Abstract
BackgroundKinetic models can present mechanistic descriptions of molecular processes within a cell. They can be used to predict the dynamics of metabolite production, signal transduction or transcription of genes. Although there has been tremendous effort in constructing kinetic models for different biological systems, not much effort has been put into their validation. In this study, we introduce the concept of resampling methods for the analysis of kinetic models and present a statistical model invalidation approach.ResultsWe based our invalidation approach on the evaluation of a kinetic model’s predictive power through cross validation and forecast analysis. As a reference point for this evaluation, we used the predictive power of an unsupervised data analysis method which does not make use of any biochemical knowledge, namely Smooth Principal Components Analysis (SPCA) on the same test sets. Through a simulations study, we showed that too simple mechanistic descriptions can be invalidated by using our SPCA-based comparative approach until high amount of noise exists in the experimental data. We also applied our approach on an eicosanoid production model developed for human and concluded that the model could not be invalidated using the available data despite its simplicity in the formulation of the reaction kinetics. Furthermore, we analysed the high osmolarity glycerol (HOG) pathway in yeast to question the validity of an existing model as another realistic demonstration of our method.ConclusionsWith this study, we have successfully presented the potential of two resampling methods, cross validation and forecast analysis in the analysis of kinetic models’ validity. Our approach is easy to grasp and to implement, applicable to any ordinary differential equation (ODE) type biological model and does not suffer from any computational difficulties which seems to be a common problem for approaches that have been proposed for similar purposes. Matlab files needed for invalidation using SPCA cross validation and our toy model in SBML format are provided at http://www.bdagroup.nl/content/Downloads/software/software.php.
Highlights
Introduction to Machine LearningCambridge: MIT press; 2004. 30
We introduced the use of two resampling methods, namely cross validation and forecast analysis for the analysis of kinetic systems biology models
Cross validation and forecast analysis allowed us to use a part of the available time series metabolite concentration data to infer the proposed model’s kinetic parameters and the remaining part of the same dataset to assess the predictive power of the model
Summary
Introduction to Machine LearningCambridge: MIT press; 2004. 30. Box GEP, Jenkins GM: Time Series Analysis: Forecasting and Control. Kinetic models are mechanistic representations of biological systems They include information on two main levels. The first level of information includes the metabolites, enzymes, signaling molecules and chemical reactions involved in the model together with the formulation. The median of the number of the reactions and species that 462 curated kinetic models in Biomodels Database [5] included are only 12 and 11, respectively The information they provide at both levels increases very rapidly. In vitro and in vivo kinetics can be very different, in the values of the parameters but more importantly, in the formulation [3] This points to the need for careful investigation of the model’s validity on the first information level that we defined above
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