LII:Directions in Laboratory Systems: One Person's Perspective

Title: Directions in Laboratory Systems: One Person's Perspective

Author for citation: Joe Liscouski, with editorial modifications by Shawn Douglas

License for content: Creative Commons Attribution-ShareAlike 4.0 International

Publication date: November 2021

Introduction

The purpose of this work is to provide one person's perspective on planning for the use of computer systems in the laboratory, and with it a means of developing a direction for the future. Rather than concentrating on “science first, support systems second,” it reverses that order, recommending the construction of a solid support structure before populating the lab with systems and processes that produce knowledge, information, and data (K/I/D).

Intended audience

This material is intended for those working in laboratories of all types. The biggest benefit will come to those working in startup labs since they have a clean slate to work with, as well as those freshly entering into scientific work as it will help them understand the roles of various systems. Those working in existing labs will also benefit by seeing a different perspective than they may be used to, giving them an alternative path for evaluating their current structure and how they might adjust it to improve operations.

However, all labs in a given industry can benefit from this guide since one of its key points is the development of industry-wide guidelines to solving technology management and planning issues, improving personnel development, and more effectively addressing common projects in automation, instrument communications, and vendor relationships (resulting in lower costs and higher success rates). This would also provide a basis for evaluating new technologies (reducing risks to early adopters) and fostering product development with the necessary product requirements in a particular industry.

About the content

This material follows in the footsteps of more than 15 years of writing and presentation on the topic. That writing and presentation—compiled here—includes:

• Are You a Laboratory Automation Engineer? (2006)
• Elements of Laboratory Technology Management (2014)
• A Guide for Management: Successfully Applying Laboratory Systems to Your Organization's Work (2018)
• Laboratory Technology Management & Planning (2019)
• Notes on Instrument Data Systems (2020)
• Laboratory Technology Planning and Management: The Practice of Laboratory Systems Engineering (2020)
• Considerations in the Automation of Laboratory Procedures (2021)
• The Application of Informatics to Scientific Work: Laboratory Informatics for Newbies (2021)

While that material covers some of the “where do we go from here” discussions, I want to bring a lot of it together in one spot so that we can see what the entire picture looks like, while still leaving some of the details to the titles above. Admittedly, there have been some changes in thinking over time from what was presented in those pieces. For example, the concept of "laboratory automation engineering" has morphed into "laboratory systems engineering," given that in the past 15 years the scope of laboratory automation and computing has broadened significantly. Additionally, references to "scientific manufacturing" are now replaced with "scientific production," since laboratories tend to produce ideas, knowledge, results, information, and data, not tangible widgets. And as the state of laboratories continues to dynamically evolve, there will likely come more changes.

Of special note is 2019's Laboratory Technology Management & Planning webinars. They provide additional useful background towards what is covered in this guide.

The "laboratory of the future"

The “laboratory of the future” (LOF) makes for an interesting playground of concepts. People's view of the LOF is often colored by their commercial and research interests. Does the future mean tomorrow, next month, six years, or twenty years from now? In reality, it means all of those time spans coupled with the length of a person's tenure in the lab, and the legacy they want to leave behind.

However, with those varied time spans we’ll need flexibility and adaptability for managing data and information while also preserving access and utility for the products of lab work, and that requires organization and planning. Laboratory equipment will change and storage media and data formats will evolve. The instrumentation used to collect data and information will change, and so will the computers and software applications that manage that data and information. Every resource that has been expended in executing lab work has been to develop knowledge, information, and data (K/I/D). How are you going to meet that management challenge and retain the expected return on investment (ROI)? Answering it will be one of the hallmarks of the LOF. It will require a deliberate plan that touches on every aspect of lab work: people, equipment and systems choices, and relationships with vendors and information technology support groups. Some points reside within the lab while others require coordination with corporate groups, particularly when we address long-term storage, ease of access, and security (both physical and electronic).

During discussions of the LOF, some people focus on the technology behind the instruments and techniques used in lab work, and they will continue to impress us with their sophistication. However, the bottom line of those conversations is their ability to produce results: K/I/D.

Modern laboratory work is a merger of science and information technology. Some of the information technology is built into instruments and equipment, the remainder supports those devices or helps manage operations. That technology needs to be understood, planned, and engineered into smoothly functioning systems if labs are to function at a high level of performance.

Given all that, how do we prepare for the LOF, whatever that future turns out to be? One step is the development of “laboratory automation engineering” as a means of bringing structure and discipline to the use of informatics, robotics, and automation to lab systems.

Principles of laboratory systems engineering

The laboratory systems engineer (LSE) is someone able to understand and be conversant in both the laboratory science and IT worlds, relating them to each other to the benefit of lab operation effectiveness while guiding IT in performing their roles. A fully dedicated LSE will understand a number of important principles:

1. Knowledge, information, and data should always be protected, available, and usable.
2. Data integrity is paramount.
3. Systems and their underlying components should be supportable, meaning they are proven to meet users' needs (validated), capable of being modified without causing conflicts with results produced by previous versions, documented, upgradable (without major disruption to lab operations), and able to survive upgrades in connected systems.
4. Systems should be integrated into lab operations and not exist as isolated entities, unless there are overriding concerns.
5. Systems should be portable, meaning they are able to be relocated and installed where appropriate, and not restricted to a specific combination of hardware and/or software that can’t be duplicated.
6. There should be a smooth, reproducible (bi-directional, if appropriate), error-free (including error detection and correction) flow of results, from data generation to the point of use or need.

Abbreviations, acronyms, and initialisms

K/D/I: Knowledge, data, and information

LOF: Laboratory of the future

LSE: Laboratory systems engineer

ROI: Return on investment

About the author

Initially educated as a chemist, author Joe Liscouski (joe dot liscouski at gmail dot com) is an experienced laboratory automation/computing professional with over forty years of experience in the field, including the design and development of automation systems (both custom and commercial systems), LIMS, robotics and data interchange standards. He also consults on the use of computing in laboratory work. He has held symposia on validation and presented technical material and short courses on laboratory automation and computing in the U.S., Europe, and Japan. He has worked/consulted in pharmaceutical, biotech, polymer, medical, and government laboratories. His current work centers on working with companies to establish planning programs for lab systems, developing effective support groups, and helping people with the application of automation and information technologies in research and quality control environments.