commercial robotic systems first emerged
in the 1980s. The initial purpose of these
systems was to replace manual tasks
such as liquid handling/pipetting and
plate management. Further technological
advances meant that robots could connect
to corporate inventories and sample
management and dispensing systems,
meaning that scientists could design
experiments and then effectively walk away
and let the automation do the work.
Many of the new instruments coming to
market are Io T enabled, and we are seeing
a rise in vendors adopting these concepts
to help deliver better services, such as
automated preventative maintenance
and reagents delivered just in time (JIT).
By making it possible to target the right
information at the right audience, Io T
enabled instruments are not only easier to
use, but can also help ensure data integrity
and give early warning indicators of
Many of the robotic systems designed
for lab use were at first difficult to operate
and often prohibitively expensive for all
but the most generous research budgets.
This is changing with the introduction
of more affordable technology such as
Opentrons, and vendors are working to
make robotic systems more user friendly.
Programming a robot used to require
specialist knowledge and was typically the
responsibility of one or more computer-savvy scientists or IT support staff. Now,
almost anyone in the lab can create robust
and reproducible workflows for automated
systems to execute.
What comes next?
Once a fully connected and automated
environment has been established, the
focus can then shift to the next phase
of development—a self-monitoring
and regulating closed system that can
make decisions on what task should be
undertaken next based on the current
status of an experiment.
As analytical instruments become ever
more sophisticated, and the wealth of
data produced at each experimental stage
increases, informatics solutions need to
keep pace so that the data can be turned
into useful information. Monitoring
analytical instruments in real time,
gathering data and performing automated
analysis and feedback loops all must be
considered at this stage, and models can
even be developed and trained to allow
systems to control themselves based on
real-time patterns that they are seeing.
Companies such as Lab Genius and
Ginkgo Bioworks are already applying
machine learning algorithms to protein
engineering and synthetic biology with
great potential for both therapeutic and
consumer product development.
The foundations of this technical
revolution are Io T and robotics—but
their routine use in laboratories and
manufacturing contexts will require
considerable optimization and rigorous
testing. Biological systems are notoriously
complex and unpredictable and processes
that work well at small scale can be
difficult, if not impossible, to scale up to
production level, as companies like Amyris
have discovered to their cost.
The final part of the system to consider for
automation is data analysis and reporting,
both of which are critical parts of the
decision-making process for scientists.
Typically, the process for data aggregation,
analysis and reporting—despite the
automation of the instruments—is still
manual for many organizations. Great strides
are being made to accelerate analysis and
reporting automation, with product concepts
of alerting, automated decision trees based
on business rules and machine learning, and
research and development ‘project progress’
reporting all starting to emerge.
For analysis aspects, it’s important to make
sure that all the different system parts are
integrated at the data level, which can then
allow the automation and analysis to occur.
The need to both increase efficiency
and reduce time to bring new products to
market is forcing companies to reconsider
the ways they interact with and leverage
technology. By considering automation
in this holistic manner we can see great
opportunities for new technology and a
streamlining of research and development.
The possibilities of making new products
cheaper, faster, and with higher quality
than ever before is an exciting prospect to
look forward to.
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