Systems Pharmacology-Bioinformatics and Computational Core
The emergence of technologies that can be used to measure on a genome-wide scale protein and RNA levels and their activities within cells brings about the necessity for advanced bioinformatics and computational analyses and data integration methodologies. The Bioinformatics and Computational Core of the ETI provides expertise in these emerging interdisciplinary fields on the usage of databases, statistical analyses tools and model development analyses.
Computational analysis of multi-variable experiments provides the opportunity for rapid identification of novel drug targets and biomarkers. Systems pharmacology is an emerging area that uses computational methods to analyze data from pharmacological studies for the purpose of studying drug action, predicting side effects and discovering new drug targets. One of the most promising approaches used in systems pharmacology is network analysis. Networks can be built from many data sources. The abstraction to nodes and links is a useful step for data integration. To build such networks focused on the action of therapeutics, drugs can be linked to their targets, targets in turn linked to signaling pathways and cellular regulatory networks. Drug interactions can be used to develop networks to link them to disease genes. Other sources of data can be linked with such networks using Gene IDs and drug names as anchors. The core will use publicly available databases and tools developed here and elsewhere to build such networks. This resource will be used to provide customized computational support for specific studies. Such network analysis can be used for placing new drugs in context of already profiled drugs to identify unknown targets and predict potential adverse events.
In addition, the focus in systems pharmacology is to predict disease genes, and biomarker sets. Modeling approaches can be used to understand how potential drugs affect different cells, tissue and the entire organism. The Systems Pharmacology Computational Core will provide data analysis services to assist investigators placing their experimental data in context of other drugs and link results to known regulatory molecular pathways in mammalian cells and systems.
Systems pharmacology can be used to develop biomarker sets that can be used as indicators for drug actions across different tissues, in model organisms and in clinical trials. Biomarkers sets are unique groups of cellular or extracellular components that through network analysis as a set may be able to predict disease origins, progression and treatment outcomes. Results from multi-variable studies are often report uncharacterized genes. These are potential biomarkers and sometimes their products may be novel drug target. Even when network based mechanistic analyses appear premature, machine learning approaches can be used to identify biomarker sets by correlating phenotype to molecular signatures. The core will provide Mount Sinai investigators with an array of tools that can perform such classification tasks.
The SPBCC will provide the following services:
1. Development of cellular networks from disease gene lists and drug target lists.
2. Network Analyses using graph theory approaches.
3. Development and analyses of correlative models using machine learning approaches.