Computational Synthetic Biology Enabled through JAX: A Showcase.

Mathematical modeling is indispensable in synthetic biology but remains underutilized. Tackling problems, from optimizing gene networks to simulating intracellular dynamics, can be facilitated by the ever-growing body of modeling approaches, be they mechanistic, stochastic, data-driven, or AI-enabled. Thanks to progress in the AI community, robust frameworks have emerged to enable researchers to access complex computational hardware and compilation.

Synthetic biology landscape in the UK.

The UK is home to a vibrant and diverse synthetic biology community. Many of its successes in research innovation and technological commercialisation can be attributed to a strong base of dedicated academics, investors, industrial leadership, and policymakers. Here, we give an overview of the organisations making up the network that have been key to these successes and the roles that they play within the different levels of the community.

Ten future challenges for synthetic biology.

After 2 decades of growth and success, synthetic biology has now become a mature field that is driving significant innovation in the bioeconomy and pushing the boundaries of the biomedical sciences and biotechnology. So what comes next? In this article, 10 technological advances are discussed that are expected and hoped to come from the next generation of research and investment in synthetic biology; from ambitious projects to make synthetic life, cell simulators and custom genomes, through to new methods of engineering biology that use automation, deep learning and control of evolution. The non-exhaustive list is meant to inspire those joining the field and looks forward to how synthetic biology may evolve over the coming decades.