Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.
about
Markov State Models of gene regulatory networksFinite state projection based bounds to compare chemical master equation models using single-cell data.Generalized method of moments for estimating parameters of stochastic reaction networks.Transcriptional bursting in Drosophila development: Stochastic dynamics of eve stripe 2 expression.BayFish: Bayesian inference of transcription dynamics from population snapshots of single-molecule RNA FISH in single cells.Minimal moment equations for stochastic models of biochemical reaction networks with partially finite state space.An adaptive solution to the chemical master equation using tensors.Stochastic system identification without an a priori chosen kinetic model-exploring feasible cell regulation with piecewise linear functions.Identification of Gene regulation models from single-cell data.Single-cell mRNA profiling reveals the hierarchical response of miRNA targets to miRNA induction
P2860
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P2860
Integrating single-molecule experiments and discrete stochastic models to understand heterogeneous gene transcription dynamics.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Integrating single-molecule ex ...... s gene transcription dynamics.
@ast
Integrating single-molecule ex ...... s gene transcription dynamics.
@en
type
label
Integrating single-molecule ex ...... s gene transcription dynamics.
@ast
Integrating single-molecule ex ...... s gene transcription dynamics.
@en
prefLabel
Integrating single-molecule ex ...... s gene transcription dynamics.
@ast
Integrating single-molecule ex ...... s gene transcription dynamics.
@en
P2860
P1433
P1476
Integrating single-molecule ex ...... s gene transcription dynamics.
@en
P2093
Gregor Neuert
Zachary Fox
P2860
P356
10.1016/J.YMETH.2015.06.009
P50
P577
2015-06-12T00:00:00Z