The development of drugs against pathogens that cause hemorrhagic fever, such as Marburg and Ebola virus, requires researchers to gather much information about the virus. The accelerating of the research process is of great interest; therefore a new algorithm was developed to analyze intracellular processes. The algorithm will classify the motion characteristics of subviral particles in fluorescence microscopic image sequences of Ebola or Marburg virusinfected cells. The classification is based on the calculation of mean squared displacement. The results look promising to distinguish different particle tracks in active and passive transport. The paper ends with a discussion.

The development of a drug against pathogens of hemorrhagic fever, like the Marburg virus, is a great challenge. Therefore, accurate knowledge of the properties of subviral particles in the host cell must be obtained. The base for subviral particle analysis is a special fluorescence microscopy technique. In order to automate and visualize the subviral particles’ motion patterns, previously a tracking algorithm was developed. In this article a new algorithm to parameterize and visualize the achieved particle tracks is introduced. A good potential for a fast data recognition is shown, with constantly respecting a high usability for pharmaceutical researchers. This algorithm was tested on both simulated and real data and provides reproducible results.