TY - JOUR
T1 - Maximum likelihood estimation of ion channel kinetics from macroscopic currents
AU - Milescu, Lorin S.
AU - Akk, Gustav
AU - Sachs, Frederick
N1 - Funding Information:
This material is based upon work supported by the National Institutes of Health (grant RR11114 for L.M. and F.S.), and by the National Science Foundation (grant 0110282 for G.A.).
PY - 2005/4
Y1 - 2005/4
N2 - We describe a maximum likelihood method for direct estimation of rate constants from macroscopic ion channel data for kinetic models of arbitrary size and topology. The number of channels in the preparation, and the mean and standard deviation of the unitary current can be estimated, and a priori constraints can be imposed on rate constants. The method allows for arbitrary stimulation protocols, including stimuli with finite rise time, trains of ligand or voltage steps, and global fitting across different experimental conditions. The initial state occupancies can be optimized from the fit kinetics. Utilizing arbitrary stimulation protocols and using the mean and the variance of the current reduce or eliminate problems of model identifiability (Kienker, 1989). The algorithm is faster than a recent method that uses the full autocovariance matrix (Celentano and Hawkes, 2004), in part due to the analytical calculation of the likelihood gradients. We tested the method with simulated data and with real macroscopic currents from acetylcholine receptors, elicited in response to brief pulses of carbachol. Given appropriate stimulation protocols, our method chose a reasonable model size and topology.
AB - We describe a maximum likelihood method for direct estimation of rate constants from macroscopic ion channel data for kinetic models of arbitrary size and topology. The number of channels in the preparation, and the mean and standard deviation of the unitary current can be estimated, and a priori constraints can be imposed on rate constants. The method allows for arbitrary stimulation protocols, including stimuli with finite rise time, trains of ligand or voltage steps, and global fitting across different experimental conditions. The initial state occupancies can be optimized from the fit kinetics. Utilizing arbitrary stimulation protocols and using the mean and the variance of the current reduce or eliminate problems of model identifiability (Kienker, 1989). The algorithm is faster than a recent method that uses the full autocovariance matrix (Celentano and Hawkes, 2004), in part due to the analytical calculation of the likelihood gradients. We tested the method with simulated data and with real macroscopic currents from acetylcholine receptors, elicited in response to brief pulses of carbachol. Given appropriate stimulation protocols, our method chose a reasonable model size and topology.
UR - http://www.scopus.com/inward/record.url?scp=17844405808&partnerID=8YFLogxK
U2 - 10.1529/biophysj.104.053256
DO - 10.1529/biophysj.104.053256
M3 - Article
C2 - 15681642
AN - SCOPUS:17844405808
SN - 0006-3495
VL - 88
SP - 2494
EP - 2515
JO - Biophysical Journal
JF - Biophysical Journal
IS - 4
ER -