Technology 2 min read

New Automated Tool KymoButler Helps Scientists Track Moving Particles

A machine learning tool called KymoButler has been developed by scientists to help them track and monitor moving particles.

Image courtesy of Shutterstuck

Image courtesy of Shutterstuck

Researchers have developed an automated tool called KymoButler to help them track the movement of particles inside cells. According to the paper in eLife Reports, the new invention can accelerate research across various fields.

Our good health or lack thereof is often influenced by the movements of molecules, proteins, and other cellular components in the body. Aside from contributing to the progression of some diseases, these movements can also play an essential role in brain development.

As such, it becomes essential to track how these particles move in time and space. The current kymograph method enables tracking of particles by extracting and analyzing time-lapse videos of their movements in a microscope.

There’s just one problem. Since the researchers had to perform the analysis manually, not only is it slow, but it’s also prone to unconscious bias. To solve this problem, the researchers used machine learning technology to automate the process.

In a statement, lead author of the study and Ph.D. student in the Department of Physiology, Development, and Neuroscience at the University of Cambridge, UK, Maximilian Jakobs, said:

“We used the power of machine learning to solve this long-standing problem by automating the tracing of kymographs.”

Using KymoButler to Automate Tracking of Particles

The deep learning technology in KymoButler is created to mimic the networks in the brain. That way, the software can learn and become better at a specific task over time and after multiple attempts.

Next, they used KymoButler to test both real and artificial data from researchers studying the movements of various particles.

Jakobs noted:

“We demonstrate that KymoButler performs as well as expert manual data analysis on kymographs with complex particle trajectories from a variety of biological systems.”

The software also cut down the analysis time to less than one minute rather than the 90 minutes that it would otherwise have taken. And it could only get better.

According to the senior author of the study, Kristian Franze, as the software analyzes various forms of data, it’ll continue to improve. With that in mind, the team provided a link to enable other researchers to upload their kymograph anonymously to further the software development.

“We hope our tool will prove useful for others involved in analyzing small particle movements, whichever field they may work in,” says Franze.

Read More: Scientists Manipulate Brain Cells Using Smartphone-Controlled Implant

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Sumbo Bello

Sumbo Bello is a creative writer who enjoys creating data-driven content for news sites. In his spare time, he plays basketball and listens to Coldplay.

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