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Statistical Optimization of Biological Systems de Tapobrata Panda

Statistical Optimization of Biological Systems

Autor Tapobrata Panda, Thomas Theodore, R. Arun Kumar
en Limba Engleză Paperback – 26 iul 2017
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.
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Specificații

ISBN-13: 9781138893139
ISBN-10: 1138893137
Pagini: 296
Dimensiuni: 156 x 234 mm
Greutate: 0.43 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Cuprins

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.

Notă biografică

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.

Descriere

This book explains how to apply non-statistical and statistical techniques to the optimization of biological systems. Employing real-life bioprocess optimization problems and their solutions as examples, the text describes experimental design from identifying process variables to selecting a screening design, applying response surface methodology, and conducting regression modeling. It demonstrates the statistical analysis and optimization of different experimental designs, details the optimization techniques employed to determine optimum levels of the process variables for both single- and multiple-response systems, and discusses important experimental designs.