Statistical Optimization of Biological Systems

Statistical Optimization of Biological Systems

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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 modelingDemonstrates the statistical analysis and optimization of different experimental designs, the results of which are used to establish important variables and optimum settingsDetails the optimization techniques employed to determine optimum levels of the process variables for both single- and multiple-response systemsDiscusses important experimental designs, such as evolutionary operation programs and Taguchi’s designsDelineates the concept of hybrid experimental design using the essence of a genetic algorithmStatistical 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 more

Product details

  • Paperback
  • 155.96 x 233.93mm
  • CRC Pr I Llc
  • English
  • 1138893137
  • 9781138893139

Table of contents

IntroductionWhy and How Biological Systems Differ from Their Counterparts?Factors in Biological SystemsTerminologiesWhat Is Optimization?ExercisesReferences Non-Statistical Experimental DesignIntroductionSteps in Designing an ExperimentExercisesReferencesFurther Reading Response Surface Experimental DesignsIntroductionPrincipal Objective of Response Surface MethodDrawbackTypes of Response SurfacesClassification of Response Surface DesignsFirst-Order DesignsNon-Geometric DesignSecond-Order DesignsExercisesReferences Statistical Analysis of Experimental Designs and Optimization of Process VariablesIntroductionAnalysis of Experimental DesignsTo Find Optimal Conditions of Experimental Variables for the BioprocessesExercisesReferencesFurther Reading Evolutionary Operation ProgrammesIntroductionClassification of EVOPSpecific TerminologiesWorksheet for EVOPResponse SurfaceExercisesReferences Taguchi’s DesignIntroductionAim of Taguchi’s DesignExperimental Designs versus Taguchi’s DesignBasis of Taguchi’s Design TechniqueClasses of Optimization ProblemsTerminologiesArray in Orthogonal DesignSignal-to-Noise RatioOrthogonal ArrayTaguchi’s MethodANOVA for Optimization of Experimental Parameters Using a Taguchi Design of ExperimentLimitation in Taguchi’s DesignOutcome of Taguchi’s DesignApplication of Taguchi’s DesignExerciseReferencesFurther Reading Hybrid Experimental Design Based on a Genetic AlgorithmIntroductionNeed for Search AlgorithmsMethodTerminologiesLimitations of Genetic AlgorithmHow GA Finds Uses in Biological Systems?Hybrid Design of Experiments Based on GARelevant Problems and Their SolutionExerciseReferencesFurther Readingshow more

About Tapobrata Panda

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 more