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:
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.
Why and How Biological Systems Differ from Their Counterparts?
Factors in Biological Systems
What Is Optimization?
Non-Statistical Experimental Design
Steps in Designing an Experiment
Response Surface Experimental Designs
Principal Objective of Response Surface Method
Types of Response Surfaces
Classification of Response Surface Designs
Statistical Analysis of Experimental Designs and Optimization of Process Variables
Analysis of Experimental Designs
To Find Optimal Conditions of Experimental Variables for the Bioprocesses
Evolutionary Operation Programmes
Classification of EVOP
Worksheet for EVOP
Aim of Taguchi’s Design
Experimental Designs versus Taguchi’s Design
Basis of Taguchi’s Design Technique
Classes of Optimization Problems
Array in Orthogonal Design
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
Hybrid Experimental Design Based on a Genetic Algorithm
Need for Search Algorithms
Limitations of Genetic Algorithm
How GA Finds Uses in Biological Systems?
Hybrid Design of Experiments Based on GA
Relevant Problems and Their Solution
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.