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way. The model overloading with number of independent parameters after its application to simulate finite experimental dataset may provide the good fit to experimental data by the price of making fitting results not sensible to the changes of parameters values, therefore leaving parameter values undetermined. Structural methods are also referred to as
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Structural identifiability analysis is a particular type of analysis in which the model structure itself is investigated for non-identifiability. Recognized non-identifiabilities may be removed analytically through substitution of the non-identifiable parameters with their combinations or by another
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Practical identifiability analysis can be performed by exploring the fit of existing model to experimental data. Once the fitting in any measure was obtained, parameter identifiability analysis can be performed either locally near a given point (usually near the parameter values provided the best
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could be applied as an important step to ensure correct choice of model, and sufficient amount of experimental data. The purpose of this analysis is either a quantified proof of correct model choice and integrality of experimental data acquired or such analysis can serve as an instrument for the
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or if there is an insufficient number of data points, it could be that the estimated parameter values could vary drastically without significantly influencing the goodness of fit. To address these issues the
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does not reveal how reliable the parameter estimates are. The goodness of fit is also not sufficient to prove the model was chosen correctly. For example, if the experimental data is
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detection of non-identifiable and sloppy parameters, helping planning the experiments and in building and improvement of the model at the early stages.
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model fit) or globally over the extended parameter space. The common example of the practical identifiability analysis is profile likelihood method.
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Stanhope, S.; Rubin, J. E.; Swigon D. (2014), "Identifiability of linear and linear-in-parameters dynamical systems from a single trajectory",
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Cobelli, C.; DiStefano, J. (1980). "Parameter and structural identifiability concepts and ambiguities: a critical review and analysis".
398:"Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood"
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Gutenkunst, Ryan N.; Waterfall, Joshua J.; Casey, Fergal P.; Brown, Kevin S.; Myers, Christopher R.; Sethna, James P. (2007).
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that are used to determine how well the parameters of a model are estimated by the quantity and quality of
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Raue, A.; Kreutz, C.; Maiwald, T.; Bachmann, J.; Schilling, M.; Klingmuller, U.; Timmer, J. (2009-08-01).
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Vandeginste, B.; Bates, D. M.; Watts, D. G. (1988). "Nonlinear regression analysis: Its applications".
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of a model, but also the relation of the model to particular experimental data or, more generally, the
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Lavielle, M.; Aarons, L. (2015), "What do we mean by identifiability in mixed effects models?",
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Methods used to determine how well the parameters of a model are estimated by experimental data
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Brun, Roland; Reichert, Peter; Künsch, Hans R. (2001).
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