Toward the Unification of Laboratory Software Platforms

Scientists are all too familiar with the issues of proliferation of software platforms in their work environments.  Most laboratory devices have their own software packages and interfaces, and the lab as a whole typically runs a variety of additional platforms from lab notebooks, to sample management packages to data analysis software.  In a world in which data generation, capture and analysis are the focus, the complexity of managing and integrating multiple disparate platforms can be daunting, and counterproductive.

Lab automation software running on an iMac

Bridging software platforms to form a more unified working experience for scientists is an often overlooked but substantial potential benefit of laboratory automation, if properly executed.  While any laboratory automation integration requires a master scheduling package to oversee the protocol execution, best in class automation software offers significant potential for integration across platforms in the lab, which in turn simplify the execution of research by scientists.

The first layer of unification is the master control of a range of devices used in an automation integration and is largely a baseline requirement across all decent automation scheduling software.  The central scheduling software must have the ability to interact with the devices used in the automation platform in order to send and receive instructions, and data and ultimately to execute the necessary steps required by whatever assay is being run.  Certain lab automation software packages do this more elegantly and efficiently than others, but in the end, you cannot have an integrated platform without this first layer of unification.

The second layer of unification is much less common, and arguably much more powerful.  Whether doing high-throughput screening, compound management, DNA synthesis or any number of other biological processes, labs use software platforms to create demand for work, schedule that work, track inventory, etc.  Work is planned in these lab management software platforms, and then historically has had to be translated by a scientist to an automation platform which would then do the actual protocols associated with whatever work was being ordered.  The process of translating the work from the laboratory management software to the automation scheduling software is both expensive and complicated for the scientist as they do not necessarily think or speak in the language of the automation system.

Today, the most powerful automation software packages do the above translation for you by interfacing through sophisticated APIs with your laboratory management software, where the orders for work are initially placed.  With such a system, a scientist need only enter the work required in the core business management software, in their own natural lab/business language, and the automation scheduling software can translate the requirements into all of the necessary information required to execute the order on the automation platform.  This both reduces time/cost for the scientists and increases accuracy by eliminating the potential for human translation errors.

Similarly, the output of work executed on a laboratory automation platform is typically threefold – experimental results, process parameters, and resulting inventory changes and locations.  In a world of software silos, once work is complete on an automation platform, a scientist is required to log the results of any experiments into a data capture and analysis system, log any parameters of interest in their proper places, and record inventory changes based on what was produced and consumed.  All of these steps are costly, and cumbersome.

By contrast, the same use of API-based communications through the lab automation software used on the upstream side can be used on the downstream side to pass results to data capture and analysis platforms and update changes in inventory in the core lab management platform, seamlessly and with no human intervention.  This further enables the ability to pass a much richer set of process parameters from the automation run itself along with any results, which without the automation would be much too time consuming to capture and record.

Today, sophisticated laboratory professionals are rightly seeing the power automation can have not just in reducing repetitive work for scientists through robotics, but also in bringing together the various software platforms used to run a research operation.  The right automation software has the ability to interact with both upstream and downstream software, as well as control all the requisite devices.  This further enhances the ability of the scientists to focus on science, rather than needing to also do computer science just to manage their automation.


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