Tenets of Lab Automation – Software

Tenets of Lab Automation - Software - Hero Banner

Lab Automation Software: An Under-Appreciated Champion

In our first installment of the Tenets of Lab Automation blog series, we discussed hardware. In this second installment, we will focus on software.

As a company with a deep history in the life science industry, we’ve experienced and participated in many product launches over the last few decades. We’ve personally implemented several dozen different devices, systems, and workstations. And at each step, we’ve assisted scientific partners with the expertise needed to integrate disparate tools into seamless, time-saving, and cost-efficient automated workflows.

What puzzles us from that experience is seeing that most vendors focus on the importance of hardware only to overlook the underlying software of their products.

Why would they downplay such a critical component of an automated workflow, especially as software plays such a significant role in shaping every aspect of today’s world?

Software is ubiquitous in our daily lives; from smartphones that are always within arm’s reach, to the apps we use to access information, communicate with others, and navigate our surroundings.

Software also shapes our impression of the world.

One way it does so is through user interface design. An application or website design can influence how we perceive information presented to us. For example, a well-designed and intuitive app user interface can help us feel productive and efficient, while a poorly designed interface may be frustrating and unproductive to navigate.

Another way that software shapes user impressions is through algorithms. One good example of this are algorithms that are used by search engines and social media platforms to personalize the information we see based on our search history and online behavior.

Lab automation - Illustrative image

Elevating the Value of Software in Lab Automation

In the life science laboratory, software helps shape our impressions of the world. By allowing us to generate data, and then draw insights and make decisions based on data, software ultimately helps us make positive impacts towards discoveries and breakthroughs in life science.

Indeed, without software, we cannot command our scientific tools to perform desired tasks such as dispensing reagents to a microplate on a dispensing device. Similarly, without software controlling the process, sample transfer from one item of labware to another on an automated liquid handler would simply not be possible.

What’s more, without software, you would also lose the ability to schedule workflows to be performed on a robotic Work Cell at a future date or time. And you couldn’t process or store findable, accessible, interoperable, and reusable (FAIR) data.

From allowing us to interpret data to helping operate our hardware devices, lab automation software is critical to the success of scientific discoveries as well as our daily lives.

Illustrative Image - Lab Automation

Digitization of Scientific Records

Software also enables the digitization of scientific records. Digitization is an essential practice in life sciences (and many other industries) for several reasons:

  1. Digitized records strengthen accessibility. Digital records are much more accessible than paper records. They may be easily searched, sorted, and shared with others, even remotely. This allows for quick and easy access to valuable information while it enhances collaboration.
  2. Digitized records don’t occupy valuable floor space. Digital records take up much less physical space compared to paper records. This is particularly important for scientific organizations that produce and store large amounts of data. In addition to reducing physical space, this reduces or eliminates costs associated with physical document storage.
  3. Digitized records are well-preserved. Digital records are much more resistant to physical alteration and tampering compared to paper records. This is particularly important for laboratories who seek regulatory approval or even for those maintaining robust process control.
  4. Digitized records promote efficiency. Digital records are easily sharable and accessible by multiple users simultaneously. This helps to improve efficiency and collaboration across even large organizations and scientific disciplines.
  5. Digitized records are secure. Digital records may be encrypted and secured with access controls. This protects sensitive information, such as personal patient sample identification, from unauthorized access, theft, or accidental deletion.
  6. Digitized records are cost-efficient. By digitizing records, organizations can reduce costs associated with physical storage and manipulation, organization, and data retrieval.

Digitalization has been a common practice in tech, and tech-adjacent industries for well over fifteen to twenty years. However, it’s been only in the last several years that digitalization has really taken hold in the life sciences industry.

Because of the recent expansion of digitalization, life scientists today are witnessing a digital transformation in real-time.

This digital transformation will propel scientific research by aligning scientific data with FAIR principles. It will also facilitate connecting of software tools into the Internet of Things (IoT) and enable data streams to flow across organizations and between collaborators.

For organizations seeking to gain a competitive advantage or a deep dive into their project scope, this software-driven digital transformation turns an already beneficial laboratory automation system into a highly efficient data factory. The possibilities are endless.

