Metabolic Pathway Design

This textbook provides solid tools for in silico engineering biology and easily guides the student step-by-step to mastering the smart design of metabolic pathways. In the first part an engineering approach to biology through the Design-Build-Test-Learn cycle will be explained, while basic tools to model both, biological systems and chemistry-based models, will be provided. Using these basic tools, the second part will focus on describing several computational protocols for metabolic pathway design, from enzyme selection to pathway discovery and enumeration. The last part brings back the context of industrial biotechnology to help understand the challenges of scaling up and optimization. By working with the free programming language Scientific Python, this book provides easy accessible tools for studying and learning the principles of modern in silico metabolic pathway design.The textbook is written for students with biological or engineering backgrounds, and while it is designed for advanced undergraduates and master students in biotechnology, biomedical engineering, bioinformatics and systems biology, introductory parts render it also useful to beginners, willing to learn the basics of scientific coding and get hands-on on real examples.

Pablo Carbonell is a senior staff scientist at 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, enzyme selection Selenzyme and protein design Promis. Pablo 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.