Identifying Q-Matrix of Cyclic Markov Chain

Xu Yan XIANG, Xiang Qun YANG, Ying Chun DENG

Acta Mathematica Sinica, Chinese Series ›› 2013, Vol. 56 ›› Issue (5) : 735-750.

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Acta Mathematica Sinica, Chinese Series ›› 2013, Vol. 56 ›› Issue (5) : 735-750. DOI: 10.12386/A2013sxxb0073
Articles

Identifying Q-Matrix of Cyclic Markov Chain

  • Xu Yan XIANG1, Xiang Qun YANG2, Ying Chun DENG2
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Abstract

The sojourn time and hitting time distributions (the mixtures of exponential distributions) are provided for a given subset of state space of Markov chain. Then the derivatives of these distributions at t=0 are related to the Q-matrix. Based on the constraint relationships and the priori information from the structure of Markov chain, an inversion approach is developed to identify the transition rates from the parameters characterizing these distributions. For cyclic Markov chain with finite states, as a result, it is derived that its Q-matrix can be uniquely determined by the distributions of their sojourn time and hitting time at arbitrary two adjacent states. The corresponding algorithm is included to show the validity of such approach.

Key words

Markov chain / cyclic scheme / reversibility / transition rate / sojourn time / hitting time

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Xu Yan XIANG, Xiang Qun YANG, Ying Chun DENG. Identifying Q-Matrix of Cyclic Markov Chain. Acta Mathematica Sinica, Chinese Series, 2013, 56(5): 735-750 https://doi.org/10.12386/A2013sxxb0073

References

[1] Anderson W. J., McDunnough P., On the representation of symmetric transition functions, Adv. Appl. Prob., 1990, 22: 548-563.

[2] Ball F. G., Sabsom S. M., Single-channel autocorrelation functions: the effects of time interval omission, Adv. Appl. Prob., 1991, 23: 772-797.

[3] Blatz A. L., Magleby K. L., Correcting single channel data for missed events, Biophys. J., 1986, 49: 967-980.

[4] Blunck R., Cordero-Morales J. F., Cuello L. G., et al., Detection of the opening of the bundle crossing in KcsA with fl uorescence lifetime spectroscopy reveals the existence of two gates for ion conduction, J. Gen. Physiol., 2006, 128: 569-581.

[5] Chakrapani S., Cordero-Morales J. F., Perozo E., A quantitative description of KcsA gating II—Singlechannel currents, J. Gen. Physiol., 2007, 130(5): 479-496.

[6] Colquhoun D., Hawkes A. G., Relaxation and fluctuations of membrane currents that flow through drugoperated ion channels, Proc. R. Soc. Lond. B, 1977, 199: 231-262.

[7] Colquhoun D., Hawkes A. G., On the stochastic properties of single ion channels, Proc. R. Soc. Lond. B, 1981, 211: 205-235.

[8] Colquhoun D., Hawkes A. G., On the stochastic properties of bursts of single ion channels opening and of clusters of brusts, Philos. Trans. R. Soc. Lond. Biol. Sci., 1982, 300: 1-59.

[9] Colquhoun D., Hatton C. J., Hawkes A. G., The quality of maximum likelihood estimates of ion channel rate constants, J. Gen. Physiol., 2003, 547(3): 699-728.

[10] Colquhoun D., Sigworth F., Fitting and Statistical Analysis of Single Channel Records, in Single-Channel Recording, B. Sakmann and E. Neher, Editors, Plenum Publishing Corp., New York, 1995: 483-587.

[11] Deng Y. C., Peng S. L., Qian M. P.,et al., Identifying transition rates of ionic channel via observation of a single state, J. Phys. A: Math. & Gen., 2003, 36: 1195-1212.

[12] Doyle D. A., Morais Cabral J., Pfuetzner R. A., et al., The structure of the potassium channel: molecular basis of K+ conduction and selectivity, Science, 1998, 280: 69-77.

[13] French R. J., Wonderlin W. F., Software for acquisition and analysis of ion channel data: choices, tasks, and strategies, Methods Enzymol, 1992, 207: 711-728.

[14] Horn R., Lang K., Estimating kinetic constants from single channel data, Biophys., 1983, 43: 207-230.

[15] Jackson M. B., Stationary single channel analysis, Methods Enzymol, 1992, 207: 729-746.

[16] Jewell N. P., Mixtures of exponential distributions, The Annals of Statistics, 1982, 10: 479-484.

[17] Keatinge C. L., Modelling losses with the mixed exponential distribution, Proceeding of the Causality Actuarial Society, 1999, 86: 654-698.

[18] Purohit P., Mitra A., Auerbach A., A stepwise mechanism for acetylcholine receptor channel gating, Nature, 2007, 446: 930-933

[19] Sakemann B., Neher E., Single-Channel Recording, Press, New York, London, 1982.

[20] Shelley C., Magleby K. L., Linking exponential components to kinetic states in markov models for singlechannel gating, J. Gen. Physiol., 2008, 132(2): 295-312.

[21] Tombola F., Pathak M. M., Isacoff E. Y., How does voltage open an ion channel? Annu. Rev. Cell Dev. Biol., 2006, 22: 23-52.

[22] Wagner M., Michalek S., Timmer J., Estimating transition rates in aggregated Markov models of ion channel gating with loops and with nearly equal dwell times, Proc. R. Soc. Lond. Biol. Sci., 1999, 266: 1919-1926.

[23] Xiang X. Y., Identifying the Q-Matrix of Markov Models about Ion Channels and Learning about Biological Neural Networks, Hunan Normal University, Changsha, 2007 (in Chinese).

[24] Xiang X. Y., Deng Y. C., Yang X. Q., Observation and statistics of hierarchical Markov chain, Appl. Math. J. Chinese Univ. Ser. A, 2006, 21: 301-310 (in Chinese).

[25] Xiang X. Y., Yang X. Q., Deng Y. C., Identifying transition rates for a type of gating schemes of ion channels with loops (in Chinese), Appl. Math. J. Chinese Univ. Ser. A, 2009, 24: 146-154.

[26] Xiang X. Y., Yang X. Q., Deng Y. C., Identifying transition rates of ion channels underlying with star-graph branch type Markov chain, Appl. Math. J. Chinese Univ. Ser. A, 2010, 25: 13-26 (in Chinese).

[27] Xiang X. Y., Yang X. Q., Deng Y. C., Markov chain inversion approach to identify the transition rates of ion channels, Acta Mathematica Scientia, 2012, 32B(5): 1703-1718.

[28] Xiang X. Y., Yang X. Q., Deng Y. C., Feng J. F., Identifying transition rates of ionic channel of star-graph branch type, J. Phys. A: Math. and Gen., 2006, 39: 9477-9491.
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