This talk will give examples for two major areas in which mathematics and computer science can contribute to a "Systems" approach to molecular biology. The first part of the talk will focus on the development of new measurement approaches, in particular a novel way to interpret data acquired from mass spectrometry (MS) analysis of very complex samples. Typically, multidimensional liquid chromatography (LC) is used to separate such samples into fractions of lower complexity. Until now, the multidimensional structure among samples has not been used, but we will argue and demonstrate how this structure can be exploited to improve many aspects of the experimental-computational protocol for identifying and quantifying proteins on a large scale.
The second half of the presentation will discuss the integration of heterogeneous data types; especially, a novel approach to find sequence motifs relevant for protein interactions. In addition to sequence information, an experimentally determined large-scale interaction network is used as an additional input. The integration of this new type of data allows the efficient usage of a more complex and realistic model for biologically and medically important families of proteins, and leads to improved predictions of protein interactions.
