Connecting protein and mRNA burst distributions for stochastic models of gene expression

Physical Biology ◽

10.1088/1478-3975/8/4/046001 ◽

2011 ◽

Vol 8 (4) ◽

pp. 046001 ◽

Author(s):

Vlad Elgart ◽

Tao Jia ◽

Andrew T Fenley ◽

Rahul Kulkarni

Keyword(s):

Gene Expression ◽

Stochastic Models

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Stochastic models for inferring genetic regulation from microarray gene expression data

10.1016/j.biosystems.2009.11.002 ◽

2010 ◽

Vol 99 (3) ◽

pp. 192-200 ◽

Author(s):

Tianhai Tian

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Stochastic Models ◽

Genetic Regulation ◽

Microarray Gene Expression Data ◽

Expression Data ◽

Microarray Gene Expression ◽

Microarray Gene

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Exact distributions for stochastic models of gene expression with arbitrary regulation

Science China Mathematics ◽

10.1007/s11425-019-1622-8 ◽

2019 ◽

Vol 63 (3) ◽

pp. 485-500 ◽

Author(s):

Zihao Wang ◽

Zhenquan Zhang ◽

Tianshou Zhou

Keyword(s):

Gene Expression ◽

Stochastic Models ◽

Exact Distributions

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Time-dependent propagators for stochastic models of gene expression: an analytical method

Journal of Mathematical Biology ◽

10.1007/s00285-017-1196-4 ◽

2017 ◽

Vol 77 (2) ◽

pp. 261-312 ◽

Author(s):

Frits Veerman ◽

Carsten Marr ◽

Nikola Popović

Keyword(s):

Gene Expression ◽

Stochastic Models ◽

Analytical Method ◽

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Inference of Kinetic Parameters of Delayed Stochastic Models of Gene Expression Using a Markov Chain Approximation

EURASIP Journal on Bioinformatics and Systems Biology ◽

10.1155/2011/572876 ◽

2011 ◽

Vol 2011 (1) ◽

pp. 572876 ◽

Author(s):

Henrik Mannerstrom ◽

Olli Yli-Harja ◽

Andre S Ribeiro

Keyword(s):

Gene Expression ◽

Markov Chain ◽

Kinetic Parameters ◽

Stochastic Models ◽

Markov Chain Approximation ◽

Chain Approximation

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Applications of Little’s Law to stochastic models of gene expression

Physical Review E ◽

10.1103/physreve.82.021901 ◽

2010 ◽

Vol 82 (2) ◽

Author(s):

Vlad Elgart ◽

Tao Jia ◽

Rahul V. Kulkarni

Keyword(s):

Gene Expression ◽

Stochastic Models ◽

Little's Law ◽

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Exact protein distributions for stochastic models of gene expression using partitioning of Poisson processes

Physical Review E ◽

10.1103/physreve.87.042720 ◽

2013 ◽

Vol 87 (4) ◽

Author(s):

Hodjat Pendar ◽

Thierry Platini ◽

Rahul V. Kulkarni

Keyword(s):

Gene Expression ◽

Stochastic Models ◽

Poisson Processes

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A new efficient approach to fit stochastic models on the basis of high-throughput experimental data using a model of IRF7 gene expression as case study

BMC Systems Biology ◽

10.1186/s12918-017-0406-4 ◽

2017 ◽

Vol 11 (1) ◽

Author(s):

Luis U. Aguilera ◽

Christoph Zimmer ◽

Ursula Kummer

Keyword(s):

Gene Expression ◽

Experimental Data ◽

High Throughput ◽

Stochastic Models ◽

Efficient Approach

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Anomaly detection in gene expression via stochastic models of gene regulatory networks

BMC Genomics ◽

10.1186/1471-2164-10-s3-s26 ◽

2009 ◽

Vol 10 (Suppl 3) ◽

pp. S26 ◽

Author(s):

Haseong Kim ◽

Erol Gelenbe

Keyword(s):

Gene Expression ◽

Anomaly Detection ◽

Gene Regulatory Networks ◽

Stochastic Models ◽

Regulatory Networks ◽

Gene Regulatory

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Stochastic Models of Gene Expression with Delayed Degradation

Bulletin of Mathematical Biology ◽

10.1007/s11538-010-9622-4 ◽

2011 ◽

Vol 73 (9) ◽

pp. 2231-2247 ◽

Author(s):

Jacek Miȩkisz ◽

Jan Poleszczuk ◽

Marek Bodnar ◽

Urszula Foryś

Keyword(s):

Gene Expression ◽

Stochastic Models

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Spectral solutions to stochastic models of gene expression with bursts and regulation

Physical Review E ◽

10.1103/physreve.80.041921 ◽

2009 ◽

Vol 80 (4) ◽

Author(s):

Andrew Mugler ◽

Aleksandra M. Walczak ◽

Chris H. Wiggins

Keyword(s):

Gene Expression ◽

Stochastic Models

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