Scientists have been dealing with the issue of laboratory automation for decades, and during that time the meaning of those words has expanded from the basics of connecting an instrument to a computer, to the possibility of a fully integrated informatics infrastructure beginning with sample preparation and continuing on to the laboratory information management system (LIMS), electronic laboratory notebook (ELN), and beyond. Throughout this evolution there has been one underlying concern: how do we go about doing this?
The answer to that question has changed from a focus on hardware and programming, to today’s need for a lab-wide informatics strategy. We’ve moved from the bits and bytes of assembly language programming to managing terabytes of files and data structures.
The high-end of the problem—the large informatics database systems—has received significant industry-wide attention in the last decade. The stuff on the lab bench, while the target of a lot of individual products, has been less organized and more experimental. Failed or incompletely met promises have to yield to planned successes. How we do it needs to change. This document is about the considerations required when making that change. The haphazard “let’s try this” method has to give way to more engineered solutions and a realistic appraisal of the human issues, as well as the underlying technology management and planning.
Why is this important? Whether you are conducting intense laboratory experiments to produce data and information or making chocolate chip cookies in the kitchen, three things remain important: productivity, the quality of the products, and the cost of running the operation. In any case, if the productivity isn’t high enough, you won’t be able to justify your work; if the quality isn’t there, no one will want what you produce. Conducting laboratory work and making cookies have a lot in common. Your laboratories exist to answer questions. What happens if I do this? What is the purity of this material? What is the structure of this compound? The field of laboratories asking these questions is extensive, basically covering the entire array of lab bench and scientific work, including chemistry, life sciences, physics, and electronics labs. The more efficiently we answer those questions, the more likely it will be that these labs will continue operating and, that you’ll achieve the goals your organization has set. At some point, it comes down to performance against goals and the return on the investment organizations make in lab operations.
This article looks at conditions that need to be met before you embark on the automation of a laboratory process. It comes down to a key factor: is it worth it? What will you gain by doing it, how much effort will it take, and will it significantly improve lab operations?
The material can be access through this link to the LIMSwiki.
Laboratory Systems Engineering 