For this spotlight, we dive into the world of molecular research and talk to the software team of the highly ambitious startup Lumicks at their Amsterdam HQ.
Lumicks manufactures dynamic single-molecule and cell avidity analysis equipment. Their breakthrough technologies enable visualization of molecular interactions and acoustic manipulation of biomolecules. With their products, Lumicks helps scientists to understand life to the smallest detail, which is critical for cancer research and drug development.
Founded in 2014, Lumicks quickly became a success story. Today, research sites all around the world have adopted Lumicks technology: from UC Berkeley at the U.S. west coast all the way to ShanghaiTech University in far east China.
CAF Team: Dear Lumicks team, thank you for taking the time speaking to us! Lumicks works hard to deliver unique insights into biological processes at a molecular level. A recent article in The Scientist speaks about using sound waves to capture cells, which is a process you use for some of your products, while other products mention optical tweezers. A bit exaggerated: How can lasers and music help us to cure cancer? Without going into too much detail, how would you explain what your products do for people like us that don’t have degrees in molecular biology?
Lumicks Team: The key thing about both the lasers and the sounds waves is that they exert tiny forces. With the music during a pop concert, you can really feel those forces in your stomach. With light, it’s perhaps a bit harder to imagine: after all, you don’t experience a “thump” whenever you turn on the light, nor do you feel the sunlight pushing you back on a sunny day. Yet even light pushes against things a tiny bit, and this is actually used in the vacuum of space to drive spacecrafts with “solar sails”. At Lumicks, we harness those tiny forces to push and pull on the cells and molecules that make up your body. We can, for instance, “grab” a DNA molecule by its ends and stretch it, and then watch how other molecules land on it to repair damage on the DNA. Researchers learn invaluable things from such experiments about the processes that lead to diseases, including cancer.
CAF Team: You are working with very small scales but very high resolutions. We can imagine it takes quite a lot of data processing in real-time in order to visualize the experiments and allow scientists to manipulate individual proteins or cells while studying their behavior. What are the biggest software engineering challenges that you faced during initial product development?
Lumicks Team: Perhaps surprisingly, data volume is not our biggest challenge. Our sensor data comes in at hundreds of MB per second, tops. The real challenge lies in the large variety of hardware we need to talk to from the software, and orchestrating all the hardware in a coordinated way, often with strict timing requirements. We also have both a GUI and Python interface, which can run simultaneously, and so need some careful synchronisation. Plus each researcher using our software has their own specific needs for the experiments they are doing, so we need a lot of flexibility in putting the various modules together.
CAF Team: A big part of software engineering is constant learning about the problem domain and iterating on a prototype until a robust solution emerges. However, we can’t iterate endlessly. Time to market is very crucial, even critical in a startup like Lumicks. What brings the actor model, and CAF in particular, to the table that helps you iterating faster and reaching a sophisticated solution in reasonable time?
Lumicks Team: Our application does a lot of parallel processing and multi-threading: there is all sorts of hardware I/O running in the background, plus data processing, storage management, etc. CAF allows us to stay sane amidst all the threads: the actor framework enforces a clear model of data ownership and saves us from a lot of mutex-related headaches. Not having to worry about a whole class of subtle, hard-to-reproduce bugs is a life saver. On top of that, the availability of compile-time checking of messaging interfaces is very useful, and something where CAF has a tangible advantage over actor implementations we’ve seen in other frameworks and languages.
CAF Team: How did you learn about CAF and what convinced you to use it over other frameworks?
Lumicks Team: As a team we have used actors in other languages, such as LabVIEW and Erlang, and found it to be a good model for parallel processing. During the development of our control software it became clear that actors would be a great fit and and so we undertook a review of C++ frameworks. CAF came out as a clear winner and we have been using it ever since. The active development of the library was a major factor for us, as well as its flexibility and level of compile-time safety.
CAF Team: How would you summarize your experience with CAF so far? What was the first version you’ve used? Were there any unexpected obstacles in adopting CAF or aha moments?
Lumicks Team: CAF has been working very well for us. We started on version 0.15.5, in the Fall of 2017. We had some small issues in the beginning, mostly related to the lack of official shared library support on Windows, but otherwise have been closely tracking the official releases. We haven’t had any major unexpected obstacles, so far, but finding the Qt mix-in was a pleasant surprise. It made integrating with our UI components much easier!
CAF Team: You develop all of your software in house with an international team of software engineers. How do you introduce new team members to CAF and how would you describe the learning curve?
Lumicks Team: We haven’t found the learning curve for new team members to be too challenging. As a team we have built a number of abstractions around CAF and so this helps to reduce the surface area that new team members need to learn. We also work with CAF on a single node, and use typed interfaces as much as possible to make intentions clear and explicit. Processes within the team also help. All of our code is thoroughly reviewed before merging and we do lots of internal knowledge sharing, from working together on tricky problems to fortnightly show and tell sessions on new and interesting developments.
CAF Team: What could CAF do better to smooth out the learning curve and help developers being more productive?
Lumicks Team: Although the CAF documentation has improved since we first started using the library, it is still an area where more work would help to smooth out the learning curve, along with some more complex examples of CAF usage. Improved tooling for debugging would also make a big impact on productivity.
CAF Team: Final question: if you were to decide what the next feature of CAF would be, what would you have in mind?
Lumicks Team: One feature that would really help us as a team is better tooling to understand message flows, for example to visualize the flow of messages between actors. It would help not only to debug issues during development but also to optimise performance. We are also particularly interested in the possibilities of the new streaming API, so seeing that move out of its experimental state would also be great!
CAF Team: Thank you very much for this interview! We wish you and your team all the great success in helping scientists around the world to understand—and hopefully cure!—diseases such as cancer and Alzheimer’s!
Lumicks Team: Thank you! If any of your readers are interested in working with CAF to help scientists unlock new types of experiments in a small dynamic team based in Amsterdam then check out our careers page!