BACKGROUND: Utilizing kinetic models of biological systems commonly require computational approaches to estimate parameters, posing a variety of challenges due to their highly non-linear and dynamic nature, which is further complicated by the issue of non-identifiability. We propose a novel parameter estimation framework by combining approaches for solving identifiability with a recently introduced filtering technique that can uniquely estimate parameters where conventional methods fail. This framework first conducts a thorough analysis to identify and classify the non-identifiable parameters and provides a guideline for solving them. If no feasible solution can be found, the framework instead initializes the filtering technique with informed prior to yield a unique solution. RESULTS: This framework has been applied to uniquely estimate parameter values for the sucrose accumulation model in sugarcane culm tissue and a gene regulatory network. In the first experiment the results show the progression of improvement in reliable and unique parameter estimation through the use of each tool to reduce and remove non-identifiability. The latter experiment illustrates the common situation where no further measurement data is available to solve the non-identifiability. These results show the successful application of the informed prior as well as the ease with which parallel data sources may be utilized without increasing the model complexity. CONCLUSION: The proposed unified framework is distinct from other approaches by providing a robust and complete solution which yields reliable and unique parameter estimation even in the face of non-identifiability.

MOTIVATION: In systems biology, kinetic models represent the biological system using a set of ordinary differential equations (ODEs). The correct values of the parameters within these ODEs are critical for a reliable study of the dynamic behaviour of such systems. Typically, it is only possible to experimentally measure a fraction of these parameter values. The rest must be indirectly determined from measurements of other quantities. In this article, we propose a novel statistical inference technique to computationally estimate these unknown parameter values. By characterizing the ODEs with non-linear state-space equations, this inference technique models the unknown parameters as hidden states, which can then be estimated from noisy measurement data. RESULTS: Here we extended the square-root unscented Kalman filter SR-UKF proposed by Merwe and Wan to include constraints with the state estimation process. We developed the constrained square-root unscented Kalman filter (CSUKF) to estimate parameters of non-linear state-space models. This probabilistic inference technique was successfully used to estimate parameters of a glycolysis model in yeast and a gene regulatory network. We showed that our method is numerically stable and can reliably estimate parameters within a biologically meaningful parameter space from noisy observations. When compared with the two common non-linear extensions of Kalman filter in addition to four widely used global optimization algorithms, CSUKF is shown to be both accurate and computationally efficient. With CSUKF, statistical analysis is straightforward, as it directly provides the uncertainty on the estimation result. AVAILABILITY AND IMPLEMENTATION: Matlab code available upon request from the author. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BACKGROUND: Metabolic flux analysis has become an established method in systems biology and functional genomics. The most common approach for determining intracellular metabolic fluxes is to utilize mass spectrometry in combination with stable isotope labeling experiments. However, before the mass spectrometric data can be used it has to be corrected for biases caused by naturally occurring stable isotopes, by the analytical technique(s) employed, or by the biological sample itself. Finally the MS data and the labeling information it contains have to be assembled into a data format usable by flux analysis software (of which several dedicated packages exist). Currently the processing of mass spectrometric data is time-consuming and error-prone requiring peak by peak cut-and-paste analysis and manual curation. In order to facilitate high-throughput metabolic flux analysis, the automation of multiple steps in the analytical workflow is necessary. RESULTS: Here we describe iMS2Flux, software developed to automate, standardize and connect the data flow between mass spectrometric measurements and flux analysis programs. This tool streamlines the transfer of data from extraction via correction tools to ¹³C-Flux software by processing MS data from stable isotope labeling experiments. It allows the correction of large and heterogeneous MS datasets for the presence of naturally occurring stable isotopes, initial biomass and several mass spectrometry effects. Before and after data correction, several checks can be performed to ensure accurate data. The corrected data may be returned in a variety of formats including those used by metabolic flux analysis software such as 13CFLUX, OpenFLUX and 13CFLUX2. CONCLUSION: iMS2Flux is a versatile, easy to use tool for the automated processing of mass spectrometric data containing isotope labeling information. It represents the core framework for a standardized workflow and data processing. Due to its flexibility it facilitates the inclusion of different experimental datasets and thus can contribute to the expansion of flux analysis applications.

No abstract available.