ScaaS: Science as a service (a research revolution)


Greetings, my fellow colleagues of tedium. “Give me your tired, your poor, your huddled masses yearning to breathe free …” Welcome to the new world of research: ScaaS.

It’s time to bring us out of the dark ages. No more old PCs running Windows 2000. No more equipment still using floppy drives. No more carpal tunnel syndrome from repetitive tasks a robot could (and SHOULD) do. It’s not just about our quality of life – it’s about changing the devastating trend of costly R&D.

I’m happy to say that the future is here – or, at least, we’re on the brink of it.  I’m calling it science as a service. It’s like software as a service, but for the research industry. Even if you aren’t familiar with the term SaaS, you’ve probably used an implementation of it. SaaS-based products host their software and your associated data on the cloud, and you interact with it via a simple web browser interface.

Now consider using that model for your favorite scientific experiment:

Instead of purchasing the hardware ($20k – $100k, or more), dealing with the software, maintaining the instrument, and doing the experiment by hand, you pop open your favorite web browser. You select the experiment and direct every detail by specifying a series of options (cell type, temperature, internal standard, instrument settings, etc. – if it can be altered, there should be an option for it). Maybe you also select parameters for how your data should be analyzed, how many times it should be repeated, or you select a desired completion date. Click, click, click, and your little experiment is on its way.* And you are on to bigger and better things.

Sounds great, right? Other people think so too. There are already a few names in this field. You should check out this great talk about how Emerald Therapeutics’ Symbolic Laboratory creates a construct for lean research (and how “lean research” could no longer be an oxymoron). TechCrunch blogged recently about Transcriptic and Benchling, and companies like Synthego, Gen9, and Gingko Bioworks are making headway too. 

So what’s keeping this from being immediately adopted in every lab in the country?

First, it’s probably because most experiments are not done in an automated fashion. If you go through a web interface to order an experiment, but then a human in a CRO does it for you, this doesn’t help much. It may save you some time, but it’s not a scalable or cost-efficient model. But just because these experiments aren’t normally done in an automated way doesn’t mean they can’t be done in an automated way. Most people still prefer grad students as a cheap form of labor (students making just over minimum wage, in fact), even though an automated instrument is more cost-effective in the long run.

But there’s another problem. It’s a mindset. Let’s face it: We scientists can be greedy. We just don’t want an experiment to be out of our hands. There is a biased attitude of trust that if you do it yourself, it’s “done right”. But the do-it-yourself model hasn’t worked out so well for us in terms of reproducibility. I’m not saying that you shouldn’t be concerned about handing over your experiments, but if a robot is doing the work you can take solace in that it will do what it’s programmed to do.** Robots don’t make complex errors like forgetting one element of a buffer recipe because it hasn’t had its morning coffee. The errors are standardized and if a major problem occurs, the robot stops entirely.

Lastly, scientists need to be more demanding about data. We need to make a priority of gathering it, storing it, and sharing it. In order to trust an experiment to a ScaaS system, the user needs to get back all the data (raw data, meta data, instrument files) they can get their hands on. Not just for the experiment at hand, but for the controls too. And not just the control run before their experiment – every control run. Ever. That should all be open-access. That way a user could investigate global changes in the behavior of the instrument, not just see an isolated period in time when their experiment was run. I would even suggest providing video records of the experiment in progress. (If you can afford a webcam to watch your kitty sleep all day, then a ScaaS center can afford them to watch their robots.) 

In conclusion – the solution is out there. But to adopt this system, we have to change the way we do science. We need to start integrating automation on all levels, incorporating computer science into the scientific way of life, and most of all, becoming gluttons for data.

* There is, of course, a potential problem with this. I’m not suggesting that only one powerhouse should dominate the market for a particular experiment. It would disastrous to find out later they’d done something wrong – remember the fiasco when we found out the major breast cancer cell line MDA-MB-435 was actually a melanoma cell line? I don’t think we need to get ourselves in that potential situation. I think competition within the private sector can do what it does best – have companies compete until a few “gold standard” options exist that are well-validated and trusted.

** The old adage “garbage in, garbage out” applies here. To properly program a robot, you have to have a unique blend of science and computer science savvy. I’m privileged to know some of these gurus, and in the right hands, this works.