Bayesian analysis of single‐molecule experimental data

By Samuel C. Kou, X. Sunney Xie, Jun S. Liu. 2005

85 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/2eb1ce1c3eca9be6ad741d67d1745f2f77fee6f6

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Citations in the corpus

Particle filters for partially observed diffusions (2007, methodology): “ν. Such models are popular in insurance (Dassios and Jang, 2005) and ﬁnance (Engel, 2000; Duﬃe and Singleton, 1999), and they have recently been used to analyse data from single molecule experiments (Kou et al., 2005).There is a signiﬁcant diﬀerence between this observation regime and the two previo”
Stochastic modeling in nanoscale biophysics: Subdiffusion within proteins (2008, background): “Many new discoveries [see, e.g., Asbury, Fehr and Block (2003), English et al. (2006), Kou et al. (2005), Lu, Xun and Xie (1998), Zhuang et al. (2002)] have emerged from the nanoscale single-molecule studies.”
Statistical inference for dynamical systems: A review (2012, background): “Let us mention that very often both dynamical noise and observational noise arise in real data examples, especially in biology and ecology [15, 35, 91, 96, 107, 164, 173]; nonetheless, as the other settings arise in some physical models, we discuss each of the logical possibilities for the noise str”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, methodology): “For drawing statistical inference regarding a kinetic model, the Bayesian approach has gained increasing popularity in recent years [453, 454, 455, 456, 457, 458].”
Nonparametric estimation of diffusions: a differential equations approach (2012, background): “This arrises for example in single molecule experiments, see for example Kou et al. (2005), where a flexible model for the drift is important since it allows the identification of metastable states for the molecule; for details and further references in the context of FRET experiments see Noé & Wu…”
Network Inference and Biological Dynamics (2011, background): “The cellular dynamics that underlie network inference are subject to stochastic eﬀects [Elowitz, Levine and Siggia (2002); Kou, Xie and Liu (2005); McAdams and Arkin (1997); Paulsson (2005); Swain, Elowitz and Siggia (2002)].”