Open Access Methodology article

Incremental parameter estimation of kinetic metabolic network models

Gengjie Jia1, Gregory Stephanopoulos2 and Rudiyanto Gunawan3*

Author Affiliations

1 Chemical and Pharmaceutical Engineering, Singapore-MIT Alliance, Singapore, 117576, Singapore

2 Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA

3 Institute for Chemical and Bioengineering, ETH Zürich, 8093, Zürich, Switzerland

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BMC Systems Biology 2012, 6:142  doi:10.1186/1752-0509-6-142

Published: 21 November 2012



An efficient and reliable parameter estimation method is essential for the creation of biological models using ordinary differential equation (ODE). Most of the existing estimation methods involve finding the global minimum of data fitting residuals over the entire parameter space simultaneously. Unfortunately, the associated computational requirement often becomes prohibitively high due to the large number of parameters and the lack of complete parameter identifiability (i.e. not all parameters can be uniquely identified).


In this work, an incremental approach was applied to the parameter estimation of ODE models from concentration time profiles. Particularly, the method was developed to address a commonly encountered circumstance in the modeling of metabolic networks, where the number of metabolic fluxes (reaction rates) exceeds that of metabolites (chemical species). Here, the minimization of model residuals was performed over a subset of the parameter space that is associated with the degrees of freedom in the dynamic flux estimation from the concentration time-slopes. The efficacy of this method was demonstrated using two generalized mass action (GMA) models, where the method significantly outperformed single-step estimations. In addition, an extension of the estimation method to handle missing data is also presented.


The proposed incremental estimation method is able to tackle the issue on the lack of complete parameter identifiability and to significantly reduce the computational efforts in estimating model parameters, which will facilitate kinetic modeling of genome-scale cellular metabolism in the future.

Incremental parameter estimation; Kinetic modeling; Metabolic network; GMA model