2017 in Microbes and Machines: How is Big Data Redefining Biotechnology?

While people do believe the biggest impact of machine learning lies in developments in the Silicon Valley, we may all be wrong.

According to Matt Asay and his piece for Tech Republic, the field of healthcare -- particularly biotechnology -- is being affected by machine learning in big ways. Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals.

They are developing and exploring tools, both software and hardware, and applying them to biology.

They are experimenting with the ability to design and optimize microbes, and their results are becoming faster and cheaper than before. According to Tech Republic, their efforts are being fueled by cutting-edge advances in both data science and biotech, and the rapidly dropping cost of reading, writing and editing DNA.

Meaning the latest advances in software, big data, machine learning, biotech and chemistry are combining to start a new "industrial revolution." This may mean the field of biotechnology - and medicine in general - will meet a ton of new changes in their respective fields. This may also herald the arrival of more technologically-advanced systems that may finally aid professionals in providing better healthcare to their constituents.

Meanwhile, according to Tech Republic, Lygos's flagship product is malonic acid that is derived from petroleum that is now used in industries such as flavor and fragrance, electronic manufacturing and coatings.

How they got there is also interesting. For instance, microbes have evolved over millions of years to become hyper-efficient "factories." Lygos is starting to unlock the ability to control and guide evolution to reprogram a microbe to produce its products. A microbe can do a computation every time it divides and grows itself, which occurs once every 20 minutes.

Lygos has millions of them growing on a single vat, and using technology that harnesses this "evolutionary" aspect, they can have more powerful machine-learning platforms in nature.