No abstract

Algorithms for parameter estimation and model selection that identify both the structure and the parameters of an ordinary differential equation model from experimental data are presented. The work presented here focuses on the case of an unknown structure and some time course information available for every variable to be analysed, and this is exploited to make the algorithms as efficient as possible. The algorithms are designed to handle problems of realistic size, where reactions can be nonlinear in the parameters and where data can be sparse and noisy. To achieve computational efficiency, parameters are mostly estimated for one equation at a time, giving a fast and accurate parameter estimation algorithm compared with other algorithms in the literature. The model selection is done with an efficient heuristic search algorithm, where the structure is built incrementally. Two test systems are used that have previously been used to evaluate identification algorithms, a metabolic pathway and a genetic network. Both test systems were successfully identified by using a reasonable amount of simulated data. Besides, measurement noise of realistic levels can be handled. In comparison to other methods that were used for these test systems, the main strengths of the presented algorithms are that a fully specified model, and not only a structure, is identified, and that they are considerably faster compared with other identification algorithms.

Motivation: In recent years, the biological literature has seen a significant increase of reported methods for identifying both structure and parameters of ordinary differential equations (ODEs) from time series data. A natural way to evaluate the performance of such methods is to try them on a sufficient number of realistic test cases. However, weak practices in specifying identification problems and lack of commonly accepted benchmark problems makes it difficult to evaluate and compare different methods.Results: To enable better evaluation and comparisons between different methods, we propose how to specify identification problems as optimization problems with a model space of allowed reactions (e.g. reaction kinetics like Michaelis–Menten or S-systems), ranges for the parameters, time series data and an error function. We also define a file format for such problems.We then present a collection of more than 40 benchmark problems for ODE model identification of cellular systems. The collection includes realistic problems of different levels of difficulty w.r.t. size and quality of data. We consider both problems with simulated data from known systems, and problems with real data. Finally, we present results based on our identification algorithm for all benchmark problems. In comparison with publications on which we have based some of the benchmark problems, our approach allows all problems to be solved without the use of supercomputing.Availability: The benchmark problems are available at www.odeidentification.orgContact: peterg@chalmers.seSupplementary information: Supplementary data are available at Bioinformatics online.

Next to fuel costs, crew costs are the largest direct operating cost of airlines. Therefore much research has been devoted to the planning and scheduling of crews over the last thirty years. The planning and scheduling of crews is usually considered as two problems: the crew pairing problem and the crew assignment (rostering) problem. These problems are solved sequentially. In this paper we focus on the pairing problem. The aim of the paper is twofold. First, we give an overview of the crew pairing problem and synthesize the optimization methods that have been published previously. Second, we present the Carmen pairing construction system which is in operation at most major European airlines. Our purpose is to identify the particular properties of the Carmen system that have made this system the preferred decision support system for crew pairing optimization in Europe.