thomas Théodore is an Associate Professor of Chemical Engineering at the Siddaganga Institute of Technology, Tumkur, India. He received Chemical Engineering degrees from Annamalai University, Chidambaram, India, and Alagappa College of Technology, Chennai, India; an MS in Bioengineering from the École Supérieure de Physique et de Chimie Industrielles de la Ville de Paris, France; an MEngSc in Biopharmaceutical Engineering from University College Dublin, Ireland; and a PhD in Biochemical Engineering from the Indian Institute of Technology Madras, Chennai, India. His areas of interest include therapeutic proteins and biodegradable polymers.