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.

Autorentext

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.

Inhalt

1. Introduction to engineering biology 1.1. The engineering waves of biology: genetic, genomics, systems and synthetic 1.2. Industrial biotechnology in revolutions 1.3. The present: Design-Build-Test-Learn foundries 1.4. The future: automation, cloud biotechnology and artificial intelligence 2. Understanding the cell: genome-scale engineering 2.1. Systems biology models 2.2. Model reconstruction from omics to big data 2.3. Model simulation through constraint-based approaches 2.4. Modeling dynamics 3. Sources of natural chemical diversity 3.1. Understanding the mechanisms of enzyme innovation and adaptation 3.2. Knowledge-based encodings for chemical reactions 3.3. Modeling enzyme promiscuity using reaction rules and molecular signatures 3.4. Enumerating chemical diversity 4. Enzyme discovery and selection 4.1. Discovery through sequence homology 4.2. Discovery through reaction homology 4.3. Screening and selection through directed evolution 5. The metabolic space 5.1. Metabolic phenotypes 5.2. The metabolic spaces of the biosphere 5.3. Extended, non-natural and outer metabolic spaces 6. Pathway discovery 6.1. Defining chemical targets 6.2. Retrosynthetic analysis of the metabolic scope 6.3. Pathway enumeration 6.4. Pathway ranking 7. Pathway design 7.1. Pathway selection 7.2. Enzyme selection 7.3. Genetic parts selection 7.4. Combinatorial design 7.5. Experimental design 8. Chassis redesign 8.1. Knock-outs 8.2. Knock-ins 8.3. Knowledge-based redesign 9. Learning and adaptation 9.1. Principles of machine learning 9.2. Deep learning 9.3. Smart parts selection 9.4. Smart experimental redesign 10. Scaling-up and derivatization 10.1. Scale-up 10.2 Derivatization 10.3 Agile biodesign