Often a functional relationship between parameters presents itself by a one-dimensional manifold in parameter space containing parameter sets of optimal goodness. The method takes time course recordings of biochemical systems in steady state or transient state as input. The authors present a method to identify model parameters that are structurally non- identifiable in a deterministic framework. However, it turns out that intrinsic fluctuations in particle numbers can make parameters identifiable that were previously non- identifiable. Noise in measured data is usually considered to be a nuisance for parameter estimation. Model parameters can be structurally non- identifiable because of functional relationships. Besides the lack of experimental data of high enough quality, some of the biggest challenges here are identification issues. Parameterisation of kinetic models plays a central role in computational systems biology. Zimmer, Christoph Sahle, Sven Pahle, Jürgen In this study, we compared clinical and laboratory parameters between patients with acute renal failure (ARF) and chronic renal failure (CRF), to identify discriminatory .Įxploiting intrinsic fluctuations to identify model parameters. Introduction: In developing countries, a large number of patients presenting acutely in renal failure are indeed cases of advanced chronic renal failure. What Clinical and Laboratory Parameters Distinguish Between. A detailed mathematical description of the algorithm is given in the appendices. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. International Nuclear Information System (INIS) MXLKID: a maximum likelihood parameter identifier CONCLUSIONS/SIGNIFICANCE: We have shown that the previously developed concepts of parameter local identifiability and redundancy are closely related to the apparently weaker properties of weak local identifiability and gradient weak local identifiability-within the widely used exponential family these concepts largely coincide. (J Theoret Biol 254 (2008 229-238 that generalize a large number of other recently used quasi-biological cancer models. We consider applications to a recently developed class of cancer models of Little and Wright (Math Biosciences 183 (2003 111-134 and Little et al. Within the widely used exponential family, parameter irredundancy, local identifiability, gradient weak local identifiability and weak local identifiability are shown to be largely equivalent. We relate these to the notions of parameter identifiability and redundancy previously introduced by Rothenberg (Econometrica 39 (1971 577-591 and Catchpole and Morgan (Biometrika 84 (1997 187-196. These are based on local properties of the likelihood, in particular the rank of the Hessian matrix. METHODOLOGY/PRINCIPAL FINDINGS: In this paper we outline general considerations on parameter identifiability, and introduce the notion of weak local identifiability and gradient weak local identifiability. Before data is analysed it is critical to determine which model parameters are identifiable or redundant to avoid ill-defined and poorly convergent regression. Closely related to this idea is that of redundancy, that a set of parameters can be expressed in terms of some smaller set. Such parameters are said to be unidentifiable, the remaining parameters being identifiable. It may well be that some of these parameters cannot be derived from observed data via regression techniques. Parameter identifiability and redundancy: theoretical considerations.ĭirectory of Open Access Journals (Sweden)įull Text Available BACKGROUND: Models for complex biological systems may involve a large number of parameters.
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