Perhaps you learned the scientific method in high school: problem, hypothesis, experiment, analyze data, draw conclusions. Industrial scientists across the scientific and technological landscape have streamlined this process into the Design, Make, Test, Analyze (DMTA) Loop. You start with a design, run your experiments, collect and test samples, then analyze the data and use the insights derived to iterate on the next design. Electronic Lab Notebooks (ELNs) and Lab Information Management Systems (LIMS) are arguably two of the most crucial systems used in the Design and Analyze phase of the DMTA cycle.
Let’s craft an analogy — An ELN is like a storybook, with a distinct beginning, middle, and end. Often, though it depends on your domain, we begin with a picture of what you hope to achieve. For chemists, it’s a reaction scheme with the predicted end product. For biologists, perhaps it’s a specific animal model of disease or a protein structure. For pharmacologists, it’s a target assay or endpoint.
As ELNs are “experiment-oriented,” they focus on experiment results and functions like an “Evernote for science labs.” These are generally more unstructured and flexible, allowing the end user to dictate how to structure their work and affirm success.
A LIMS, on the other hand, is “just the facts, ma’am": it’s sample-oriented and focuses more on barcode tracking, workflow management, and handling analytical results performed on groups or lists of samples. LIMS typically enforces a rigid structure and is less free-form than an ELN.
Let’s zoom out and consider the informatics landscape in biopharma R&D. Few would disagree that challenges begin to mount: data complexity increases and systems fragment. Workflows are held together with brittle, point-to-point integrations. Labs and even individual scientists produce heterogeneous data streams. To introduce some order into this otherwise chaotic data landscape, we here at TetraScience tend to apply a mental model to every R&D workflow — we move information and data from data sources to data targets.
Data sources are where valuable R&D data are produced (or entered) by scientists and lab instruments. Some common data sources include:
Data targets, on the other hand, are systems that consume data to deliver insights and conclusions or to generate reports. There are roughly two types of data targets:
Biopharmas make significant investments when selecting ELN/LIMS. It pays dividends to optimize the scientists' experience when using these solutions.
However, one of the major challenges impeding scientific ROI is the tedious busywork of capturing experimental workflow from, and getting data into, the ELN/LIMS. For example:
In summary, major pain points include moving information from the analyze/design to execution (Make/Test) phases of the DMTA loop, and back again.
When following compliance or expecting high data integrity, this problem manifests more severely, leading to second- or third-scientist reviews. When high-throughput experimentation (HTE) is involved or large files are produced, this problem becomes unmanageable due to the sheer size of the data and the number of operations needed. Imagine spending an hour to run your assay and then 2-3x more time to collect, move, transcribe, upload, and review the same data!
That’s why using Tetra Data Platform (TDP) and ELN/LIMS together is the growing trend in biopharma’s digital lab stack — enabling a true “lab of the future” or “digital lab” experience. The scientists design their experiment in ELN/LIMS, bring their experimental design or sample list to the lab for execution, and then get the data back into the ELN/LIMS without worrying about the “plumbing.” Very often, biopharma uses multiple ELN/LIMS depending on the department or data streams. TDP serves as the data exchange layer for multiple ELN/LIMS while remaining invisible to the scientists. We provide background data stewardship: harmonizing, validating, and moving the data to facilitate the Design, Make, Test, Analyze (DMTA) loop at the core of every scientific process.
Just like almost everything else in life, execution starts from the planning and design. The experimental work starts in assay design or as a request to measure a list of samples (sometimes called a batch). Using TDP, scientists can automatically send experimental designs and transfer batch information or sample lists to instrument control software like Waters Empower, ThermoFisher Chromeleon, or AGU SDC.
Once the scientist finishes executing an experiment, TDP can automatically collect the data, harmonize, validate the data, and then push data into the ELN/LIMS. Sometimes, scientists will search and retrieve the data from the TDP and then import into ELN/LIMS to start their analysis.
Save time and ensure data integrity. Leveraging TDP as the data exchange layer between analysis and execution eliminates manual input of design parameters or sample identifiers to instrument control software as well as the need to manually enter data back into the ELN/LIMS, saving scientists valuable time, preventing compliance issues related to manual transcription, and eliminating tedious second- or third-scientist review by enabling a validated workflow.
Enable data science. Importantly, the automated integration allows ALL scientifically-relevant data to be collected, stored, and queried through the TDP, enabling other data science oriented tools to leverage the experimental data and providing rich data sets for future analysis (data that scientists may wish to ingest into their ELN and LIMS).
Project agility and time-to-value. With TDP focusing on the data (exchange) layer, biopharma organizations now have one throat to choke in terms of bridging the gap between data sources and targets, simplifying project complexity, and shortening time-to-value. TetraScience is responsible for maintaining and upgrading productized integrations and moves in conjunction with ELN/LIMS vendors during rollout.
Our biopharma clients and partners often ask us: “Why wouldn’t an ELN or LIMS just interface with instruments directly? Wouldn’t that be more efficient?”
If you work for an “early-stage” organization with limited data, a lab under construction, or your assays and syntheses change often, this may not be a bad idea. In fact, many ELN/LIMS providers enable features for tabular data import...or your scientists can just manually transcribe the data into/out of ELN/LIMS.
However, if your life sciences company has reached a certain scale — remember, ELN/LIMS core focus is not connecting data sources and targets — very obvious disadvantages will surface when ELN/LIMS are the “connective tissue” of the data layer:
Every day, more ELN/LIMS providers leverage TetraScience to gain a foothold in the cloud-first data landscape and partner with us to drive synergistic product value between our systems.
Linked below are documents describing some real-world data integration examples with industry leading providers, including bidirectional (pull/push) data exchange, showing the full DMTA cycle brought to life and written in data.