Synthetic biology has the potential to make significant impacts in almost every sector: food, medicine, agriculture, climate, energy, and materials. The global synthetic biology market is currently estimated at around $4 billion and has been forecast to grow to more than $20 billion by 2025, according to various market reports. However, scientists normally have to spend years developing a meticulous understanding of each part of a cell and what it does in order to manipulate it, which has limited the development of synthetic biology for years. Synthetic biologists urgently need a more efficient way.
The good news comes from the Department of Energy's Lawrence Berkeley National Laboratory (Berkeley Lab) , where the scientists have adapted machine learning algorithms to the needs of synthetic biology to guide development systematically, as reported this week. Based on this innovation, scientists are now able to predict how changes in a cell's DNA or biochemistry will affect its behavior, then make recommendations for the next engineering cycle along with probabilistic predictions for attaining the desired goal. According to the published paper in the journal Nature Communications, machine learning allows computers to make predictions after "learning" from substantial amounts of available "training" data. A bioengineer era for synthetic biology is coming now.
Radivojević, Tijana, et al. "A machine learning Automated Recommendation Tool for synthetic biology." Nature Communications 11.1 (2020): 1-14.