Computational Biology And Machine Learning For Metabolic Engineering And Synthetic Biology

This volume provides protocols for computational, statistical, and machine learning methods that are mainly applied to the study of metabolic engineering, synthetic biology, and disease applications. These techniques support the latest progress in cross-disciplinary research that integrates the different scales of biological complexity. The topics covered in this book are geared toward researchers with a background in engineering, computational analytical, and modeling experience and cover a broad range of topics in computational and machine learning approaches. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Comprehensive and practical, Computational Biology and Machine Learning for Metabolic Engineering and Synthetic Biology is a valuable resource for any researcher or scientist who wants to learn more about the latest computational methods and how they are applied toward the understanding and prediction of complex biology.

Systems and Synthetic Metabolic Engineering provides an overview of the development of metabolic engineering within medicine that is fueled by systems and synthetic biology. These newly developed, successful strategies of metabolic engineering guide the audience on how to propose and test proper strategies for metabolic engineering research. In addition to introductory, regulatory and challenges in the field, the book also covers dynamic control and autonomous regulation to control cell metabolism, along with computational modeling and industrial applications. The book is written by leaders in the field, making it ideal for synthetic biologists, researchers, students and anyone working in this area. Discusses the current progress of metabolic engineering, focusing on systems biology and synthetic biology Covers introductory, regulatory, strategies, production and challenges in the field Written technically for synthetic biologists, researchers, students, industrialists, policymakers and stakeholders

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 book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.

Emphasises a hands-on approach to modelling Strong emphasis on coding and software tools for systems biology Covers the entire spectrum of modelling, from static networks, to dynamic models Thoughtful exercises to test and enable student understanding of concepts Current chapters on exciting new developments like whole-cell modelling and community modelling

Metabolic systems engineering combines the tools and approaches of systems biology, synthetic biology, and evolutionary engineering. This book reviews studies on metabolism, from the earliest work of Lavoisier and Buchner to current cutting-edge research in metabolic systems engineering. This technology has been used in bioengineering applications to create high-performing microbes and plants that produce important chemicals, pharmaceuticals, crops, and other natural products. Current applications include optimizing metabolic pathways to enhance degradation of biomass for biofuel production and accelerated processing of environmental waste products and contaminants. The book includes examples to illustrate the applications of this technology in the optimization of metabolic pathways to create robust industrial strains as well as in the engineering of biological processes involving health and diseases of humans, animals, and plants. Written by a seasoned computational biologist with many years of experience in genomics, bioinformatics, and systems biology, this book will appeal to anyone interested in metabolic systems analysis and metabolic pathway engineering.

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.

This text not only describes the current developments in systems biology, systems synthetic biology, and systems metabolic engineering for engineering and biology researchers but also discusses challenges and future prospects for systems biology and the concept of systems biology as an integrated platform for bioinformatics, systems synthetic biology, and systems metabolic engineering.