Open science hardware & software to study bacteria movement
A spotlight on MBac, a 2018 Global Sprint project
Alexander Kutschera (@alexwastooshort) is a biotechnologist, tinkerer and PhD student at the TU Munich where he researches microbiology and plant immunity. Alex is interested in building things that solve problems and make his (lab) life easier, which led to create MBac and join the current round of Mozilla Open Leaders.
I interviewed Alex to learn more about MBac and how you can help at the Mozilla’s Global Sprint 2018.
What is MBac?
Mbac is an open science hardware and software projects which aims to make looking at movements of bacteria easier. The hardware part is a special lighting chamber for agar plates (that’s what microbiologists use to grow bacteria) combined with a single-board computer and a camera. The software part uses computer vision and machine learning approaches to analyze the bacterial movements just from images.
Why is it important to look at bacterial movements?
Studying the movements of bacteria can tell you a lot about how and where they live. Previously, when DNA sequencing was not common, this was used along with other characterization experiments to classify bacteria. Sometimes just the ability to move in a certain way makes the difference between harmful and harmless bacteria. That’s why, in certain cases, one can identify possible dangerous bacteria by looking at their movements.
Why did you start MBac?
It all started when I installed a camera in our incubator (that’s the place where we let our bacteria grow) because I was tired of not knowing what my bacteria a doing during the night. They usually form beautiful shapes and I was always wondering how this happens. From there, I developed the idea to use the images to calculate how fast the bacteria moves and to classify the shapes they form.
Another very important part was that, for my experiments, I was able to reproduce always saw the same patterns when I compared the movements of two bacteria species, but the results from independent experiments had much more variability. Similar observations were already made by various researchers and the only way to minimize the variability is to keep every parameter (e.g. exact drying time of the agar plates or how you apply the seal) of the experiment very stable. With MBac I want to make it easier to keep some of the parameters stable (hardware setup) and additionally extract more data during the experiment (software) which then can be used to minimize overall variability.
What challenges have you faced working on this project?
My biggest challenge was and is communicating about the project in the right way. First of all it was really hard for me not to use science jargon and to keep the descriptions simple and accessible. At the same time I want to avoid oversimplification so everybody can learn something. It is sometimes hard for me to combine both things and still write something people enjoy reading.
What kind of skills do I need to help you?
There are things for everyone! You don’t need any special skills to give me some feedback on the repository README and tell me if you understood everything. There are also issues you could use to learn how to plot beautiful graphs with python while contributing to the software. Also, people with experience in computer vision and machine learning are welcome to help with the image processing.
How can others join your project at #mozsprint 2018?
Visit the repository, join the gitter chat and choose an open issue to work on! I will be available most of the time during the sprint and you can ask me any questions right away. Every piece of feedback, every comment and every idea is welcome!
What meme or gif best represents your project?
Join us wherever you are May 10–11 at Mozilla’s Global Sprint to work on many amazing open projects! Join a diverse network of scientists, educators, artists, engineers and others in person and online to hack and build projects for a health Internet. Register today
