Network models for molecular kinetics and their initial applications to human health  

作　　者：Gregory R Bowman Xuhui Huang Vijay S Pande 

机构地区：[1]Biophysics Program, Stanford University, Stanford, CA 94305, USA [2]Department of Chemistry, The Hong Kong University of Science and Technology, Kowloon, Hong Kong [3]Department of Bioengineering, Stanford University, Stanford, CA 94305, USA [4]Department of Chemistry, Stanford University, Stanford, CA 94305, USA

出　　处：《Cell Research》2010年第6期622-630,共9页细胞研究（英文版）

摘　　要：Molecular kinetics underlies all biological phenomena and, like many other biological processes, may best be understood in terms of networks. These networks, called Markov state models （MSMs）, are typically built from physical simulations. Thus, they are capable of quantitative prediction of experiments and can also provide an intuition for complex couformational changes. Their primary application has been to protein folding; however, these technologies and the insights they yield are transferable. For example, MSMs have already proved useful in understanding human diseases, such as protein misfolding and aggregation in Alzheimer＇s disease.

关 键 词：Markov state models molecular dynamics simulations protein folding conformational change Alzheimer＇s disease 

分 类 号：Q51[生物学—生物化学] TP18[自动化与计算机技术—控制理论与控制工程]