A selective view of stochastic inference and mod-eling problems in nanoscale biophysics  

作　　者：KOU S.C. 

机构地区：[1]Department of Statistics,Harvard University,Cambridge,MA 02138,USA

出　　处：《Science China Mathematics》2009年第6期1181-1211,共31页中国科学：数学（英文版）

基　　金：supported by the United States National Science Fundation Career Award (Grant No. DMS-0449204)

摘　　要：Advances in nanotechnology enable scientists for the first time to study biological pro-cesses on a nanoscale molecule-by-molecule basis.They also raise challenges and opportunities for statisticians and applied probabilists.To exemplify the stochastic inference and modeling problems in the field,this paper discusses a few selected cases,ranging from likelihood inference,Bayesian data augmentation,and semi-and non-parametric inference of nanometric biochemical systems to the uti-lization of stochastic integro-differential equations and stochastic networks to model single-molecule biophysical processes.We discuss the statistical and probabilistic issues as well as the biophysical motivation and physical meaning behind the problems,emphasizing the analysis and modeling of real experimental data.Advances in nanotechnology enable scientists for the first time to study biological pro-cesses on a nanoscale molecule-by-molecule basis.They also raise challenges and opportunities for statisticians and applied probabilists.To exemplify the stochastic inference and modeling problems in the field,this paper discusses a few selected cases,ranging from likelihood inference,Bayesian data augmentation,and semi-and non-parametric inference of nanometric biochemical systems to the uti-lization of stochastic integro-differential equations and stochastic networks to model single-molecule biophysical processes.We discuss the statistical and probabilistic issues as well as the biophysical motivation and physical meaning behind the problems,emphasizing the analysis and modeling of real experimental data.

关 键 词：likelihood analysis Bayesian data augmentation semi-and NON-PARAMETRIC INFERENCE SINGLE-MOLECULE experiment SUBDIFFUSION generalized LANGEVIN equation fractional BROWNIAN motion stochastic network enzymatic reaction 

分 类 号：O213[理学—概率论与数理统计]