Examples are general emergence of properties of signaling pathways79 such as extended signal duration, threshold behaviors, etc, endodermal growth factor www.selleckchem.com/products/nutlin-3a.html receptor (EGFR) signaling,80-82 and the TNF alpha-mediated NF-kappa B-signaling pathway (NFkB).83,84 Specific pathway models for neuroscience applications are currently rare. Nevertheless, an understanding of the dynamics Inhibitors,research,lifescience,medical of these diseases could help to develop strategies to halt them at. the stage they have reached at detection, or to prevent them entirely.85 Conclusion Despite the great, uncertainties inherent, in functional genomics

techniques, they will be indispensable for future work in drug development and therapy monitoring. However, these techniques must, be accompanied by solid support,

from data analysis. Bioinformatics, and to an increasing degree, systems biology, have key roles in this process. The information that Inhibitors,research,lifescience,medical we can gain about, a biological system (for example a disease process) appears in practice as an experimental observation, and research is restricted to the targeted molecular level and the precision of the experimental techniques in use. It is very likely that, the range of this experimental granularity will increase in the coming years, utilizing heterogeneous techniques that, target a biological question of interest, Inhibitors,research,lifescience,medical at, different, points so that data integration becomes Inhibitors,research,lifescience,medical a. major challenge for future biomedical research. In the case of complex disease conditions it is clear that, such integrated

approaches are required in order to link clinical, genetic, behavioral, and environmental data with diverse types of molecular phenotype information and to identify correlative associations. Such correlations, if found, are the key to identifying biomarkers and processes that, are either causative or indicative of the disease. In order to screen the success of drug treatment, in the individual patient, new generations of tools and research methods will be developed. These Inhibitors,research,lifescience,medical tools will enable us to perform the crucial step from qualitative to quantitative analysis. Systems biology is pointing in this direction. With its close connection of experimental data, generation, predictive data modeling, and subsequent validation it holds the promise of providing computational tools capable of personalized treatment and therapy monitoring in the individual else patient. Selected abbreviations and acronyms AD Alzheimer’s disease ALS amyotrophic lateral sclerosis DRPLA dentatombral-pallidoluysian atrophy GEO gene expression omnibus GO gene ontology GPCR G -protein-coupled receptor HD Huntington ‘s disease PCR polymerase chain reaction PD Parkinson’s disease SAGE serial analysis of gene expression SOP standard operating procedure Notes The authors wish to thank Christoph Wierling for proofreading the manuscript and Sylvia Krobitsch for providing neuroscience literature.