Optimization of time-course experiments for kinetic model discrimination.
Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key...
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2012-01-01
|
Series: | PLoS ONE |
Online Access: | http://europepmc.org/articles/PMC3293846?pdf=render |
id |
doaj-b1f210b995d34775a2ccc8d476c4ce56 |
---|---|
record_format |
Article |
spelling |
doaj-b1f210b995d34775a2ccc8d476c4ce562020-11-24T21:26:26ZengPublic Library of Science (PLoS)PLoS ONE1932-62032012-01-0173e3274910.1371/journal.pone.0032749Optimization of time-course experiments for kinetic model discrimination.Nuno F LagesCarlos CordeiroMarta Sousa SilvaAna Ponces FreireAntónio E N FerreiraSystems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction.In this work, a method is proposed to optimize the design of enzyme kinetic assays with the goal of selecting a model among a set of candidates. We focus on models with systems of ordinary differential equations as the underlying mathematical description. The method provides a design where an extension of the Kullback-Leibler distance, computed over the time courses predicted by the models, is maximized. Given the asymmetric nature this measure, a generalized differential evolution algorithm for multi-objective optimization problems was used.The kinetics of yeast glyoxalase I (EC 4.4.1.5) was chosen as a difficult test case to evaluate the method. Although a single-substrate kinetic model is usually considered, a two-substrate mechanism has also been proposed for this enzyme. We designed an experiment capable of discriminating between the two models by optimizing the initial substrate concentrations of glyoxalase I, in the presence of the subsequent pathway enzyme, glyoxalase II (EC 3.1.2.6). This discriminatory experiment was conducted in the laboratory and the results indicate a two-substrate mechanism for the kinetics of yeast glyoxalase I.http://europepmc.org/articles/PMC3293846?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nuno F Lages Carlos Cordeiro Marta Sousa Silva Ana Ponces Freire António E N Ferreira |
spellingShingle |
Nuno F Lages Carlos Cordeiro Marta Sousa Silva Ana Ponces Freire António E N Ferreira Optimization of time-course experiments for kinetic model discrimination. PLoS ONE |
author_facet |
Nuno F Lages Carlos Cordeiro Marta Sousa Silva Ana Ponces Freire António E N Ferreira |
author_sort |
Nuno F Lages |
title |
Optimization of time-course experiments for kinetic model discrimination. |
title_short |
Optimization of time-course experiments for kinetic model discrimination. |
title_full |
Optimization of time-course experiments for kinetic model discrimination. |
title_fullStr |
Optimization of time-course experiments for kinetic model discrimination. |
title_full_unstemmed |
Optimization of time-course experiments for kinetic model discrimination. |
title_sort |
optimization of time-course experiments for kinetic model discrimination. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2012-01-01 |
description |
Systems biology relies heavily on the construction of quantitative models of biochemical networks. These models must have predictive power to help unveiling the underlying molecular mechanisms of cellular physiology, but it is also paramount that they are consistent with the data resulting from key experiments. Often, it is possible to find several models that describe the data equally well, but provide significantly different quantitative predictions regarding particular variables of the network. In those cases, one is faced with a problem of model discrimination, the procedure of rejecting inappropriate models from a set of candidates in order to elect one as the best model to use for prediction.In this work, a method is proposed to optimize the design of enzyme kinetic assays with the goal of selecting a model among a set of candidates. We focus on models with systems of ordinary differential equations as the underlying mathematical description. The method provides a design where an extension of the Kullback-Leibler distance, computed over the time courses predicted by the models, is maximized. Given the asymmetric nature this measure, a generalized differential evolution algorithm for multi-objective optimization problems was used.The kinetics of yeast glyoxalase I (EC 4.4.1.5) was chosen as a difficult test case to evaluate the method. Although a single-substrate kinetic model is usually considered, a two-substrate mechanism has also been proposed for this enzyme. We designed an experiment capable of discriminating between the two models by optimizing the initial substrate concentrations of glyoxalase I, in the presence of the subsequent pathway enzyme, glyoxalase II (EC 3.1.2.6). This discriminatory experiment was conducted in the laboratory and the results indicate a two-substrate mechanism for the kinetics of yeast glyoxalase I. |
url |
http://europepmc.org/articles/PMC3293846?pdf=render |
work_keys_str_mv |
AT nunoflages optimizationoftimecourseexperimentsforkineticmodeldiscrimination AT carloscordeiro optimizationoftimecourseexperimentsforkineticmodeldiscrimination AT martasousasilva optimizationoftimecourseexperimentsforkineticmodeldiscrimination AT anaponcesfreire optimizationoftimecourseexperimentsforkineticmodeldiscrimination AT antonioenferreira optimizationoftimecourseexperimentsforkineticmodeldiscrimination |
_version_ |
1725979875510059008 |