结合分子、细胞、器官和表型特性的多层次网络构建用于药物诱导表型预测

J. Jung, Hasun Yu, Seyeol Yoon, Mijin Kwon, Sungji Choo, Sangwoo Kim, Doheon Lee
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引用次数: 0

摘要

通过计算方法推断药物诱导的表型可以为药物发现过程提供实质性的支持。然而,现有的主要基于单个细胞或单个器官模型的计算模型被认为是有限的,因为表型是远距离细胞/器官之间以及限制在一个细胞中的分子的随机生化过程的结果。因此,迫切需要一种新的计算模型来代表跨越整个人体的异质生化相互作用。为了满足需求,我们构建了包含先前发现的高级特性(如分子、细胞、器官和表型)的多层次网络。目前,该网络由1,776,506条边组成,包括76种预定义细胞类型内的分子网络,细胞类型之间的细胞间相互作用以及与429种表型的基因(蛋白质)关系。我们还计划使用基于Petri-net的模拟来验证已知的药物诱导表型在网络中是否可重复。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Construction of Multi-level Networks Incorporating Molecule, Cell, Organ and Phenotype Properties for Drug-induced Phenotype Prediction
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.
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