Synthetic Biology: A Primer
by Paul S. Freemont, Richard I. Kitney · Paul S. Freemont
An Imperial College Press overview aimed at final-year undergraduates and researchers, covering the molecular biology and engineering foundations of the field and contrasting bottom-up with top-down design approaches. Readers finish able to follow current synthetic biology literature.
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More resources on Synthetic Biology
synbiobeta.com
SynBioBeta is a media and events platform for the synthetic biology community, offering news and analysis, startup profiles, and information on conferences, funding, and industry resources.
syntheticbiology.org
Syntheticbiology.org is a central resource hub for learning about synthetic biology, offering introductory explanations, educational materials, and news and policy updates about the field.
igem.org
iGEM.org is the official site for the International Genetically Engineered Machine (iGEM) competition in synthetic biology. It provides project showcases, the Registry of Standard Biological Parts, tutorials and educational resources, team information, and event details like the annual Jamboree.
OpenWetWare
Community wiki where academic biology labs publish protocols, open notebooks, course materials and group pages. Useful for finding a working bench protocol and seeing how synthetic biology groups document their day-to-day methods.
Introduction to Biological Engineering Design (MIT 20.020)
Project-based introduction to engineering synthetic biological systems: prokaryotic and eukaryotic control, DNA synthesis, standard parts and abstraction, plus biosafety, security, intellectual property and ethics. Provides lecture slides, short tutorial videos and example student projects for designing biology-based solutions to real problems.
Foundations of Computational and Systems Biology (MIT 7.91J)
Computational biology fundamentals: nucleic acid and protein sequence alignment, motif finding, structural modeling and prediction, and network models of biological systems. Includes 22 lecture videos, slides, programming assignments and projects, preparing learners to apply algorithms to genomic and protein data.