Facility in the targeted manipulation of the genetic and metabolic composition of organisms, combined with unprecedented computational power, is forging a niche for a new subspecialty of biotechnology called metabolic engineering. First published in 2002, this book introduces researchers and advanced students in biology and engineering to methods of optimizing biochemical systems of biotechnological relevance. It examines the development of strategies for manipulating metabolic pathways, demonstrates the need for effective systems models, and discusses their design and analysis, while placing special emphasis on optimization. The authors propose power-law models and methods of biochemical systems theory toward these ends. All concepts are derived from first principles, and the text is richly illustrated with numerous graphs and examples throughout. Special features include: nontechnical and technical introductions to models of biochemical systems; a review of basic methods of model design and analysis; concepts of optimization; and detailed case studies.

Systems Metabolic Engineering is changing the way microbial cell factories are designed and optimized for industrial production. Integrating systems biology and biotechnology with new concepts from synthetic biology enables the global analysis and engineering of microorganisms and bioprocesses at super efficiency and versatility otherwise not accessible. Without doubt, systems metabolic engineering is a major driver towards bio-based production of chemicals, materials and fuels from renewables and thus one of the core technologies of global green growth. In this book, Christoph Wittmann and Sang-Yup Lee have assembled the world leaders on systems metabolic engineering and cover the full story – from genomes and networks via discovery and design to industrial implementation practises. This book is a comprehensive resource for students and researchers from academia and industry interested in systems metabolic engineering. It provides us with the fundaments to targeted engineering of microbial cells for sustainable bio-production and stimulates those who are interested to enter this exiting research field.

Provides a tutorial on the computational tools that use mathematical optimization concepts and representations for the curation, analysis and redesign of metabolic networks Organizes, for the first time, the fundamentals of mathematical optimization in the context of metabolic network analysis Reviews the fundamentals of different classes of optimization problems including LP, MILP, MLP and MINLP Explains the most efficient ways of formulating a biological problem using mathematical optimization Reviews a variety of relevant problems in metabolic network curation, analysis and redesign with an emphasis on details of optimization formulations Provides a detailed treatment of bilevel optimization techniques for computational strain design and other relevant problems

Learn more about foundational and advanced topics in metabolic engineering in this comprehensive resource edited by leaders in the field Metabolic Engineering: Concepts and Applications delivers a one-stop resource for readers seeking a complete description of the concepts, models, and applications of metabolic engineering. This guide offers practical insights into the metabolic engineering of major cell lines, including E. Coli, Bacillus and Yarrowia Lipolytica, and organisms, including human, animal, and plant). The distinguished editors also offer readers resources on microbiome engineering and the use of metabolic engineering in bioremediation. Written in two parts, Metabolic Engineering begins with the essential models and strategies of the field, like Flux Balance Analysis, Quantitative Flux Analysis, and Proteome Constrained Models. It also provides an overview of topics like Pathway Design, Metabolomics, and Genome Editing of Bacteria and Eukarya. The second part contains insightful descriptions of the practical applications of metabolic engineering, including specific examples that shed light on the topics within. In addition to subjects like the metabolic engineering of animals, humans, and plants, you’ll learn more about: Metabolic engineering concepts and a historical perspective on their development The different modes of analysis, including flux balance analysis and quantitative flux analysis An illuminating and complete discussion of the thermodynamics of metabolic pathways The Genome architecture of E. coli, as well as genome editing of both bacteria and eukarya An in-depth treatment of the application of metabolic engineering techniques to organisms including corynebacterial, bacillus, and pseudomonas, and more Perfect for students of biotechnology, bioengineers, and biotechnologists, Metabolic Engineering: Concepts and Applications also has a place on the bookshelves of research institutes, biotechnological institutes and industry labs, and university libraries. It's comprehensive treatment of all relevant metabolic engineering concepts, models, and applications will be of use to practicing biotechnologists and bioengineers who wish to solidify their understanding of the field.

This book review series presents current trends in modern biotechnology. The aim is to cover all aspects of this interdisciplinary technology where knowledge, methods and expertise are required from chemistry, biochemistry, microbiology, genetics, chemical engineering and computer science. Volumes are organized topically and provide a comprehensive discussion of developments in the respective field over the past 3-5 years. The series also discusses new discoveries and applications. Special volumes are dedicated to selected topics which focus on new biotechnological products and new processes for their synthesis and purification. In general, special volumes are edited by well-known guest editors. The series editor and publisher will however always be pleased to receive suggestions and supplementary information. Manuscripts are accepted in English.

