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Cryogenic procedures are fundamental tools in modern biology, e.g. for conservation or purification of biological materials. The processes occurring in biological cells and tissues during freezing and thawing are subject to ongoing research. Optimization of cell survival rates demands the development and evaluation of exactly defined temperature profiles. 4D-DMD-microscopy is capable of imaging these highly dynamic processes with high spatial and temporal resolution, utilizing well established staining procedures for differentiating structures of interest.

By two-photon time-resolved confocal 4D-microscopy it is possible to image fluorescent objects at a high spatial and temporal resolution. The usage of femtosecond-laser light creates a two photon effect and therefore reduces bleaching of the fluorophore. With this technique 4D-visualization of dynamic processes in living cells is possible.

Digital Micromirror Device based microscopy combines fast confocal 4D-microscopy along with conventional methods for light microscopy and new technological approaches to a versatile tool for the observation of in vivo processes in living biological cells and measurement of technical surfaces. Due to the use of variable size pinholes and adjustable scan patterns conditions for confocal measurement can easily be optimized to the prerequisites of the sample "on the fly".

Time resolved 3D-microscopy using DMD-arrays utilizes the principles of confocal microscopy. Application fitted patterns optimize optical imaging of reflective, transparent, and fluorescent objects. High spatial resolution is achieved simultaneously with high temporal resolution due to fast DMD control. This enables to visualize and track processes in vivo within living biocells.

Motivation: The use of gene microchips has enabled a rapid accumulation of gene-expression data. One of the major challenges of analyzing this data is the diversity, in both size and signal strength, of the various modules in the gene regulatory networks of organisms. Results: Based on the iterative signature algorithm [Bergmann,S., Ihmels,J. and Barkai,N. (2002) Phys. Rev. E 67, 031902], we present an algorithm—the progressive iterative signature algorithm (PISA)—that, by sequentially eliminating modules, allows unsupervised identification of both large and small regulatory modules. We applied PISA to a large set of yeast gene-expression data, and, using the Gene Ontology database as a reference, found that the algorithm is much better able to identify regulatory modules than methods based on high-throughput transcription-factor binding experiments or on comparative genomics. Contact: tang@nec-labs.com