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The Rapidly Evolving Automated Lab

Thu, 03/11/2010 - 7:50am

March 2010

Tim Studt, Editor-in-Chief
Tim Studt
Editor in Chief



Implementing lab automation, robotics, and automated analyses are the easy parts. Integrating these technologies with the next technology level—artificial intelligence, autonomous operation, and android-like interfaces—will take a little more effort. But it will come.

There are various predictions as to when these technologies will become practical implementations and what level they will take. Processing power—a critical element in this evolution—is one of the easier technology trends to predict. A number of futurists expect that computer-based intelligence will rival human intelligence within 30 years, which is about 10 years earlier than what they predicted in 2000—such is the accelerating pace of technology.

Other factors relating to this evolution include Internet bandwidth, price-performance ratios of wireless data devices, magnetic data storage, and random access memories, which are all following similar exponential trend curves.
Materials development trends are another critical element in this evolution, with nanoscale biological systems development currently accelerating in place of non-biological systems. There are some biological system developments taking place in the microprocessing area, but the level and extent is so primitive as to be indeterminate as to its impact on the overall development of analytical systems.

These technology progressions, while individually following exponential trend lines, are mostly evolutionary, not revolutionary, eliminating step function transitions. The recent Pittcon Conference in Orlando, Fla., and LabAutomation Conference in Palm Springs, Calif., exemplified this evolution with new systems touting higher throughput, smaller and portable sizes, and integrated cross-functionalities. Some analytical system developers promoted the revolutionary aspect of their development and, indeed, they may be isolated “technology islands” in the overall lab evolution.

One of the specific items noted in many analytical developments is the relationship of “easier-to-use” aspects, which relates to the on-going intelligence being built into analytical instruments. Many of these systems are also starting to have wireless interfaces and the ability to download both raw and processed data to a central collection point automatically.
A number of new systems are slowly being developed and integrated into the overall analytical laboratory schema. These include systems for improving the speed and cost of sequencing DNA data, collecting material information on the nanoscale level, and the rudimentary collection of data on systems biology. These developments are typical evolutionary events that we can continue to expect to see in future technology announcements, both this year and in the future.

In these respects, analytical instruments are both the drivers and the recipients in the evolution of technology development. The net effect on the lab and its staff is a continuing increase in data output and its increasing complexity. ICT (information, computer, telecommunications) systems are similarly improving in performance to handle increasing throughput and bandwidth.
To most researchers, these observations should come as no surprise, but they should be recognized in their overall context, outcomes, and inter-relationships. The lab is becoming more complex and the data more prolific, and the drivers are inherent to the lab itself—they are self-perpetuating and will continue to drive competition between labs.
While “data drives more data” is a relatively easy road to follow for researchers, they must always be cognizant of applying those new technologies (and collected data) to the development of their organization’s products. It will also be the researchers’ duty to implement these evolving analytical systems into their research
environments as quickly and as cost-effectively as possible.

Obviously with these scenarios, researchers’ jobs will continue to grow in complexity, responsibility, and knowledge requirements. Researchers are only human. They can effectively absorb only so much information and will increasingly have to rely on the advanced tools becoming available to assist them in their jobs—which puts us right back to where we started this discussion.

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