Yeast Metabolic Engineering: Methods and Protocols provides the widely established basic tools used in yeast metabolic engineering, while describing in deeper detail novel and innovative methods that have valuable potential to improve metabolic engineering strategies in industrial biotechnology applications. Beginning with an extensive section on molecular tools and technology for yeast engineering, this detailed volume is not limited to methods for Saccharomyces cerevisiae, but describes tools and protocols for engineering other yeasts of biotechnological interest, such as Pichia pastoris, Hansenula polymorpha and Zygosaccharomyces bailii. Tools and technologies for the investigation and determination of yeast metabolic features are described in detail as well as metabolic models and their application for yeast metabolic engineering, while a chapter describing patenting and regulations with a special glance at yeast biotechnology closes the volume. Written in the highly successful Methods in Molecular Biology series format, most chapters include an introduction to their respective topic, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols and tips on troubleshooting and avoiding known pitfalls. Comprehensive and authoritative, Yeast Metabolic Engineering: Methods and Protocols aims to familiarize researchers with the current state of these vital and increasingly useful technologies.

This textbook presents solid tools for in silico engineering biology, offering students a step-by-step guide to mastering the smart design of metabolic pathways. The first part explains the Design-Build-Test-Learn-cycle engineering approach to biology, discussing the basic tools to model biological and chemistry-based systems. Using these basic tools, the second part focuses on various computational protocols for metabolic pathway design, from enzyme selection to pathway discovery and enumeration. In the context of industrial biotechnology, the final part helps readers understand the challenges of scaling up and optimisation. By working with the free programming language Scientific Python, this book provides easily accessible tools for studying and learning the principles of modern in silico metabolic pathway design. Intended for advanced undergraduates and master’s students in biotechnology, biomedical engineering, bioinformatics and systems biology students, the introductory sections make it also useful for beginners wanting to learn the basics of scientific coding and find real-world, hands-on examples.

This volume highlights recent breakthroughs in the interdisciplinary areas of synthetic biology, metabolic engineering and bioprocess engineering for the production of green chemicals. It also presents practical experimental and computational tools for the design, construction and manipulation of cyanobacteria cell factories. The respective contributions cover new technologies in the field, such as novel genetic transformation techniques and bioinformatics analysis methods and address various aspects of cyanobacterial synthetic biology, offering a valuable resource for students and researchers in the fields of industry microbiology and biomedical engineering.

The genomic revolution has opened up systematic investigations and engineering designs for various life forms. Systems biology and synthetic biology are emerging as two complementary approaches, which embody the breakthrough in biology and invite application of engineering principles. Systems Biology and Synthetic Biology emphasizes the similarity between biology and engineering at the system level, which is important for applying systems and engineering theories to biology problems. This book demonstrates to students, researchers, and industry that systems biology relies on synthetic biology technologies to study biological systems, while synthetic biology depends on knowledge obtained from systems biology approaches.

Extract patterns and knowledge from your data in easy way using MATLAB About This Book Get your first steps into machine learning with the help of this easy-to-follow guide Learn regression, clustering, classification, predictive analytics, artificial neural networks and more with MATLAB Understand how your data works and identify hidden layers in the data with the power of machine learning. Who This Book Is For This book is for data analysts, data scientists, students, or anyone who is looking to get started with machine learning and want to build efficient data processing and predicting applications. A mathematical and statistical background will really help in following this book well. What You Will Learn Learn the introductory concepts of machine learning. Discover different ways to transform data using SAS XPORT, import and export tools, Explore the different types of regression techniques such as simple & multiple linear regression, ordinary least squares estimation, correlations and how to apply them to your data. Discover the basics of classification methods and how to implement Naive Bayes algorithm and Decision Trees in the Matlab environment. Uncover how to use clustering methods like hierarchical clustering to grouping data using the similarity measures. Know how to perform data fitting, pattern recognition, and clustering analysis with the help of MATLAB Neural Network Toolbox. Learn feature selection and extraction for dimensionality reduction leading to improved performance. In Detail MATLAB is the language of choice for many researchers and mathematics experts for machine learning. This book will help you build a foundation in machine learning using MATLAB for beginners. You'll start by getting your system ready with t he MATLAB environment for machine learning and you'll see how to easily interact with the Matlab workspace. We'll then move on to data cleansing, mining and analyzing various data types in machine learning and you'll see how to display data values on a plot. Next, you'll get to know about the different types of regression techniques and how to apply them to your data using the MATLAB functions. You'll understand the basic concepts of neural networks and perform data fitting, pattern recognition, and clustering analysis. Finally, you'll explore feature selection and extraction techniques for dimensionality reduction for performance improvement. At the end of the book, you will learn to put it all together into real-world cases covering major machine learning algorithms and be comfortable in performing machine learning with MATLAB. Style and approach The book takes a very comprehensive approach to enhance your understanding of machine learning using MATLAB. Sufficient real-world examples and use cases are included in the book to help you grasp the concepts quickly and apply them easily in your day-to-day work.