Systems analysis of small signaling modules relevant to eight human diseases.
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 (PPARα) agonists used to treat dyslipidemia would most effectively stimulate PPARα signaling if drug design were to increase agonist nucleoplasmic concentration, as opposed to increasing agonist binding affinity for PPARα. 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.
Department of Biomedical Engineering, University of Virginia Health System, One Boar’s Head Pointe, Charlottesville, VA 22908, USA.
Kelly F
KF
Benedict
Feilim
F
Mac Gabhann
Robert K
RK
Amanfu
Arvind K
AK
Chavali
Erwin P
EP
Gianchandani
Lydia S
LS
Glaw
Matthew A
MA
Oberhardt
Bryan C
BC
Thorne
Jason H
JH
Yang
Jason A
JA
Papin
Shayn M
SM
Peirce
Jeffrey J
JJ
Saucerman
Thomas C
TC
Skalak
D003198:Computer Simulation; D004194:Disease; D006801:Humans; D047908:Intracellular Signaling Peptides and Proteins / Q000378:metabolism*; D008954:Models, Biological; D015398:Signal Transduction; D013597:Systems Analysis; D049490:Systems Biology / Q000379:methods
D047908:Intracellular Signaling Peptides and Proteins