Simulated maximum likelihood method for estimating kinetic rates in gene expression
Author(s) -
Tianhai Tian,
Songlin Xu,
Junbin Gao,
Kevin Burrage
Publication year - 2006
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btl552
Subject(s) - parametric statistics , measure (data warehouse) , noise (video) , stochastic process , computer science , stochastic modelling , expression (computer science) , genetic algorithm , biological system , statistical physics , mathematics , mathematical optimization , statistics , biology , physics , data mining , artificial intelligence , image (mathematics) , programming language
Kinetic rate in gene expression is a key measurement of the stability of gene products and gives important information for the reconstruction of genetic regulatory networks. Recent developments in experimental technologies have made it possible to measure the numbers of transcripts and protein molecules in single cells. Although estimation methods based on deterministic models have been proposed aimed at evaluating kinetic rates from experimental observations, these methods cannot tackle noise in gene expression that may arise from discrete processes of gene expression, small numbers of mRNA transcript, fluctuations in the activity of transcriptional factors and variability in the experimental environment.
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