Computational modeling of drug response with applications to neuroscience.
The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.
Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany.
Ralf
R
Herwig
D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008958:Models, Molecular; D009488:Neurosciences / Q000379:methods*
bioinformatics; drug response; dynamic modeling; molecular interaction; network analysis; neurogenomics; systems biology