White Paper: The 21st Century Chemical / Biological Lab.
Electronic and computer engineering professionals take for granted that circuits can be designed, built, tested, and improved in a very cheap and efficient manner. Today, the electrical engineer or computer scientist can write a script in a domain specific language, use a compiler to create the circuit, use layout tools to generate the masks, simulate it, fabricate it, and characterize it all without picking up a soldering iron. This was not always the case. The phenomenal tool-stack that permits these high-throughput experiments is fundamental to the remarkable improvements of the electronics industry: from 50-pound AM tube-radios to iPhones in less than 100 years!
Many have observed that chemical (i.e. nanotech) and biological engineering are to the 21st century what electronics was to the 20th. That said, chem/bio labs – be they in academia or industry – are still in their “soldering iron” epoch. Walk into any lab and one will see every experiment conducted by hand, transferring micro-liter volumes of fluid in and out of thousands of small ad-hoc containers using pipettes. This sight is analogous to what one would have seen in electronics labs in the 1930s – engineers sitting at benches with soldering iron in hand. For the 21st century promise of chem/nano/bio engineering to manifest
itself, the automation that made large-scale electronics possible must similarly occur in chem/bio labs.
The optimization of basic lab techniques is critical to every related larger-scale goal be it curing cancer or developing bio-fuels. All such application-specific research depends on experiments and therefore reducing the price and duration of such experiments by large factors will not only improve efficiency but also make possible work that was not previously. While such core tool paths are not necessarily “sexy”, they are critical. Furthermore, a grand vision of chem/bio automation is one that no single commercial company can tackle as the vision for such requires both a very long time commitment and a very wide view of technology. It is uniquely suited to the academic environment as it both depends upon and affords cross-disciplinary research towards a common, if loosely
defined, goal.
Let me elucidate this vision with a science-fiction narrative:
Mary has a theory about the effect of a certain nucleic acid on a cancer cell line. Her latest experiment involves transforming a previously created cell line by adding newly purchased reagents, an experiment that involves numerous controlled mixing steps and several purifications. In the old-days, she would have begun her experiment by pulling-out a pipette, obtaining reagents out of the freezer, off of her bench, and from her friend's lab and then performed her experiment in an ad hoc series of pipette operations. But today, all that is irrelevant; today, she never leaves her computer.
She begins the experiment by writing a protocol in a chemical programming language. Like high-level languages used by electrical and software engineers for decades, this language has variables and routines that allow her to easily and systemically describe the set of chemical transformations (i.e. “chemical algorithms”) that will transpire during the experiment. Many of the subroutines of this experiment are well established protocols such as PCR or antibody
separation and for those Mary need not rewrite the code but merely link in the subroutines for these procedures just as a software engineer would. When Mary is finished writing her script, she compiles it. The compiler generates a set of fluidic gates that are then laid-out using algorithms borrowed from integrated circuit
design. Before realizing the chip, she runs a simulator and validates the design before any reagents are wasted – just as her friends in EE would do before they sent their designs to “tape out.” Because she can print the chip on a local printer for pennies, she is able to print many identical copies for replicate experiments. Furthermore, because the design is entirely in a script, it can be reproduced next week, next year, or by someone in another lab. The detailed script means that Mary’s successors won’t have to interpret a 10 page hand-waving explanation of her protocol translated from her messy lab notes in the supplementary methods section of the paper she publishes – her script *is* the experimental protocol. Indeed, this abstraction means that, unlike in the past, her experiments can be copyrighted or published under an open source license just as code from software or chip design can be.
Designing and printing the chip is only the first step. Tiny quantities of specific fluids need to be moved into and out of this chip – the “I/O” problem. But Mary’s lab, like any, both requires and generates thousands of isolated chemical and biological reagents each of which has to be stored separately in a controlled environment and must be manipulated without risking cross-contamination. In the old days, Mary would have used hundreds of costly sterilized pipette
tips as she laboriously transfered tiny quantities of fluid from container to container. Each tip would be wastefully disposed of despite the fact that only a tiny portion of it was actually contaminated – such was the cost when everything had to be large enough to be manipulated by hand. In the old days, each of the target containers – from large flasks to tiny plastic vials – would have had to be hand-labeled resulting in benches piled with tiny cryptic scribbled notes with all of the confusion and inefficiency that results from such clutter. Fortunately for Mary, today all of the stored fluids for her entire lab are maintained in a single fluidic database; she never touches any of them. In this fluidic database, a robotic pipette machine addresses thousands of individual fluids. These fluids are stored inside of tubes that are spooled off of a single supply and cut to length and end-welded by the machine as needed. Essentially, this fluidic database has merged the concepts of “container” and “pipette” – it simply partitions out a perfectly sized container on-demand and therefore the consumables are cheaper and less wasteful. Also, the storage of these tube-containers is extremely compact in comparison to the endless bottles (mostly filled with air) that one would have seen in the old days. The fluid-filled tubes could be simply wrapped around temperature-controlled spindles and, just like an electronic database or disk drive, the system can optimize itself by “defragmenting” its storage spindles ensuring there’s always efficient usage of the space. Furthermore, because the fluidic
database knows the manifest of its contents, all reagent accounting can be automated and optimized.
Mary has her experiment running. But, moving all these fluids around is just a means to an end. Ultimately she needs to collect data about the performance of her new reagent on the cancer line in question. In the old days, she would have run a gel, used a florescent microscope, or any number of other visualization techniques to quantify her results – any of these measurements would have required a large and expensive machine. But today, most of these measurements are either printed directly on the same chip as the fluidics using printable chemical / electronic sensors or those that can’t be printed are interfaced to a standardized re-usable sensor array. The development of those standards was crucial to the low capital cost of her equipment. Before far-sighted university engineering departments set those standards, each diagnostic had its own proprietary interface and therefore the industry was dominated by an oligopoly of several companies. But now, the standards have promoted competition and thus the price and capabilities of all the diagnostics has improved.
As Mary’s chemical program executes on her newly minted chip, she gets fluorescent read-outs on one channel and antibody detection on another – all such diagnostic were written into her experimental program in the same way that a “debug” or “trace” statement is placed into a software program. After her experiment runs, the raw sensor data is uploaded to the same terminal where she wrote the program and she begins her analysis without getting out of her chair.
After the experiment, the disposable chip and the temporary plumbing that connected to it are all safely incinerated to avoid any external contamination. In the old days, such safety protocols would have had to be known by every lab member and this would have required a time-consuming certification process. But today, all of these safety requirements are enforced by the equipment itself and therefore there’s much less risk of human mistake. Furthermore, because of the
enhanced safety and lower volumes, some procedures that were once classified as bio-safety level 3 are now BSL 2 and some that were 2 are now 1, meaning that more labs are available to work on important problems.
Mary’s entire experiment from design to data-acquisition took her under 1 hour – comparable to a week by old manual techniques. Thanks to all of this automation, Mary has evaluated her experiment and moved on to her next great discovery much faster than would have been possible before. Moreover, because so little fluid was used in these experiments her reagents last longer and therefore the cost has also fallen. Mary can contemplate larger-scale experiments than anybody dreamed of just a decade ago. Mary also makes many fewer costly mistakes because of the rigor imposed by writing and validating the entire experimental script instead of relying on ad hoc procedures. Finally, the capital cost of the equipment itself has fallen due to standardization, competition, and economies of scale. The combined result of these effects is to make the acquisition of chemical and biological knowledge orders of magnitude faster than was possible just decades ago.
