According to the dictionary, orchestration refers to the arrangement of music for an orchestra, or more broadly, the careful coordination and organization of components to achieve a specific outcome. It involves ensuring that every element works together harmoniously, creating a unified result. While this definition applies to music, the concept has evolved to play a critical role in the world of technology, particularly in areas like DevOps and, more recently, laboratory automation.
Orchestration in Tech
In IT Operations, orchestration refers to the automated configuration, scheduling tasks, managing resources, and coordination of systems, software, and processes across various environments. Think of it as a conductor managing different instruments to ensure they perform in sync, but instead of musicians, you're dealing with servers, containers, and applications. The goal is to streamline workflows, reduce manual intervention, and ensure systems are functioning efficiently and predictably using a single orchestration tool across a diverse set of resources.
In DevOps, orchestration tools such as Kubernetes, Jenkins, and Ansible are ubiquitous. They automate the deployment of software, configuration management, and the coordination of processes across complex systems. The result is faster, more reliable software delivery, just as a well-orchestrated musical performance result in a seamless, beautiful symphony.
Orchestration, in this sense, may have its roots in the tech world, but its principles resonate beyond servers and containers. Just as DevOps orchestration ensures harmony in complex IT ecosystems, laboratory orchestration applies these ideas to scientific workflows, bringing a similar level of coordination and efficiency to the lab. By exploring how orchestration translates from tech to the lab, we can uncover its potential to transform scientific processes into seamless and synchronized workflows.
Laboratory Orchestration
To better understand what lab orchestration truly means, we reached out to nearly 100 of our customers, and their answers highlighted the breadth and depth of the concept.
Responses varied widely, but common themes emerged around aspects like data flow and management, decision-making, process insight, and minimizing errors. Other key benefits mentioned were optimizing processes for speed and cost-efficiency, ensuring reliability, and providing a single coordinated platform to manage systems, instruments, and tasks. Customers also emphasized the importance of planning and scheduling, managing material and labware, as well as maintaining clear and constant communication.
These responses reveal that the definition of lab orchestration is broad and multifaceted. For many, the perceived benefits of lab orchestration were described before articulating the concept itself. With this in mind, let’s dive deeper into what we believe is central to lab orchestration, provide a practical example, and discuss the tangible impact it can have on lab operations.
The Core of Lab Orchestration
At its core, lab orchestration is about creating seamless workflows that integrate instruments, data, people, and processes into a unified system. This is achieved through automation and system coordination, enabling a lab to run more efficiently, with fewer errors and delays. To illustrate this, let’s break down the primary elements of lab orchestration:
- Device Coordination: Orchestration involves managing communication between diverse lab devices (e.g., liquid handlers, plate readers, incubators) to ensure workflows proceed without human intervention. It enables the execution of complex, multi-step processes, ensuring that tasks are performed in the correct order and with appropriate timing. Many times, this is in the domain of the Scheduling Software, but these activities are fundamental to orchestration.
- Data Automation: One of the most significant aspects of orchestration is the management, flow, and integration of data—whether it’s measurement/result, operational/event, or inventory data. Lab orchestration helps streamline data flow across multiple platforms, ensuring that it is captured, shared, and analyzed effectively. For example, if a researcher collects data from an instrument, orchestration ensures that the results are contextualized and automatically logged into a central database for further analysis or reporting along with relevant metadata. This minimizes the need for manual data entry, reduces the risk of errors, and speeds up the decision-making process.
- Hybrid Workflows: Life Science execution is comprised of a variety of activity types, e.g. software systems, lab user or manual tasks, device operation and robotic material movement. Orchestration bridges gaps between all of these activity types. It allows you to combine manual and automated steps, ensuring human interventions fit seamlessly into overall processes. This can be human in the loop for decision making, taking an action or even just notification.
- Task Coordination & Scheduling: Laboratories must manage multiple instruments, samples, and research teams to execute complex workflows. Orchestration ensures that all tasks, from sample preparation to analysis, are scheduled and coordinated in a way that minimizes downtime and maximizes throughput. For example, a researcher may need to run a series of tests on a sample, but before that, specific equipment needs to be calibrated and prepared. Orchestration tools can automatically schedule these tasks and ensure that all required steps happen in the right order.
- Workflow Efficiency: With lab orchestration, the overall goal is to improve efficiency by optimizing processes. This might mean reducing turnaround time, improving the accuracy of results, or reducing resource waste. For example, if a lab is running multiple experiments simultaneously, orchestration can help prioritize resources like equipment or personnel to avoid bottlenecks and delays, ensuring that experiments are completed as quickly as possible without compromising quality.
- Reliability and Process Logging: Another central aspect of orchestration is ensuring that lab operations are reliable and traceable. By automatically logging every step of the process, from sample preparation to results, orchestration helps ensure consistency and transparency. This is particularly important in regulated environments where strict documentation is required. Every action taken within the lab is recorded, making it easy to track and verify results, and reducing the risk of human error. This process logging is also fundamental to performing data driven operational optimization. You can’t get better without measuring.
- Collaboration and Communication: As laboratories are increasingly collaborative environments, constant communication is essential for smooth operations. Orchestration tools facilitate communication between individuals and groups, ensuring that everyone involved is aware of the status of an experiment or process. For instance, if one team finishes a task, orchestration ensures the next team is notified and prepared to continue. This helps eliminate communication gaps and ensures that tasks are completed on time.
