Automation Of Particle Detection And Tracking Of Motor Proteins

Abstract

Cells are the basic structural, functional and biological functional unit in living organisms. Cells are usually called “fundamental building blocks of life” due to their ability to replicate independently. The study of cells and cell processes is imperative in the study of living organisms. Particle detection and tracking plays a vital role in understanding the processes that occur in living cells. Biological processes involve complex and dynamic machinery which makes it very difficult to analyze and draw conclusions from the observations. Technological advancements have significantly improved the quality and quantity of data that can be collected: particles with nanometer resolution can now be imaged with intricate details over a significant interval of time, thus providing us access to information about the biological processes at a cellular and molecular level. The extensive use of fluorescent probes sheds light on the different particles and their roles in the various processes. However, there are a lot of factors which affect the processes that are not under our control thereby inhibiting us from successfully detecting and tacking particles. The presence of a plethora of particles which vary in size, nature, density of occurrence, fluorescence, nature of motion etc. makes it impossible to have a unified detection and tracking algorithm that can provide us with the most accurate results. The presence of a wide number of independent parameters some of which are mentioned above makes it hard to simulate a process and hinders the understanding of processes and drawing conclusions from them. This study mainly focuses on summarizing some of the most popular detection and tracking algorithms. Towards the end, the developed detection and tracking algorithm is applied to study bidirectional axonal cargo transport. A video containing the result of the tracking algorithm has been submitted as Supplementary Video 1.