Statistical Optimization of Biological Systems: 1st Edition (Paperback) book cover

Statistical Optimization of Biological Systems

1st Edition

By Tapobrata Panda, Thomas Theodore, R. Arun Kumar

CRC Press

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pub: 2017-07-26
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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

About the Authors

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.

Subject Categories

BISAC Subject Codes/Headings:
MED009000
MEDICAL / Biotechnology
MED090000
MEDICAL / Biostatistics
SCI010000
SCIENCE / Biotechnology