Is Cloud Lab Execution Acceptable for Young Students?
Scientific experimentation is typically portrayed in media as a very hands-on affair: an astronomer staring through a telescope, a biologist with a microscope, a chemist with glassware. A great deal of modern science is very different - nobody stares through the Hubble Space Telescope.
How important is that hands-on component? I definitely remember the magic of watching DNA precipitate out, or of my purified chloroplasts demonstrating redox activity. To play devil's advocate to my own ambitions: if the actual lab work is all done remotely, is it really different than just working with simulated data?
The advantage of the cloud lab approach is that students can run experiments that involve equipment unavailable in a typical school. Sophisticated workflows that might not be within the skillset of the local teacher become available.
But are science fairs willing to take experiments in which the student performed design and analysis work, but all of the physical execution was outsourced? How great a cultural barrier is there to this? I certainly know that three and a half decades ago the idea of a "dry" Ph.D. in biology was very radical; are purely bioinformatics projects acceptable in science competitions? Acceptance of purely dry projects might be an indicator of the probability of acceptance of remotely executed wet projects.
What Cost Is Appropriate?
Cost is certainly a key question. One argument in favor of cloud labs is that they enable access to instruments which are rare even in colleges. For enzymatic reactions, one readout we offer is Echo-MS, a mass spectrometry method. Even if we forget the Echo component, how many undergraduates let alone grade school students ever have access to a high resolution mass spectrometer?
But the flip side is that if the experiment costs are too expensive, then most schools and individuals will be priced out. The idealist in me wants to make our services available to any student, anywhere. The realist knows we can't lose money on every sale and make it up in volume. Even with our pricing philosophy of consumable costs plus a small "instrument rental" markup, interesting experiments quickly become many hundreds of dollars. DNA synthesis remains a major component and is a cost component that has not seen radical improvement in a long time. A recent preprint from Frances Arnold and collaborators reported slightly better than 50% success on one enzyme design target, so a student or student group might submit as few as 20 designs and have a very rich dataset - but that might still be a few thousand dollars once replicates are factored in.
One comparator is iGEM, the international synthetic biology competition. Granted, iGEM has been criticized for pricing out many students or that groups must engage in intensive fund raising which distracts from the actual biotech-ing. A clear counterargument exists: real-world science involves distracting, intensive fundraising so this is a very authentic, if perhaps discouraging, presentation of the practice of science.
Anyways, according to Gemini iGEM teams need $7K-$12K even if they don't travel to a jamboree, $10K-$25K if they plan to travel, and $25K-$70K for very ambitious teams that engage CROs or build many constructs. Yikes! This does suggest there is a market at high school and college level which will bear significant expenditures for advanced science projects.
What Scope Is Appropriate?
Most lab exercises I had as part of courses, except some upper level undergraduate courses, were of the fixed design type. There was a very specific experimental design to be followed and specific analyses of the data pre-specified. For example, execute a dihybrid cross of Drosophila and score the progeny by phenotype. Perform a simple organic synthesis and compute your yield. A major advantage of such designs is they are highly reliable - failed experiments are not viewed as instructive.
There's another class of experiments I would call templated, in which there is a well-defined experimental framework but the results can be truly novel. HHMI developed a curriculum around students isolating bacteriophage and many of these groups have sequenced their isolates and deposited those in GenBank. It's very easy to imagine experiments involving sequencing being productive in this space - many organisms to sequence, the intellectual process of interpreting the data rich in opportunity, and the potential for interesting discoveries.
Truly novel experiment designs and campaigns are often found in independent projects. Someone who has signed on for independent research is seen as having also signed on for some level of uncertainty as to whether anything will work.
Within the cloud lab framework, we can potentially support all of these. Fixed experimental designs can easily be structured as catalog items. Some templated experiment designs might be as well - we could offer specific assays ready-to-go for budding protein engineers, who would likely be submitting designs never before tested anywhere. Fully novel assays are possible, but entail some risk and delay in configuring and there would be a cost for that.
Where Does It Fit?
I've spent a little time using AI to probe the AP Biology standards, and there is only a very limited coverage of topics that would seem to intersect with the sort of services we offer. For example, the concept of thermostability of proteins is introduced, and we offer a thermostability assay. The concept of protein function being affected by pH and other chemical environment seems to be presented, and our enzyme assays could explore that. The idea of competitive vs. non-competitive enzyme inhibitors is also discussed; yet another place we could have assays.
But I don't see anything on designing proteins. So could this mesh with a curriculum? There are a few protein design tools out there that claim to target thermostability or pH dependence.
So perhaps protein engineering is not a great fit for the main AP Biology curriculum, but would be suitable for special projects?
Timing & Pace
Most of the experiments I ever remember running in any class were designed to fit into a single laboratory session. Maybe a few university microbiology exercises were structured so we set them up one day and counted colonies the next session. Independent projects definitely stretched over many weeks, so they are a different category.
For a variety of reasons, our protein engineering workflows have a three week turnaround time. I'd love to shorten this, but just getting the genes made imposes a significant timeline penalty.
If a course or biotech club were to engage in a multi-stage protein engineering campaign using our cloud lab, it would necessarily stretch over multiple months. Can such a pacing be reconciled against typical academic schedules? Or that the results of the experiment would be delivered many weeks after the course had moved on to entirely different topic areas?
Feedback?
If you have feedback on the intersection of cloud labs and pedagogy, I'd love to hear from you - particularly if you are an educator or a parent with children in this age range. Yes, I'm interested in selling - but I'm also just interested in hearing why this does or does not resonate with you or what key aspects I've failed to cover.
I can be reached by many means. My gmail address is keith.e.robison . Comments on this blog are moderated; if you leave one that looks more like a personal message (such as by including an email address) I won't pass it through.
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