Overview
Biotechnology partners with biology to create products and services, such as engineering skin microbes to fight cancer or brewing medicines from yeast. This industry, already 5 percent of US GDP, is poised for significant growth. Synthetic biology, a subset of biotechnology focusing on enhancing living systems, relies on DNA sequencing and synthesis. DNA sequencers are machines that read or decode specific DNA molecules, while synthesizers write user-specified sequences of DNA. Rapid progress in these technologies is driving innovation and expanding biotechnology’s potential applications.
Biology as a manufacturing process is distributed—leaves do not come from a central production facility but rather grow on trees everywhere. However, commercial biotechnology has become centralized and capital intensive. This contrast suggests a potential paradigm shift toward a more distributed approach in biotechnology, aligning it more closely with nature’s decentralized production model.
Synthetic biology merges biology, engineering, and computer science to modify and create living systems, developing novel biological functions served by amino acids, proteins, and cells not found in nature. This field creates reusable biological “parts,” streamlining design processes and reducing the need to start from scratch, thus advancing biotechnology’s capabilities and efficiency.
Synthetic biology has applications in medicine, agriculture, manufacturing, and sustainability. DNA and RNA synthesis underlies all mRNA vaccines, including those for COVID-19. Synthetic biology can also cultivate drought-resistant crops and enable the programming of cells to manufacture medicines or fuel on an agile, distributed basis.
Key Developments
Distributed Biomanufacturing This offers unprecedented production flexibility in both location and timing. Fermentation production sites can be established anywhere with access to sugar and electricity. The approach enables swift responses to sudden demands like disease outbreaks requiring specific medications. Such adaptability revolutionizes manufacturing, making it more efficient and responsive to urgent needs.
Biological Computing Computing has become central to modern biology. For example, artificial intelligence (AI) methods accelerate research by enabling the computational exploration of protein behavior, significantly reducing (though not eliminating) the need for expensive laboratory experiments. Large language models (LLMs), a form of AI, are being trained on natural DNA, RNA, and protein sequences. Called BioLLMs, they can generate new biologically significant sequences that are helpful points of departure for designing useful proteins. Models and software can be used to generate promising drug designs that may be able to speed up drug discovery from months or years to weeks.
