A stochastic transcriptional switch model for single cell imaging data

Inference for reaction networks using the linear noise approximation

(2014) Paul Fearnhead et al.   BIOMETRICS

Scalable inference of heterogeneous reaction kinetics from pooled single-cell recordings

(2014) Christoph Zechner et al.   NATURE METHODS

Delayed acceptance particle MCMC for exact inference in stochastic kinetic models

(2014) Andrew Golightly et al.   STATISTICS AND COMPUTING

Quantifying intrinsic and extrinsic noise in gene transcription using the linear noise approximation: An application to single cell data

(2013) Bärbel Finkenstädt et al.   Annals of Applied Statistics

A temporal switch model for estimating transcriptional activity in gene expression

(2013) Dafyd J. Jenkins et al.   BIOINFORMATICS

Stochastic models of transcription: From single molecules to single cells

(2013) Alvaro Sanchez et al.   METHODS

Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation

(2013) V. Stathopoulos et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

Accelerated maximum likelihood parameter estimation for stochastic biochemical systems

(2012) Bernie J Daigle et al.   BMC BIOINFORMATICS

The slow-scale linear noise approximation: an accurate, reduced stochastic description of biochemical networks under timescale separation conditions

(2012) Philipp Thomas et al.   BMC Systems Biology

Linear noise approximation is valid over limited times for any chemical system that is sufficiently large

(2012) E.W.J. Wallace et al.   IET Systems Biology

The Prolactin Gene: A Paradigm of Tissue-Specific Gene Regulation with Complex Temporal Transcription Dynamics

(2012) K. Featherstone et al.   JOURNAL OF NEUROENDOCRINOLOGY

Inference for discretely observed stochastic kinetic networks with applications to epidemic modeling

(2011) Boseung Choi et al.   BIOSTATISTICS

Pulsatile patterns of pituitary hormone gene expression change during development

(2011) K. Featherstone et al.   JOURNAL OF CELL SCIENCE

Information transmission in genetic regulatory networks: a review

(2011) Gašper Tkačik et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Mammalian Genes Are Transcribed with Widely Different Bursting Kinetics

(2011) D. M. Suter et al.   SCIENCE

Rate estimation in partially observed Markov jump processes with measurement errors

(2011) Michael Amrein et al.   STATISTICS AND COMPUTING

Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo

(2011) A. Golightly et al.   Interface Focus

Dynamic Analysis of Stochastic Transcription Cycles

(2011) Claire V. Harper et al.   PLOS BIOLOGY

Learning combinatorial transcriptional dynamics from gene expression data

(2010) Manfred Opper et al.   BIOINFORMATICS

Dynamic organisation of prolactin gene expression in living pituitary tissue

(2010) C. V. Harper et al.   JOURNAL OF CELL SCIENCE

Particle Markov chain Monte Carlo methods

(2010) Christophe Andrieu et al.   JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY

Measurement of single-cell dynamics

(2010) David G. Spiller et al.   NATURE

Switching regulatory models of cellular stress response

(2009) Guido Sanguinetti et al.   BIOINFORMATICS

Bayesian inference of biochemical kinetic parameters using the linear noise approximation

(2009) Michał Komorowski et al.   BMC BIOINFORMATICS

Real-Time Visualization of Human Prolactin Alternate Promoter Usagein VivoUsing a Double-Transgenic Rat Model

(2009) Sabrina Semprini et al.   MOLECULAR ENDOCRINOLOGY

Efficient Statistical Inference for Stochastic Reaction Processes

(2009) Andreas Ruttor et al.   PHYSICAL REVIEW LETTERS

A single molecule view of gene expression

(2009) Daniel R. Larson et al.   TRENDS IN CELL BIOLOGY

Reconstruction of transcriptional dynamics from gene reporter data using differential equations

(2008) Bärbel Finkenstädt et al.   BIOINFORMATICS

Nature, Nurture, or Chance: Stochastic Gene Expression and Its Consequences

(2008) Arjun Raj et al.   CELL