Opening a Passage to Insight and Knowledge

Proper laboratory automation software must move beyond the boundaries of workflow scheduling and process management to create data factories. By harnessing data generated from their automation platforms, life scientists can now make decisions faster and with greater insight and visibility than before.

Using laboratory automation software, scientists can turn large datasets of experiment results into actionable insights. The data hidden in tables and spreadsheets can be unlocked and converted into valuable ideas and discoveries.

In the past, it was also necessary to hire expert coders to create black-box programs. Scientists would point and click through these programs in the hope of beginning the discovery process.

Today, we have open-source code. Anyone can learn enough of the basics to perform powerful operations and share them with others. Open-source code offers many overlapping benefits in the scientific realm:

  1. Collaboration. Open-source code allows developers worldwide to collaborate on scientific projects, share ideas, and improve their workflows. This can lead to an even greater speed of scientific discovery.
  2. Transparency. Open-source code is transparent. Anyone can view and audit code for accuracy, security vulnerabilities, or other issues. The accessibility of open-source code strengthens the foundation of science, which is reproducibility.
  3. Flexibility. Open-source code may be customized to meet specific needs and requirements. This is particularly useful for different applications across an organization or discipline.
  4. Innovation. Open-source code can inspire innovation by encouraging experimentation and sharing ideas. Greater collaboration can lead to new and innovative solutions that positively impact human health and wellbeing.
  5. Support. Open-source code often has a vibrant community of developers. These like-minded individuals are a potential source of support, troubleshooting, and resources for each other. This can help to foster a sense of community and collaboration within the development community.

Modern Labs are Built Around Software

Python and C# have become ubiquitous scripting languages across laboratory automation. They diversify possible tasks, and offer free ‘libraries’ that are pre-made specifically for interacting with written chemical structures and laboratory equipment.

Scripted workflows cost only the time to write, along with any additional time needed for user training. An optimized script could obviate transcription errors and feature greater functionality and rapid scalability.

Hardware, as the physically visible part of laboratory automation, would not be possible without corresponding software to digitize and automate records.

As discussed, digital record-keeping systems such as laboratory information management systems (LIMS) or electronic lab notebooks (ELN) are indispensable to modern laboratory infrastructure.

These digital record-keeping systems save time in preparing test records and writing test results on paper. They can also check handwritten records by peers to spot and flag any potential errors.

Digital record-keeping systems have also enabled better management of workflows; creating structures for research that are adaptable and flexible. Each of these are requirements for successful research.

Software and Laboratory Orchestration

Historically, pharma R&D laboratory technology infrastructure consisted of numerous devices from various vendors. Scientists manually transferred sample-containing labware between devices and keyed in protocol results from one process to another.

In this labored and disjointed approach, there was no ‘flow’ in the workflow. At the same time, variability, and risk of errors from manual intervention was quite high.

Fast forward to today, and automation is a very welcome innovation that helps to maintain a steady current of ‘flow’ in and around the lab.

In an automated workflow, robots connect scientific equipment to bridge the device interface gap. Automation creates data factories by integrating scientific instruments and data sources. And ultimately, automation empowers scientists to collect, process, and analyze the data that they need to produce scientific insight and differentiate their institutions from the rest of the pack.

Enabling this flow is a core focus within laboratory software technology today. Seamlessly connecting scientists across their equipment loop to create data streams enhances data handling and archiving. It also improves user and data tracking and reduces the barriers to adoption across scientific organizations.

Some common steps involved in developing the data streams include:

  1. Identifying the scientific equipment needed. Identify the tools necessary for the research and choose equipment compatible with other instruments.
  2. Standardizing data formats. By standardizing data formats, data can be easily shared and analyzed by different instruments and software.
  3. Connecting equipment. Various communication protocols, such as USB, Ethernet, or wireless connections, allow the smooth transfer of data between devices.
  4. Collecting and storing data. A centralized location, such as a database or cloud storage, enables easy data access and analysis.
  5. Processing and analyzing data. Use various software tools to extract meaningful insights.
  6. Visualizing and communicating results. Analysis results must be visualized in a way that is easy to understand and communicate to other colleagues and the public.

These efforts result in creating 24/7 data factories that are orchestrated across single or multiple laboratories, across research sites, and even across global networks!