Framework Thinking Enables Lab Orchestration
The ability to implement lab orchestration effectively hinges on a strategic and intentional approach to resource management and workflow integration. The scope of lab automation in a scientific environment varies widely depending on the nature of the workflows, the historical preferences and expertise of staff, and the available budget.
As staff turnover occurs and projects evolve, instrumentation preferences and lab automation strategies change, leaving behind a heterogeneous automation landscape. Despite these variations, certain core needs remain consistent across laboratories:
- Data Integrity: Instruments must capture data in an organized manner, associating it with sample IDs and relevant metadata.
- Laboratory Efficiency: Resources—both human and technological—must be utilized widely and effectively.
Given these challenges, laboratories must adopt a structured framework to enable seamless orchestration. By intentionally organizing their diverse automation investments, data workflows, and resource allocation, labs can create a cohesive environment where operations are unified and well-documented and where orchestration is possible.
This is fundamental to the digitalization of science.
For true DMTA or any other closed loop process to be fully automated, orchestration is not a nice to have, it is a necessity.
A Case in Point
Consider an NGS core laboratory performing workflows such as DNA/RNA extractions, NGS library preparations, normalization, and pooling. These processes often involve independent automation using a mix of software and hardware from different vendors. Additional steps, such as QC for extracted materials and final libraries, may be performed manually on standalone instruments. Finally, sequencing data is generated manually using yet another software platform.
This setup—common in many labs—is the result of years of discrete investments made by different individuals. Consequently, the lab generates data from six separate software packages, creating silos that can hinder traceability, reproducibility, and efficiency.
By applying a lab orchestration framework, such a laboratory can integrate these disparate tools and processes into a streamlined system from Request to Result. This approach transforms the lab into a cohesive data and workflow engine, enabling traceable, reproducible, and high-quality data generation.
Orchestration as a Strategic Advantage
As laboratories across pharmaceutical, biotechnology, and academic industries increasingly prioritize efficiency and reproducibility, intentional framework thinking has become essential. Lab orchestration provides a strategic method for integrating automated workflows, manual tasks, data management, and resource coordination, regardless of the underlying complexity.
By harmonizing these elements, labs can reach a new level of productivity by:
- Maximizing Value of Investments: Orchestrated labs ensure all instruments and software operate seamlessly, making data and processes accessible across teams. By overcoming barriers to traceability and usability, labs can unlock the full potential of their resources, enabling more personnel to engage in diverse experiments effectively. Through dynamic resource allocation labs will dramatically improve resource utilization, minimize bottlenecks and idle time.
- Increasing Efficiency and Throughput: Automation streamlines repetitive tasks, such as sample tracking, instrument scheduling, and data entry, significantly reducing human error and saving time. Enhances the lab’s ability to scale processes to handle more sample or experiments.
- Improving Data Reproducibility and Integrity: Orchestration ensures data is captured and processed consistently, minimizing variability caused by manual intervention. Real-time monitoring boosts compliance, enhances reproducibility, and simplifies audits, all while reducing experimental errors.
- Elevating User Experience: Lab orchestration transforms how scientists and technicians interact with systems by providing intuitive dashboards, automated alerts, and standardized workflows. These features reduce complexity, streamline training, and allow personnel to focus on innovation rather than administrative burdens.
- Enhancing Flexibility and Adaptability: Orchestration tools easily allow changes in protocols in a controlled fashion to enable rapid iteration and innovation. New and different Information Systems can be connected using the orchestration platforms integration capabilities allowing your informatic landscape to evolve as needed.
Conclusion
In today’s rapidly advancing scientific landscape, laboratories face growing pressure to improve efficiency, reproducibility, and data integrity while managing an increasing array of complex tasks and technologies. Lab orchestration offers a powerful solution by integrating automated workflows, human resources, and data management into a cohesive, streamlined system. By eliminating inefficiencies, optimizing resources, and ensuring seamless coordination across disparate instruments and software platforms, lab orchestration allows laboratories to work smarter, not harder.
The benefits are clear: enhanced operational efficiency, reduced errors, improved data quality, and better overall resource management. Further, orchestration enables closed loop scientific processes such as Design, Test, Make, and Analyze (DMTA). However, successful implementation requires careful planning, a commitment to training, and an openness to overcome integration challenges. When set up intentionally and logically, lab orchestration not only empowers teams to focus on high-value tasks but also enables faster decision-making and accelerates the pace of innovation.
As laboratories continue to evolve, embracing orchestration will be key to staying competitive in a data-driven world. Whether in pharmaceutical research, clinical diagnostics, or academic experimentation, the future of lab operations lies in orchestrated, unified workflows that deliver more with less. As more of the scientific process becomes automated through tools like AI, orchestration will play a critical role in coordinating activities such as data capture, model training, integrated decision-making, and even leveraging generative AI to determine next steps. With the right orchestration framework in place, laboratories can unlock new levels of productivity, drive research breakthroughs, and ensure that every process—whether manual or automated—works together in perfect harmony.
Learn more about Cellario OS, an API-first cloud-native product that provides visibility and control for a single lab, multiple labs, or worldwide sites. It guides users through executing their scientific workflows; connecting standalone lab devices and robotic systems controlled by CellarioScheduler or other scheduling software.