Overview
- Offers real-world, hands-on examples of scientific coding
- Written for biologists and engineers alike
- Focuses on the Design-Build-Test-Learn cycle applied to metabolic engineering
- Includes lab protocols to explain the integration of modelling approaches
- Provides insights into automating full processes
- Introduces machine learning as part of pathway design
Part of the book series: Learning Materials in Biosciences (LMB)
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Table of contents (9 chapters)
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Front Matter
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Metabolic Pathway Modeling
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Front Matter
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Metabolic Pathway Discovery
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Front Matter
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Metabolic Pathway Design
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Front Matter
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Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the author
Pablo Carbonell is a senior staff scientist at the SynBioChem Centre, Manchester Institute of Biotechnology. His field of research is automated design for metabolic engineering and synthetic biology. Pablo has developed several bioretrosynthesis-based pathway design tools, including RetroPath, XTMS, EcoliTox, Selenzyme for enzyme selection and Promis for protein design. He is interested in applying the principles of machine learning and control engineering to sustainable biological design. He has contributed to the development of several theoretical models for bio-based, bionics systems – from biosensors to robotic exoskeletons.
Bibliographic Information
Book Title: Metabolic Pathway Design
Book Subtitle: A Practical Guide
Authors: Pablo Carbonell
Series Title: Learning Materials in Biosciences
DOI: https://doi.org/10.1007/978-3-030-29865-4
Publisher: Springer Cham
eBook Packages: Biomedical and Life Sciences, Biomedical and Life Sciences (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Softcover ISBN: 978-3-030-29864-7Published: 14 November 2019
eBook ISBN: 978-3-030-29865-4Published: 05 November 2019
Series ISSN: 2509-6125
Series E-ISSN: 2509-6133
Edition Number: 1
Number of Pages: X, 168
Number of Illustrations: 44 illustrations in colour
Topics: Biomedical Engineering/Biotechnology, Bioinformatics, Computational Biology/Bioinformatics, Cell Biology, Systems Biology