Summary: Mathematical modeling has a key role in systems biology. Model building is often regarded as an iterative loop involving several tasks, among which the estimation of unknown parameters of the model from a certain set of experimental data is of central importance. This problem of parameter estimation has many possible pitfalls, and modelers should be very careful to avoid them. Many of such difficulties arise from a fundamental (yet often overlooked) property: the so-called structural (or a priori) identifiability, which considers the uniqueness of the estimated parameters. Obviously, the structural identifiability of any tentative model should be checked at the beginning of the model building loop. However, checking this property for arbitrary non-linear dynamic models is not an easy task. Here we present a software toolbox, GenSSI (Generating Series for testing Structural Identifiability), which enables non-expert users to carry out such analysis. The toolbox runs under the popular MATLAB environment and is accompanied by detailed documentation and relevant examples.Availability: The GenSSI toolbox and the related documentation are available at http://www.iim.csic.es/%7Egenssi.Contact: ebalsa@iim.csic.es

MotivationMathematical modeling using ordinary differential equations is used in systems biology to improve the understanding of dynamic biological processes. The parameters of ordinary differential equation models are usually estimated from experimental data. To analyze a priori the uniqueness of the solution of the estimation problem, structural identifiability analysis methods have been developed.ResultsWe introduce GenSSI 2.0, an advancement of the software toolbox GenSSI (Generating Series for testing Structural Identifiability). GenSSI 2.0 is the first toolbox for structural identifiability analysis to implement Systems Biology Markup Language import, state/parameter transformations and multi-experiment structural identifiability analysis. In addition, GenSSI 2.0 supports a range of MATLAB versions and is computationally more efficient than its previous version, enabling the analysis of more complex models.Availability and implementationGenSSI 2.0 is an open-source MATLAB toolbox and available at https://github.com/genssi-developer/GenSSI.Supplementary information Supplementary data are available at Bioinformatics online.

A fundamental problem in the analysis of chemical reactions networks consists of identifying concentration values along time or in steady state which are coherent with the experimental concentration data available. When concentration measurements are incomplete, either because information is missing about the concentration of a species at a particular time instant, or even there is no information at all on the concentration of a species, then the problem becomes ill-defined, and then different concentration curves are compatible with existing data. In this paper we address the problem of finding the extreme (highest and lowest) concentrations under incomplete data measurements; as a byproduct of our approach, the model parameters associated with such extreme concentrations are obtained. These extreme concentrations provide valuable information on the impact that incomplete measurements have on the theoretical reconstruction of concentrations from experimental data. To obtain such concentrations range, mathematical optimization problems are formulated, solvable by a variety of global optimization approaches, such as, for example, the stochastic global optimization method suggested.

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