Georgian Technical University Artificial Intelligence Sheds New Light On Cell Developmental Dynamics.

What happens inside a cell when it is activated changing or responding to variations in its environment ? Researchers from the Georgian Technical University have developed a map of how to best model these cellular dynamics. Their work not only highlights the outstanding challenges of tracking cells throughout their growth and lifetime but also pioneers new ways of evaluating computational biology methods that aim to do this. Identifying the trajectories of individual cells. Cells are constantly changing: they divide change or are activated by the environment. Cells can take many alternative paths in each of these processes and they have to decide which direction to follow based on internal and external clues. Studying these cellular trajectories has recently become a lot easier thanks to advances in single-cell technologies which allows scientists to profile individual cells at unprecedented detail. Combined with computational methods it is possible to see the different trajectories that cells take inside a living organism and have a closer look at what goes wrong in diseases. X heading the research group explains: “If you would take a random sample of thousands of cells that are changing you would see that some are very similar while others are really different. Trajectory inference methods are a class of artificial intelligence techniques that unveil complex structures such as cell trajectories in a data-driven way. In recent years there has been a proliferation of tools that construct such a trajectory. But the availability of a wide variety of such tools makes it very difficult for researchers to find the right one that will work in the biological system they are studying”. Evaluating the available tools. Two researchers in the X lab Y and Z set out to bring more clarity to the field by evaluating and comparing the available tools. Y says: “From the start we envisioned to make the benchmark as comprehensive as possible by including almost all methods, a varied set of datasets and metrics. We included the nitty-gritty details such as the installation procedure and put everything together in one large figure — a funky heatmap as we like to call it”. Z adds: “Apart from improving the trajectory inference field we also attempted to improve the way benchmarking is done. In our study we ensured an easily reproducible and extensible benchmarking using the most recent software technologies such as containerization and continuous integration. In that way our benchmarking study is not the final product but only the beginning of accelerated software development and ultimately better understanding of our biomedical data”. User guidelines. Based on the benchmarking results the team developed a set of user guidelines that can assist researchers in selecting the most suitable method for a specific research question as well as an interactive. This is the first comprehensive assessment of trajectory inference methods. In the future the team plans to add a detailed parameter tuning procedure. The pipeline and tools for creating trajectories are freely available on dynverse and the team welcomes discussion aimed at further development.