A Survey on Methods for Modeling and Analyzing Integrated Biological Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Computer Methods and Programs in Biomedicine
Application of evolutionary optimisers in data-based calibration of Activated Sludge Models
Expert Systems with Applications: An International Journal
Inference of hidden variables in systems of differential equations with genetic programming
Genetic Programming and Evolvable Machines
Pareto epsilon-dominance and identifiable solutions for BioCAD modeling
Proceedings of the 50th Annual Design Automation Conference
Computer Methods and Programs in Biomedicine
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Motivation: Mathematical modelling of biological systems is becoming a standard approach to investigate complex dynamic, non-linear interaction mechanisms in cellular processes. However, models may comprise non-identifiable parameters which cannot be unambiguously determined. Non-identifiability manifests itself in functionally related parameters, which are difficult to detect. Results: We present the method of mean optimal transformations, a non-parametric bootstrap-based algorithm for identifiability testing, capable of identifying linear and non-linear relations of arbitrarily many parameters, regardless of model size or complexity. This is performed with use of optimal transformations, estimated using the alternating conditional expectation algorithm (ACE). An initial guess or prior knowledge concerning the underlying relation of the parameters is not required. Independent, and hence identifiable parameters are determined as well. The quality of data at disposal is included in our approach, i.e. the non-linear model is fitted to data and estimated parameter values are investigated with respect to functional relations. We exemplify our approach on a realistic dynamical model and demonstrate that the variability of estimated parameter values decreases from 81 to 1% after detection and fixation of structural non-identifiabilities. Availability: Our algorithm is written in Matlab and R. It is available from the authors on request. An implementation of ACE, written in Matlab as well as in C, is available online at www.stefanhengl.de Contact: hengl@fdm.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online.