Maximum likelihood estimation of the kinetics of receptor-mediated adhesion

Uz, Bilge; Arslan, Erdem; Laurenzi, Ian J.

doi:10.1016/j.jtbi.2009.10.015

Cited by 6 publications

(15 citation statements)

References 48 publications

(43 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of stochastic chemical kinetics, parameter inference methods are either based on Markov chain Monte Carlo schemes [ 37 – 40 ], on approximate Bayesian computation techniques [ 41 – 43 ] or on maximum likelihood estimation using a direct approximation of the likelihood [ 2 , 44 ] or a simulation-based estimate [ 45 , 46 ]. Maximum likelihood estimators are, in a sense, the most informative estimates of unknown parameters [ 47 ] and have desirable mathematical properties such as unbiasedness, efficiency, and normality.…”

Section: Discussionmentioning

confidence: 99%

Generalized method of moments for estimating parameters of stochastic reaction networks

Lück

Wolf

2016

BMC Syst Biol

View full text Add to dashboard Cite

BackgroundDiscrete-state stochastic models have become a well-established approach to describe biochemical reaction networks that are influenced by the inherent randomness of cellular events. In the last years several methods for accurately approximating the statistical moments of such models have become very popular since they allow an efficient analysis of complex networks.ResultsWe propose a generalized method of moments approach for inferring the parameters of reaction networks based on a sophisticated matching of the statistical moments of the corresponding stochastic model and the sample moments of population snapshot data. The proposed parameter estimation method exploits recently developed moment-based approximations and provides estimators with desirable statistical properties when a large number of samples is available. We demonstrate the usefulness and efficiency of the inference method on two case studies.ConclusionsThe generalized method of moments provides accurate and fast estimations of unknown parameters of reaction networks. The accuracy increases when also moments of order higher than two are considered. In addition, the variance of the estimator decreases, when more samples are given or when higher order moments are included.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0342-8) contains supplementary material, which is available to authorized users.

show abstract

Section: Discussionmentioning

confidence: 99%

Generalized method of moments for estimating parameters of stochastic reaction networks

Lück

Wolf

2016

BMC Syst Biol

View full text Add to dashboard Cite

show abstract

“…by guest www.bloodjournal.org From interactions and their subsequent fit to the Bell equation were performed. 37 Regression analyses of these simulations fit the data at bond forces ranging from 36 pN to 217 pN, and thus permitted the determination of k 0 off and s ( Figure 2F and Table 1). Although the major effect of the I1309V substitution was to prolong the intrinsic lifetime of the tether bond (sixfold), R1326H shortened its duration (2.5-fold).…”

Section: I1309v and R1326h Mutations Differentially Alter Bond Formatmentioning

confidence: 90%

Exploiting the kinetic interplay between GPIbα–VWF binding interfaces to regulate hemostasis and thrombosis

Chen

Zhou

Diacovo

et al. 2014

Blood

View full text Add to dashboard Cite

show abstract

“…In the context of stochastic chemical kinetics, parameter inference methods are either based on Bayesian inference [2,18,20] or maximum likelihood estimation [15,19,17]. The advantage of the latter method is that the corresponding estimators are, in a sense, the most informative estimates of unknown parameters [10] and have desirable mathematical properties such as unbiasedness, efficiency, and normality [11].…”

Section: Related Workmentioning

confidence: 99%

Parameter Identification for Markov Models of Biochemical Reactions

Andreychenko

Mikeev

Spieler

et al. 2011

Computer Aided Verification

View full text Add to dashboard Cite

We propose a numerical technique for parameter inference in Markov models of biological processes. Based on time-series data of a process we estimate the kinetic rate constants by maximizing the likelihood of the data. The computation of the likelihood relies on a dynamic abstraction of the discrete state space of the Markov model which successfully mitigates the problem of state space largeness. We compare two variants of our method to state-of-the-art, recently published methods and demonstrate their usefulness and efficiency on several case studies from systems biology.

show abstract

Maximum likelihood estimation of the kinetics of receptor-mediated adhesion

Cited by 6 publications

References 48 publications

Generalized method of moments for estimating parameters of stochastic reaction networks

Generalized method of moments for estimating parameters of stochastic reaction networks

Exploiting the kinetic interplay between GPIbα–VWF binding interfaces to regulate hemostasis and thrombosis

Parameter Identification for Markov Models of Biochemical Reactions

Contact Info

Product

Resources

About