Automated Biology is Taking Off

March 10, 2016

Compared to modern software development, biology development is really, really slow. It can take an excruciatingly long time to develop an improvement in the incredibly complex system of DNA and proteins in a cell. Add more time to test for the target phenotypes (characteristics). If I have a coding mistake in my software product, developers, powered by pizza and mountain dew, can work through the night and fix it, running iteration after iteration to see the results of each change. If I have a coding mistake in a cell’s DNA, even in something as simple as bacteria, it may take a week to reset the code and run the next iteration to make sure that it is fixed. The bacteria will also enjoy the pizza – but won’t work any faster. It is even harder for plant and animal cells where you may have to wait months or years to see the effect of changes on mature organisms.

 

Automated Biology isn’t new, but it feels like it is reaching a tipping point where we are witnessing measurably faster biology development as tools for genetic manipulation and analysis have hit a tipping point where new entrants can move faster and cheaper than the incumbents. Machine Learning and big data predictive analytics tools are maturing, driven by the economics of the Internet, and will find increasing traction in computational biology when paired with the explosion of readily available data. CRISPR technology allows precise gene editing in a much more rapid and predictable way than was previously available. Faster, cheaper and more sensitive gene sequencing is becoming a commodity. In agriculture, drones can fly test crops and capture massive amounts of experimental data. All of these tools will enable the rise of new biological powerhouses just like the Internet and cloud computing have done for traditional IT businesses.

 

VC interest in Automated Biology is already starting to heat up, although it is still early days. A few examples:

Both Zymergen and Ginko Bioworks have raised $40 million dollar rounds in the last year to apply new automation ideas to the synthetic biology of microorganisms. Microorganisms are the logical start, being easier to manipulate and test than plant or animal cells.

Human Longevity using big data to address human aging (appearing to also target microorganism/microbiome studies initially as well).

Google is using experimental data across multiple diseases and multiple sources to identify effective chemical compounds for a particular disease.

Benson Hill is using cross species phenotype-genotype relationships and state of the art bioinformatics (for IT people thats big data, machine learning, etc) to rapidly drive decisions around rapid plant optimization (with plants already in the field showing 20% increase in yield just by tweaking photosynthesis pathways).

A16Z just announced a fund for the intersection of biology and computation with three themes: healthcare IT for therapeutic benefit, outsourced bio (like Ginkoworks), and computation in medicine (computer reading of radiology images for example).

 

For biology, it feels like the early 2000’s for information technology; you can see the possibilities when startups can understand and optimize biological systems in months and years instead of decades. Costs are being driven down by commoditization of the tools (hardware) and the utilization of tools, some borrowed from the IT space, that take advantage of the cloud, new data and development paradigms.

 

To me, the exciting question is how can we use the new tools, and outsourced bio resources, to better understand incredibly complex biological systems and move faster to develop novel and improved organisms and therapeutic entities. I think that this has the potential to create new companies, perhaps with new business models, that challenge the status quo in how discovery and development for the benefit of microbiology, plant and animal genomics and human health.

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