Systems biology as a new research paradigm Systems biology aims at the explanation of physiology and disease from the level of interacting components such as molecular pathways, regulatory networks,

cells, organs, and ultimately the entire organism.77 With the use of computer models for such processes in silico predictions can be generated on the state of the disease or the effect, of the individual Inhibitors,research,lifescience,medical therapy The new approaches are about, to revolutionize our knowledge of disease mechanisms and of the interpretation of data from high-throughput technologies.1 These approaches are necessary, considering the increasing complexity of research. Often, several laboratories Inhibitors,research,lifescience,medical are working with different, techniques on the same problem. A fundamental challenge is thus to search through the exhaustive set of data and extract meaningful information. Here, in silico experiments can be the basis for a more successful drug screening. Furthermore, there is a fundamental need for integration rules and methods. Multiple databases exist, a variety of experimental techniques have produced gene and proteome expression data from various tissues and samples, and important disease-relevant pathways have been investigated. Information on promoter regions and transcription factors is available for

many genes as well as sequence Inhibitors,research,lifescience,medical information. This information – although extremely helpful – cannot be utilized in a sufficient way because of the lack of integrative analysis tools. A fundamental aim of systems Inhibitors,research,lifescience,medical biology is the understanding of the underlying biological processes on the basis of this data. Crucial for the step from qualitative, explorative data analysis to quantitative, predictive analysis is the combination Inhibitors,research,lifescience,medical of experimental data with the knowledge of the underlying biological reaction system. This approach makes it. possible to come up with conclusions about, the properties

of the system, even those that, are not, subject, to experiments or are not. even amenable by any experimental approach. For this purpose we have developed the modeling and simulation system PyBioS.78 With this system it. is possible to construct, models that, are based on the topology of a cellular reaction network and adequate reaction kinetics. Based enough on this information the system can automatically construct a mathematical model of differential equations that can be used for subsequent, simulation of the temporal behavior and model analysis. Particularly information on the topology of biological systems is available from several databases (eg, KEGG). PyBioS provides interfaces to these databases that can be used for the construction of appropriate model prototypes. Models include metabolic pathways, signal transduction pathways, selleckchem transport processes, gene regulatory networks, among others, and can be accessed via a Web interface.