title: Network models for molecular kinetics and their initial applications to human health.
abstract: 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 conformational 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.
authors.affiliation: Biophysics Program, Stanford University, Stanford, CA 94305, USA.
authors.forename: Gregory R
authors.initials: GR
authors.lastname: Bowman
authors.forename: Xuhui
authors.initials: X
authors.lastname: Huang
authors.forename: Vijay S
authors.initials: VS
authors.lastname: Pande
mesh_terms: D000818:Animals; D006304:Health Status; D006801:Humans; D007700:Kinetics; D008958:Models, Molecular; D016571:Neural Networks, Computer; D040901:Proteomics / Q000379:methods* / Q000639:trends