Herwig Ralf
Max Planck Institute for Molecular Genetics, Department Vertebrate Genomics, Berlin, Germany.
Dialogues Clin Neurosci. 2014 Dec;16(4):465-77. doi: 10.31887/DCNS.2014.16.4/rherwig.
The development of novel high-throughput technologies has opened up the opportunity to deeply characterize patient tissues at various molecular levels and has given rise to a paradigm shift in medicine towards personalized therapies. Computational analysis plays a pivotal role in integrating the various genome data and understanding the cellular response to a drug. Based on that data, molecular models can be constructed that incorporate the known downstream effects of drug-targeted receptor molecules and that predict optimal therapy decisions. In this article, we describe the different steps in the conceptual framework of computational modeling. We review resources that hold information on molecular pathways that build the basis for constructing the model interaction maps, highlight network analysis concepts that have been helpful in identifying predictive disease patterns, and introduce the basic concepts of kinetic modeling. Finally, we illustrate this framework with selected studies related to the modeling of important target pathways affected by drugs.
新型高通量技术的发展为在各种分子水平上深入表征患者组织提供了机会,并引发了医学向个性化治疗的范式转变。计算分析在整合各种基因组数据和理解细胞对药物的反应方面起着关键作用。基于这些数据,可以构建分子模型,该模型纳入了药物靶向受体分子的已知下游效应,并预测最佳治疗决策。在本文中,我们描述了计算建模概念框架中的不同步骤。我们回顾了拥有构建模型相互作用图基础的分子途径信息的资源,强调了有助于识别预测性疾病模式的网络分析概念,并介绍了动力学建模的基本概念。最后,我们通过与受药物影响的重要靶标途径建模相关的选定研究来说明这个框架。