Publication 2006 Issue No. 1 - January-March Abstract - A Hidden Markov Model for Transcriptional Regulation in Single Cells
A Hidden Markov Model for Transcriptional Regulation in Single Cells
January-March 2006 (vol. 3 no. 1)
pp. 57-71
 ASCII Text x John Goutsias, "A Hidden Markov Model for Transcriptional Regulation in Single Cells," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 1, pp. 57-71, January-March, 2006.
 BibTex x @article{ 10.1109/TCBB.2006.2,author = {John Goutsias},title = {A Hidden Markov Model for Transcriptional Regulation in Single Cells},journal ={IEEE/ACM Transactions on Computational Biology and Bioinformatics},volume = {3},number = {1},issn = {1545-5963},year = {2006},pages = {57-71},doi = {http://doi.ieeecomputersociety.org/10.1109/TCBB.2006.2},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE/ACM Transactions on Computational Biology and BioinformaticsTI - A Hidden Markov Model for Transcriptional Regulation in Single CellsIS - 1SN - 1545-5963SP57EP71EPD - 57-71A1 - John Goutsias, PY - 2006KW - Hidden Markov modelsKW - Monte Carlo simulationKW - stochastic biochemical systemsKW - stochastic dynamical systemsKW - transcriptional regulationKW - transcriptional regulatory systems.VL - 3JA - IEEE/ACM Transactions on Computational Biology and BioinformaticsER -
We discuss several issues pertaining to the use of stochastic biochemical systems for modeling transcriptional regulation in single cells. By appropriately choosing the system state, we can model transcriptional regulation by a hidden Markov model (HMM). This opens the possibility of using well-known techniques for the statistical analysis and stochastic control of HMMs to mathematically and computationally study transcriptional regulation in single cells. Unfortunately, in all but a few simple cases, analytical characterization of the statistical behavior of the proposed HMM is not possible. Moreover, analysis by Monte Carlo simulation is computationally cumbersome. We discuss several techniques for approximating the HMM by one that is more tractable. We employ simulations, based on a biologically relevant transcriptional regulatory system, to show the relative merits and limitations of various approximation techniques and provide general guidelines for their use.

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Index Terms:
Hidden Markov models, Monte Carlo simulation, stochastic biochemical systems, stochastic dynamical systems, transcriptional regulation, transcriptional regulatory systems.
Citation:
John Goutsias, "A Hidden Markov Model for Transcriptional Regulation in Single Cells," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 3, no. 1, pp. 57-71, Jan.-March 2006, doi:10.1109/TCBB.2006.2