Construction of Multi-level Networks Incorporating Molecule, Cell, Organ and Phenotype Properties for Drug-induced Phenotype Prediction

Abstract

Inferring drug-induced phenotypes via computational approaches can give a substantial support to drug discovery procedure. However, existing computational models that are mainly based on a single cell or a single organ model are thought to be limited because the phenotypes are consequences of stochastic biochemical processes among distant cells/organs as well as molecules confined in one cell. Therefore, there is an urgent demand for a new computational model that represents heterogeneous biochemical interactions spanning the entire human body. To meet the demand, we constructed multi-level networks that incorporate previously uncovered high-level properties such as molecules, cells, organs, and phenotypes. Currently, the networks consist of 1,776,506 edges including molecular networks within 76 pre-defined cell-types, inter-cell interactions among the cell-types, and gene (protein) relations to 429 phenotypes. We are also planning to verify if known drug-induced phenotypes are reproducible in the networks using a Petri-net based simulation.