This integration of data standards and device communication is emerging as a game changer. Cutting-edge software providers offer laboratory orchestration or device scheduling software platforms to lead the laboratory technology charge.

Next-generation software provides drivers for various laboratory hardware with consulting or engineering services to connect hardware in a vendor-agnostic manner. By doing so, a holistic approach to laboratory automation is achieved.

With data connected to central dashboards, laboratory automation software links every device to shared analytical systems. This, together with new capabilities in advanced analytics and machine learning, significantly accelerates scientific outcomes.

Gains can include optimizing scientific processes, devices, and workflow metrics, and, ultimately, the interconnected laboratory.

Illustrative image - Lab automation - Cellario

Forward-Reaching Challenges

Some vendors may say that software is the driving force behind lab automation. They will tell you that you can implement your do-it-yourself automation project via their software. They may even say their software can orchestrate your lab for you.

As a matter of fact, at the most recent SLAS2023 conference, many vendors touted their cloud and lab orchestration capabilities. In reality, most software solutions presented at the conference were either in early-stage development or vaporware.

We have only begun our software journey in the life sciences. No single vendor today can provide a complete software solution for simplifying every process within your lab. At least not yet.

In the meantime, laboratory software companies are making phenomenal strides forward.

The Future of Lab Automation Software

In conclusion, scientific discoveries in the life science industry depend heavily on software.

With the effective use of software, laboratories can drive expanded automation and digital record-keeping to ensure greater accessibility, efficiency, and security, as well as cost- and space-savings.

Successful laboratory automation software goes beyond workflow scheduling and process management. It creates data factories that empower life scientists to utilize data from their automation platforms for quicker and better decision-making.

Open-source code promotes collaboration, transparency, flexibility, innovation, and support among scientists. Python and C# are now widely used scripting languages in laboratory automation, expanding the possibilities of tasks that can be performed.

By keeping an open and extensible software architecture standard, it will not be long before we can see the real impact that software can have on scientific discovery.

We look forward to watching software—as the second tenet of laboratory automation—drive the next technological and scientific gains.

Amplify your Automation Learning Experience

What’s the difference between semi-automated and fully automated workflows?

Many device manufacturers claim that their products are fully automated. This doesn’t mean that the workflow itself is fully automated.

Any time a human must perform a task in an otherwise automated workflow—even transferring samples from one device to another—the workflow is semi-automated. A fully automated workflow requires no human interaction other than responding to prompts if necessary.

One workflow is not necessarily better than the other. In fact, it depends on the lab’s needs and budget within their application. Need help deciding which workflow is right for your lab? Contact us for expert guidance and assistance.

How does the IoT impact labs?

The internet of things (IoT) has infiltrated just about every aspect of modern life, yet the concept is quite simple.

IoT refers to any collection of devices connected to a network and the internet. In a lab’s building the IoT can support daily operations, including environmental regulation, smart lighting, and security controls. In the smart lab itself, the IoT can include LIMS software, automated storage devices, automated liquid handler, absorbance reader, sequencer, and much more.

The IoT connection allows these devices to communicate and transmit data with each other. Among other benefits, this allows for remote monitoring, digital data sharing and storage, real-time insights and alerts, inventory and sample tracking, and proactive device maintenance.

Overall, the IoT empowers the scientific community to gain deeper insights than ever before and to also approach the future in a proactive, rather than reactive, manner.

What are drivers?

A driver is the small intermediary software program between a device and the device’s application programming interface (API). The driver works with the API to tell the device how to function. In an automated workflow, a collection of device drivers can be added to whole lab automation software, like Cellario, to facilitate hands-free operation and data collection.

What’s the difference between LIMS and ELN?

These days, the distinction between a laboratory information management system (LIMS) and an electronic lab notebook (ELN) is dynamic and even overlapping.

Generally speaking, a LIMS is sample-centric, meaning that it tracks samples, inventory, and data throughout the sample lifecycle and generates analytical reports. A LIMS is an excellent tool to manage structured, repetitive workflows in manufacturing or regulated environments.

An ELN, on the other hand, is experiment-centric, meaning that it is a searchable repository for all information related to a given experiment, including data and context from multiple sources, interpretation, and troubleshooting. An ELN is ideal for managing unstructured and changing workflows, especially in research and development labs, as well as accommodating personalized ways of working.

Request Info