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**Harvard**

Berglund, M., Sunnåker, M., Adiels, M., Jirstrand, M. och Wennberg, B. (2012) *Investigations of a compartmental model for leucine kinetics using non-linear mixed effects models with ordinary and stochastic differential equations.*.

** BibTeX **

@article{

Berglund2012,

author={Berglund, Martin and Sunnåker, Mikael and Adiels, Martin and Jirstrand, Mats and Wennberg, Bernt},

title={Investigations of a compartmental model for leucine kinetics using non-linear mixed effects models with ordinary and stochastic differential equations.},

journal={Mathematical medicine and biology : a journal of the IMA},

issn={1477-8602},

volume={29},

issue={4},

pages={361-384},

abstract={Non-linear mixed effects (NLME) models represent a powerful tool to simultaneously analyse data from several individuals. In this study, a compartmental model of leucine kinetics is examined and extended with a stochastic differential equation to model non-steady-state concentrations of free leucine in the plasma. Data obtained from tracer/tracee experiments for a group of healthy control individuals and a group of individuals suffering from diabetes mellitus type 2 are analysed. We find that the interindividual variation of the model parameters is much smaller for the NLME models, compared to traditional estimates obtained from each individual separately. Using the mixed effects approach, the population parameters are estimated well also when only half of the data are used for each individual. For a typical individual, the amount of free leucine is predicted to vary with a standard deviation of 8.9% around a mean value during the experiment. Moreover, leucine degradation and protein uptake of leucine is smaller, proteolysis larger and the amount of free leucine in the body is much larger for the diabetic individuals than the control individuals. In conclusion, NLME models offers improved estimates for model parameters in complex models based on tracer/tracee data and may be a suitable tool to reduce data sampling in clinical studies.},

year={2012},

keywords={non-linear mixed effects models; two stage approach; compartmental models; tracer experiments; leucine kinetics; ordinary differential equations; stochastic differential equations; Ornstein-Uhlenbeck process },

}

** RefWorks **

RT Journal Article

SR Electronic

ID 148967

A1 Berglund, Martin

A1 Sunnåker, Mikael

A1 Adiels, Martin

A1 Jirstrand, Mats

A1 Wennberg, Bernt

T1 Investigations of a compartmental model for leucine kinetics using non-linear mixed effects models with ordinary and stochastic differential equations.

YR 2012

JF Mathematical medicine and biology : a journal of the IMA

SN 1477-8602

VO 29

IS 4

SP 361

OP 384

AB Non-linear mixed effects (NLME) models represent a powerful tool to simultaneously analyse data from several individuals. In this study, a compartmental model of leucine kinetics is examined and extended with a stochastic differential equation to model non-steady-state concentrations of free leucine in the plasma. Data obtained from tracer/tracee experiments for a group of healthy control individuals and a group of individuals suffering from diabetes mellitus type 2 are analysed. We find that the interindividual variation of the model parameters is much smaller for the NLME models, compared to traditional estimates obtained from each individual separately. Using the mixed effects approach, the population parameters are estimated well also when only half of the data are used for each individual. For a typical individual, the amount of free leucine is predicted to vary with a standard deviation of 8.9% around a mean value during the experiment. Moreover, leucine degradation and protein uptake of leucine is smaller, proteolysis larger and the amount of free leucine in the body is much larger for the diabetic individuals than the control individuals. In conclusion, NLME models offers improved estimates for model parameters in complex models based on tracer/tracee data and may be a suitable tool to reduce data sampling in clinical studies.

LA eng

PMID 21965323

DO 10.1093/imammb/dqr021

LK http://dx.doi.org/10.1093/imammb/dqr021

OL 30