A. Albertsen and U.-P. Hansen, \Estimation of kinetic rate constants from multi-channel recordings by a direct t of the time series," Biophys. Jour., vol. 67, pp. 1393-1403, 1994.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... models (HMMs) have recently found application to the analysis of single channel recordings, both for the construction of an idealized quantal signal from a noisy recording [4] 9] and for estimation of kinetic parameters directly from the recording rather than from an idealized reconstruction [2], 10] 19] 17] HMMs have also been used in a variety of other areas, for example, in speech recognition [18] and gene finding [14] A key to their computational effectiveness is that the number of operations required to evaluate the likelihood or its gradient or to evaluate posterior ....

....clamp recordings, we believe that our methods will be especially effective in evaluating the likelihood of superpositions of independent channels. Such superpositions produce a very high dimensional state space that has hindered the successful application of otherwise promising HMM techniques [2]. Our code is written in C to be driven by Matlab, and we will be pleased to share it with anyone who is interested. ....

A. Albertson and U.-P. Hansen, "Estimation of kinetic rate constants from multi-channel recordings by a direct fit of the time series," Biophys. J., vol. 67, pp. 1393--1403, 1994.

.... (1998) The concept of discretely sampled continuous time HMM is discussed in Michalek and Timmer (1999) Parameter estimation and gating scheme identification with unfiltered filtered HMM Parameters in unfiltered HMM can be obtained by maximum likelihood estimators (Fredkin and Rice, 1992; Albertsen and Hansen, 1994; Qin et al. 1997; Michalek and Timmer, 1999) For filtered HMM, the approximate likelihood estimator has been shown to be unbiased in computer simulations of the same type as the present ion channel recordings (Michalek et al. 1998) Comparison between different gating schemes incorporating ....

Albertsen, A. and Hansen, U.-P. (1994). Estimation of kinetic rate constants from multi-channel recordings by a direct fit of the time series. Biophys. J., 67, 1393-- 1403.

....21 = q 23 = 0:025. The closing rates are q 12 = 0:05, q 32 = 0:5. The length of a sampling interval is chosen to be 1. We prefer dealing with rate constants, since the topology of the continuous time transition graph cannot easily be described using transition probabilities for discrete time jumps [17], 18] The standard deviation of the white noise before filtering is set to oe = 3. Realizations are simulated quasi continuously by approximating the continuous time signal by a 10 times oversampled discrete signal. An 8 pole Bessel filter is applied on the quasi continuous signal which is then ....

A. Albertsen and U.-P. Hansen, "Estimation of kinetic rate constants from multi-channel recordings by a direct fit of the time series," Biophys. J., vol. 67, pp. 1393--1403, October 1994.

....the maximum likelihood estimator under the restriction of detailed balance. The subscript n denotes the number of data points. There are ecient algorithms known to calculate the the maximum likelihood estimator from the measured data (Baum et al. 1970; Horn and Lange 1983; Fredkin and Rice 1992; Albertsen and Hansen 1994; Michalek and Timmer 1999) As indicated in Eq. 4) the twofold log likelihood ratio is asymptotically 2 1 distributed under the null hypothesis. The 2 distribution has one degree of freedom because the law of detailed balance imposes one constraint on the transition rates per loop. ....

Albertsen, A. and Hansen, U.-P. (1994). Estimation of kinetic rate constants from multi-channel recordings by a direct t of the time series. Biophys. J., 67:1393-1403.

.... application to the analysis of single channel recordings, both for the construction of an idealized quantal signal from a noisy recording (Chung et al. 1990; Fredkin and Rice, 1992a) and for estimation of kinetic parameters directly from the recording rather than from an idealized reconstruction (Albertson and Hansen, 1994; Fredkin and Rice, 1992b; Venkataramanan et al. 1996; Qin et al. 1994) Hidden Markov models have also been used in a variety of other areas, for example in speech recognition (Rabiner, 1989) and gene finding (Krogh et al. 1994) A key to their computational effectiveness is that the number of ....

....we believe that our methods will be especially effective in evaluating the likelihood of superpositions of independent channels. Such superpositions produce a very high dimensional state space which has hindered the successful application of otherwise promising hidden Markov model techniques (Albertson and Hansen, 1994). Our code is written in C to be driven by Matlab, and we will be pleased to share it with anyone who is interested. ....

Albertson, A. and Hansen, U.-P. (1994). Estimation of kinetic rate constants from multi-channel recordings by a direct fit of the time series.

.... ratios or functional dependence from common underlying variables are needed to express the dynamical behavior of a system e.g. to consider physical mechanisms and combinatorics [7] 8] agonist concentration [9] 10] multiple conductance sublevels [11] or for multi channel recordings [12] [13], 14] Sometimes, it is necessary to set certain rates to zero, defining a special topology of the directed graph of transitions. In either cases, it is hardly possible to formulate the recommended constraints in terms of discrete transition probabilities since they arise from the nonlinear ....

....to set certain rates to zero, defining a special topology of the directed graph of transitions. In either cases, it is hardly possible to formulate the recommended constraints in terms of discrete transition probabilities since they arise from the nonlinear matrix exponentiation operation [13]. Up to now the EM algorithm as main tool in HMM estimation [1] 5] is not formulated to deal directly with the parameters of the underlying continuous time process. This problem has been stated frequently [3] 11] 15] We present equations to perform EM reestimation iterations for such HMMs ....

A. Albertsen and U.-P. Hansen, "Estimation of kinetic rate constants from multi-channel recordings by a direct fit of the time series," Biophys. J., 67:1393--1403, 1994.

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A. Albertsen and U.-P. Hansen, \Estimation of kinetic rate constants from multi-channel recordings by a direct t of the time series," Biophys. Jour., vol. 67, pp. 1393-1403, 1994.

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