Connecting lab automation to decision-making


Ashwin Pillai is Test Engineer 3 in HTS & Assay Development at Ginkgo Bioworks, a company whose mission is to make biology easier to engineer. Ahead of his presentation in the Automation Technologies Track at SLAS2023, Pillai shares insight on data and how we can connect lab automation to decisions.

Pillai works directly with scientists to onboard new assays onto robotic platforms, to improve data pipelines for new instruments and metadata capture, and to enable new technologies for scientists to execute on their lab work more efficiently. He describes his role as involving bridging the gap between automation, biology, and data with a guiding principle of making the lives of scientists easier by enabling the utilisation of automation technologies and data tools. 

Pillai says he has chosen to present in the automation technology track at SLAS2023 to showcase all the different ways lab automation and data can be used to bring meaningful insights and speed up the discovery process. 

Breakthroughs in synthetic biology

Pillai explains that in recent years, there have been significant improvements to the gene editing space within the synthetic biology sector. He says: “Organisms can be edited and synthesised to test out different mutations for improvement in function. These organisms can be synthesised faster, sequenced faster and more reliably, and follow an iterative improvement cycle (design, build, test).”

He explains that there has also been an improvement in lab automation technology through instrumentation and software. He elaborates: “More data can be collected from instruments to ensure high quality data, API integrations across different software (LIMS, Cellario, Snowflake, Spotfire) allow for a more seamless user experience. Less is more, and a centralised location for information helps users get up to speed faster and be more effective at their roles.”

Future breakthroughs

For us to continue on this upwards trajectory, Pillai says that scientific advancements will and must keep occurring as they are absolutely required to continue progressive work. Focusing on the instrumentation and software side of things, he says: “There are so many tools to accomplish the same outcomes that it becomes a challenge to string together the right software tools and workflows. I think the biotech space for software is lagging behind the tech industry. We need to build very purposeful software and be able to integrate old instruments that are industry standard but capable of communicating with APIs, ELNs, LIMS, and Scheduling Software.”

Technology and work

Pillai breaks down why automation is important to his work. He says it allows for the following:

Scale – Reduces the time required to complete a workflow / assay while also increasing throughput of the process itself.

Consistency – Once a protocol is programmed it will run exactly the same every time. This allows for higher confidence in quality and reproducibility.

Knowledge gap

When it comes to the challenges currently being faced and extrapolating insight from data, Pillai says that the challenge is the amount of data that is generated and understanding what is meaningful. He says the questions to ask are: “Data could be returned during a workflow for every single log returned from an instrument, but will that help you answer your question? Data is also returned from every single instrument (liquid handlers, plate readers, liquid dispensers, accessory equipment, incubators, sequencers)… are they all in uniform format or does work need to be done to align all of them? There is also an issue with metadata during experiments. How can you track and tag information so that when someone looks at it a week later or three years later, the data still makes sense?”

Key opportunities

According to Pillai, the key opportunities from lab automation are bridging the gap between biology, automation and data. He asks: “How do you meet the need of the scientist for a workflow (culturing or assay/screening)? How do you enable the scientist to solve their problem while improving scale, and consistency? And how do you return that data in a simple, quickly and in a meaningful way?” 

He says that a person who is interested in solving these types of problems will always find opportunities in the biotech industry. Particularly, he believes those with experience with various instruments, schedulers, programming, data analysis and visualisation, software (API), ELNs, LIMS should strive to turn these components into a seamless single stack.

SLAS 2023 Supplement, Volume 24 – Issue 1, Winter 2022/2023


Ashwin PillaiAshwin Pillai works at Ginkgo Bioworks in Boston, as an Automation Scientist in the High Throughput Screening group. He graduated from University of Illinois with a BS in Ag & Bioengineering with a minor in Integrative Biology. He also completed a Bioinformatics Certificate from Harvard Extension School. Previously, he contributed to R&D in pharma at Bristol Myers Squibb in Lead Discovery (uHTS), supporting Lead Profiling (ADME-Tox) across multiple robotic platforms.


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