Friday, September 02, 2016

Where we stand in biological design automation

Another of the events that I attended in Newcastle was an SRC Workshop on the interaction between electronic design automation and biological design automation. This takes a little unpacking to explain. For the past year or so, I have been part of a road-mapping project organized by the Semiconductor Research Corporation, which is in essence a research foundation run by the United States semiconductor industry. As we are approaching the end what physics allows in improving ordinary computers, this organization is investigating possible new directions for the industry to expand, and one of those directions is towards biology. There are a number of different aspects of this investigation, some of which I have talked about before, and more of which I will talk about in the future, but the one that we were discussing in Newcastle is how the methods used to design electronic systems might help in designing biological systems and vice versa.

Now those of you who have been following me and my work and my writings may know that I am a big advocate of biological design automation. I think that computational tools and models are the only way to really achieve the potential engineering biology. So it might be surprising then, dear reader, for you to hear that one of the big themes that I heard developing at this workshop was that lack of biological design automation software is not the bottleneck in the area and probably will not be for some time.

The issue is this: what I mean when I say "the lack of software is not the issue" is not that we do not need the software. We desperately need good biological design automation software. But if I had two years and 500 programmers, I could not produce that software right now. And that is what the challenges of adapting EDA techniques to the BDA environment is really about, in my opinion. The bottleneck is not a software problem that can be solved by a sufficient application of industry resources and know-how. Instead, as I have argued before, the bottleneck is and currently remains the lack of good devices, characterization data, and models of composition. Until we have a better understanding of what we are looking to automate, we cannot solve the problem by throwing software resources at it.

BDA software, however, does have a critical role to play in solving the problems of biological design and engineering. This is because automation tools expose the requirements of engineering in an especially clear and difficult to evade fashion. Unfortunately, much of the work that has been done on the characterization of biological systems and devices is simply not usable in biological design beyond the most simple and qualitative level. Please understand that this is not a criticism of that work, which is in many cases very good indeed, but a necessary recognition that its purposes have typically been more explanatory and exploratory, and that the knowledge produced from such an investigation is simply not sufficient for the requirements of establishing routine engineering control over the phenomena in question. The degree of precision, curation, and completeness necessary for an excellent scientific publication is simply much lower than what is required for a design automation tool because computer algorithms are unforgivingly stupid, while the people who read scientific papers are very intelligent. That means that if there is any ambiguity or gap in knowledge that is pertinent to the engineering of the system, then an automation tool will almost certainly run afoul of it and force us to confront these issues directly that we otherwise might overlook until they came back to bite us and cost millions of dollars of wasted effort and disappointing failures.

And so, I believe that high-level design automation and the supporting knowledge necessary to enable it need more time to develop and mature before they become an industrially viable business for more than certain niche applications. Right now, the places where automation tools are likely to be of high value are at a low-level, such as we se in protein design, CRISPR nuclease design, codon optimization, etc. Likewise, there is a great deal of potential opportunity in the automation of laboratory equipment and I expect a high potential for disruptive innovation in this area given the high premiums and extreme vertical integration common in laboratory suppliers at present. Any business that can begin shifting biological engineering instruments from from the current "car sales" model towards something more like office equipment services might be able to radically affect the area. Similarly, I foresee great disruptive potential for anyone who can bring microfluidics from specialty investigation to a set of compact and user-friendly tools.

In the meantime, more basic research funding is still needed to enable the development of characterization and devices that will be able to support the more complex biological engineering targets of the future. This is particularly true since I do not see this being profitable area for large companies to invest in for the near future, given that there are still so many pieces of low-hanging bioengineering fruit that can be collected for significant financial return. 

Thus, at present, I see biological design automation as another good example of a technological area where government investment is likely to spur the foundation of multi-billion dollar industries, if only we can start it going.
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