Multi-level quantitative bio-network modeling for cocaine addiction

Abstract

Cocaine addiction is a serious problem personally and socially. Many studies on cocaine addiction have been done biologically. The model containing the knowledge from the studies is needed. There have been some models. But they are too narrow bio molecular models not considering addiction or non-bio molecular model which are Neurocomputational model, pharmacological-based model, economical-based model, and combining model of Neuropsychology, cognition and behavior not having quantitative and bio molecular value. In this work, we construct a computational model of which input is quantitative mRNA expression data and output is the vulnerability to the cocaine addiction. We gather knowledge about cocaine addiction within nucleus accumbens (NAc) and construct a computational model. We validated the model with the age-associated mRNA expression data and the age-associated vulnerability to the cocaine addiction. We also used the model to suggest the effect of genes suppression on the vulnerability to the cocaine, and validated that with some literatures in which suppression experiment was done. Based on the suggestion the effect, we can suggest putative target genes for cocaine addiction treatment.