ArticleBMC bioinformatics2022
Statistical inference for a quasi birth-death model of RNA transcription.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
backgroundA birth-death process of which the births follow a hypoexponential distribution with L phases and are controlled by an on/off mechanism, is a population process which we call the on/off-seq-L process. It is a suitable model for the dynamics of a population of RNA molecules in a single living cell. Motivated by this biological application, our aim is to develop a statistical method to estimate the model parameters of the on/off-seq-L process, based on observations of the population size at discrete time points, and to apply this method to real RNA data.
methodsIt is shown that the on/off-seq-L process can be seen as a quasi birth-death process, and an Erlangization technique can be used to approximate the corresponding likelihood function. An extensive simulation-based numerical study is carried out to investigate the performance of the resulting estimation method. RESULTS AND
conclusionA statistical method is presented to find maximum likelihood estimates of the model parameters for the on/off-seq-L process. Numerical complications related to the likelihood maximization are identified and analyzed, and solutions are presented. The proposed estimation method is a highly accurate method to find the parameter estimates. Based on real RNA data, the on/off-seq-3 process emerges as the best model to describe RNA transcription.
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