Bridging the gaps in systems biology.

Systems biology aims at creating mathematical models, i.e., computational reconstructions of biological systems and processes that will result in a new level of understanding-the elucidation of the basic and presumably conserved "design" and "engineering" principles of biomolecular systems. Thus, systems biology will move biology from a phenomenological to a predictive science. Mathematical modeling of biological networks and processes has already greatly improved our understanding of many cellular processes. However, given the massive amount of qualitative and quantitative data currently produced and number of burning questions in health care and biotechnology needed to be solved is still in its early phases. The field requires novel approaches for abstraction, for modeling bioprocesses that follow different biochemical and biophysical rules, and for combining different modules into larger models that still allow realistic simulation with the computational power available today. We have identified and discussed currently most prominent problems in systems biology: (1) how to bridge different scales of modeling abstraction, (2) how to bridge the gap between topological and mechanistic modeling, and (3) how to bridge the wet and dry laboratory gap. The future success of systems biology largely depends on bridging the recognized gaps.

Image analysis in nonlinear microscopy.

The ability to automatically extract quantitative data from nonlinear microscopy images is here explored, taking nonlinear and coherent effects into account. Objects of different degrees of complexity were investigated: theoretical images of spherical objects, experimentally collected coherent anti-Stokes Raman scattering images of polystyrene spheres in background-generating agar, well-separated lipid droplets in living yeast cells, and conglomerations of lipid droplets in living C. elegans nematodes. The in linear microscopy useful measure of full width at half-maximum (FWHM) was shown to provide inadequate measures of object size due to the nonlinear density dependence of the signal. Instead, the capability of four state-of-the-art image analysis algorithms was evaluated. Among these, local thresholding was found to be the widest applicable segmentation algorithm.