Systems Analysis of Small Signaling Modules Relevant to Eight Human Diseases

作者：Benedict Kelly F; Mac Gabhann Feilim; Amanfu Robert K; Chavali Arvind K; Gianchandani Erwin P; Glaw Lydia S; Oberhardt Matthew A; Thorne Bryan C; Yang Jason H; Papin Jason A; Peirce Shayn M; Saucerman Jeffrey J; Skalak Thomas C^*

来源：Annals of Biomedical Engineering, 2011, 39(2): 621-635.

DOI：10.1007/s10439-010-0208-y

• Systems biology
• Human disease
• Protein signaling
• Comparative meta-analysis
• Sensitivity analysis

Using eight newly generated models relevant to addiction, Alzheimer's disease, cancer, diabetes, HIV, heart disease, malaria, and tuberculosis, we show that systems analysis of small (4-25 species), bounded protein signaling modules rapidly generates new quantitative knowledge from published experimental research. For example, our models show that tumor sclerosis complex (TSC) inhibitors may be more effective than the rapamycin (mTOR) inhibitors currently used to treat cancer, that HIV infection could be more effectively blocked by increasing production of the human innate immune response protein APOBEC3G, rather than targeting HIV's viral infectivity factor (Vif), and how peroxisome proliferator-activated receptor alpha (PPAR alpha) agonists used to treat dyslipidemia would most effectively stimulate PPARa signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARa. Comparative analysis of system-level properties for all eight modules showed that a significantly higher proportion of concentration parameters fall in the top 15th percentile sensitivity ranking than binding affinity parameters. In infectious disease modules, host networks were significantly more sensitive to virulence factor concentration parameters compared to all other concentration parameters. This work supports the future use of this approach for informing the next generation of experimental roadmaps for known diseases.

• 出版日期2011-2