Next-Generation Bioimaging Systems

The question I would like to help answer is: What is the role and what can imaging do for systems biology? In recent years, the focus in biological sciences has shifted from understanding single parts of larger systems, sort of vertical approach, to understanding complex systems at the cellular and molecular levels, horizontal approach. Thus the revolution of "omics" projects, genomics and now proteomics. Understanding complexity of biological systems is a task that requires acquisition, analysis and sharing of huge databases, and in particular, high-dimensional databases. For example, in the current project on location proteomics, the fluorescence microscopy data sets can have a dimension as high as 5: two spatial dimensions, z-stacks, time series and different-color channels (different color probes for different proteins). Processing such huge amount of bioimages visually by biologists is inefficient, time-consuming and error-prone. Therefore, we would like to move towards automated, efficient and robust processing of such bioimage data sets. Moreover, some information hidden in the images may not be readily visually available. For example, in the same project, we use images of two proteins residing in the Golgi apparatus-giantin and gpp130. These two proteins cannot be distinguished better than randomly by humans, while when employing data mining methods, they can be told apart. Therefore, we do not only replace humans by machines for faster and more efficient processing but also because new knowledge is generated through use of sophisticated algorithms. The ultimate dream is to have distributed yet integrated large bioimage databases which would allow researchers to upload their data, have it processed, share the data, download data as well as platform-optimized code, etc., and all this in a common format, something akin to the DICOM format for clinical imaging. To achieve this goal, we must draw upon a whole host of sophisticated tools from signal processing, machine learning and scientific computing. While such tools are widely present in clinical (medical) imaging, they are not as widespread in imaging of biological systems at cellular and molecular levels. This is a huge challenge and requires integration of interdisciplinary teams. I will address some of these issues in this presentation