Automated experiments boost precision and reproducibility

Synthace's Antha cloud platform offers an automated framework for experimentation and analysis. This allows for rapid, codeless design of liquid handlers, as well as visual previews and error checks prior to experiment execution.

    “Biological systems can often be quite noisy, and a lot of assays tend to be very labor- and resource-intensive,” explains Michael Sadowski, PhD, Head of Bioinformatics at Synthace. “That can impose a lot of constraints on how big an experiment you can do if you’re doing everything by hand.”

Antha not only overcomes the issue of speed in manual testing, but it also makes accuracy and reproducibility easier.


Additional difficulties arise as a result of manual operations. One is that scientists frequently develop ad hoc spreadsheet-based tools that, although beneficial for certain use cases, are time-consuming and error-prone to adapt for future studies. Another issue is that critical background data, such as instrument setups and ambient conditions, is frequently overlooked.

“There's a lot of administrative burden involved in capturing data and putting it in the right context,” Sadowski says. “You typically interact with dozens of different machines, all of which have their own interfaces and data formats. Then you have to wrangle all that into your analysis software.”

Antha allows for automated data collecting, organising, and visualisation in addition to execution automation. According to Sadowski, the approach eliminates human error and makes science more repeatable through documenting and rigorous adherence to study procedures, which helps speed up discoveries. He says that in a traditional laboratory environment,

“there are things you don’t realize you’re doing and things you don’t record that might be critical to the process. So other scientists can’t later understand what you did. Studies show that it’s rare to find enough information in a published paper to reproduce an experiment.”

Early on, Antha's engineers recognised the benefit of automating the difficulty of repeatability. Synthace, on the other hand, did not stop there. They're pursuing a broad concept of "Computer-Aided Biology," which they've defined as a way to apply computational approaches to experimental issues. The goal of Computer-Aided Biology is to eliminate the bottlenecks that currently exist when dealing with biological systems by developing software to manage the large-scale data collection, organising, analysis, and modelling that is necessary to comprehend biology.




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