Predicting altered pathways using extendable scaffolds.
Many diseases, especially solid tumors, involve the disruption or deregulation of cellular processes. Most current work using gene expression and other high-throughput data, simply list a set of differentially expressed genes. We propose a new method, PAPES (predicting altered pathways using extendable scaffolds), to computationally reverse-engineer models of biological systems. We use sets of genes that occur in a known biological pathway to construct component process models. We then compose these models to build larger scale networks that capture interactions among pathways. We show that we can learn process modifications in two coupled metabolic pathways in prostate cancer cells.
Broom
B M
BM
Department of Biostatistics and Applied Mathematics, MD Anderson Cancer Center, 1515 Holcombe Boulevard, Houston, TX 77030, USA. broom@mdanderson.org
McDonnell
T J
TJ
Subramanian
D
D
D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling; D005978:Glutathione / Q000378:metabolism; D006801:Humans; D008297:Male; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis; D018384:Oxidative Stress; D010100:Oxygen / Q000378:metabolism; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology*; D015202:Protein Engineering; D012680:Sensitivity and Specificity; D012984:Software; D014508:Urea / Q000378:metabolism
D014508:Urea; D005978:Glutathione; D010100:Oxygen