Mentor: Shaun Sze-Xian Lim
Evaluating the behavior of mice is essential to evaluating the effects of various biochemical and physical manipulations and shapes the way for elucidating new drugs and therapies that can potentially translate into the clinical setting. The 3D-assisted Neural Network for Computational Ethology (DANNCE) algorithm was developed to more quantitatively describe mice behavior by making predictions based on a machine learning algorithm. However, currently, there is not a standardized device, or arena, in which to place these mice and implement the algorithm. Our lab developed an arena, which by tuning mirror angles, can capture 5 perspectives of a mice enclosed in a rectangular box with one high-resolution camera. By using a large sample of mice in this arena, we hope to establish the efficacy of our design and standardize mice behavior observation. In our experiment, we alter the phase of the circadian rhythm of the mice, their dosage of caffeine, and observe the effect of gender on different mice behavior. We are currently working on completing the data set collection and hope to see preliminary predictions that are consistent with our expected observations.
This week, we all had the opportunity to listen to our peers deliver an 8-minute chalk talk, briefly discussing their projects, questions, and methods they are employing to investigate their area of research.
One talk I found particularly interesting was Sid’s talk on investigating the biochemical and physical differences between cell lines that are able to extravate and proliferate in the organs they metastasize to and those that can’t. Of course, his research is highly relevant to combating cancer, since if we can find what makes certain cells more malignant, we can exploit those biochemical and physical properties to create cures. However, I found even more interesting how he discussed that all cell clones enter the bloodstream in intravasation but only some divide in the organs at which they metastasize to. It seems like one of the first questions researchers would ask and it’s fascinating that there are still no answers, like in much of cancer research.
I’m excited to see where Sid’s lab’s research goes in the future and what other ways the information he is researching over the summer can be used to develop more effective cures.
My mentor, Shaun, was interested in science and technology long before his work today at the intersection of neurobiology and biomedical engineering. He majored in engineering and specialized in biomedical engineering at the University of Cambridge, possessing a propensity for both creation and innovation and helping others live healthier lives. He took one year after his undergraduate education to immerse himself in mechanobiology research in Singapore as a research technician and furthered his knowledge of this niche area in biology.
Subsequently, he decided to pursue an MD-PhD. However, his journey is unlike most of the students in the combined program. He completed the first two years of his M.D. from Duke-NUS, but instead of obtaining his Ph.D. there as well, decided to come to Duke to conduct research in the Tadross Lab. He did this as it aligned more with his goal of combining neurobiology with the engineering process and building tools to study the brain, arguably the most complex component of our biology. He plans to complete his M.D. at Duke-NUS and hopes to see himself conducting research that not only advances science, but also translates into helping people live a better quality of life.
Talking to Shaun was an extremely insightful experience for me, as I also hope to continue the path of research and medicine. I gained long-term advice – like the importance of having a clear and focused research question – and short-term advice – such as how to deal with the frustrations of failure in research and acquire a more positive mindset in dealing with unexpected outcomes. With this interview, I learned more about what it meant to be and think like a researcher.
Over the past few years, the lab has been working on developing an imaging box, which takes multi-angle videos of mice in a box, observing their locomotion and other behavior. It provides a way for us to more accurately quantify the behavioral phenotype we often observe in mice due to our experiments. Just recently, the box was finished and put into use, and now it’s time to test and verify its efficacy.
When we look at mice behavior in the lab, we typically use an algorithm like 3-Dimensional Aligned Neural Network for Computational Ethology (DANNCE), which is more robust than traditional techniques because by using machine learning, it can create a virtual diagram of a mouse using points in space and analyze how those points move about over time.
DANCCE Algorithm at Work from Dunn, T.W., Marshall, J.D., Severson, K.S. et al. Geometric deep learning enables 3D kinematic profiling across species and environments. Nat Methods 18, 564–573 (2021). https://doi.org/10.1038/s41592-021-01106-6
This gives us a more refined way to quantify mouse behaviors like grooming and turning associated with Parkinson’s disease, the lab’s ultimate focus.
Our experiment consists of testing two variables, drug dosage and the circadian rhythm, on mice behavior, locomotion specifically. By using a technique called Principal Component Analysis, we will take the data of mice moving in 3-dimensions and compress it onto a single image from which we can see differences in mice locomotion. With further analysis, we hope to be able to show that our box does indeed pick up on the differences, no matter how subtle, between mice behaviors in a quantitative and informative way.
My name is Shivam, and this summer, I have the opportunity to continue research I have been conducting at the Tadross Lab, and take a new direction with it for my BSURF project. I am working extensively with mice to explore how in the long-term we can create a “behaviorsome,” analogous to the genome and transcriptome at the cellular level. Specifically, for my project, I am exploring how the compound effects of circadian biology and drugs can affect mice movement in 3D. To do this, we developed a box that uses machine learning to analyze mice motion in 3D, giving us a new way to think about activity and movement.
I am super excited to truly delve into my research and dedicate the time to moving this project forward. I expect this experience to be different from the school year in that I have the opportunity to make a bigger commitment and more progress. I also hope to continue this research forward in the school year, given the broad nature of the project.
This project has lots of room for error, and I am eager to learn how we can go wrong, and how we can improve. I am grateful to the BSURF program for giving me the opportunity to do so this summer, and am looking forward to seeing where my research goes!