scholarly journals Determination of parameter identifiability in nonlinear biophysical models: A Bayesian approach

2014 ◽  
Vol 143 (3) ◽  
pp. 401-416 ◽  
Author(s):  
Keegan E. Hines ◽  
Thomas R. Middendorf ◽  
Richard W. Aldrich
Keyword(s):  
Biological Molecules ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Biophysical Parameters ◽  
Physical Mechanisms ◽  
Biophysical Models ◽  
Underlying Causes ◽  
True Values ◽  
Accurate Quantification

A major goal of biophysics is to understand the physical mechanisms of biological molecules and systems. Mechanistic models are evaluated based on their ability to explain carefully controlled experiments. By fitting models to data, biophysical parameters that cannot be measured directly can be estimated from experimentation. However, it might be the case that many different combinations of model parameters can explain the observations equally well. In these cases, the model parameters are not identifiable: the experimentation has not provided sufficient constraining power to enable unique estimation of their true values. We demonstrate that this pitfall is present even in simple biophysical models. We investigate the underlying causes of parameter non-identifiability and discuss straightforward methods for determining when parameters of simple models can be inferred accurately. However, for models of even modest complexity, more general tools are required to diagnose parameter non-identifiability. We present a method based in Bayesian inference that can be used to establish the reliability of parameter estimates, as well as yield accurate quantification of parameter confidence.

scholarly journals An approach to identify time consistent model parameters: sub-period calibration

2013 ◽  
Vol 17 (1) ◽  
pp. 149-161 ◽  
Author(s):  
S. Gharari ◽  
M. Hrachowitz ◽  
F. Fenicia ◽  
H. H. G. Savenije
Keyword(s):  
Model Performance ◽  
Realistic Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
New Approach ◽  
Consistent Model ◽  
Set Up ◽  
Consistent Parameter

Abstract. Conceptual hydrological models rely on calibration for the identification of their parameters. As these models are typically designed to reflect real catchment processes, a key objective of an appropriate calibration strategy is the determination of parameter sets that reflect a "realistic" model behavior. Previous studies have shown that parameter estimates for different calibration periods can be significantly different. This questions model transposability in time, which is one of the key conditions for the set-up of a "realistic" model. This paper presents a new approach that selects parameter sets that provide a consistent model performance in time. The approach consists of testing model performance in different periods, and selecting parameter sets that are as close as possible to the optimum of each individual sub-period. While aiding model calibration, the approach is also useful as a diagnostic tool, illustrating tradeoffs in the identification of time-consistent parameter sets. The approach is applied to a case study in Luxembourg using the HyMod hydrological model as an example.

scholarly journals Bootstrapping Neural Networks

2000 ◽  
Vol 12 (8) ◽  
pp. 1929-1949 ◽  
Author(s):  
Jürgen Franke ◽  
Michael H. Neumann
Keyword(s):  
Neural Networks ◽  
Classical Method ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Statistical Estimator ◽  
Consistency Results ◽  
Data Generating Process ◽  
Probabilistic Structure ◽  
Output Space

Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called bootstrap, which is based on an imitation of the probabilistic structure of the data-generating process on the basis of the information provided by a given set of random observations. In this article we investigate this classical method in the context of artificial neural networks used for estimating a mapping from input to output space. We establish consistency results for bootstrap estimates of the distribution of parameter estimates.

scholarly journals Moving beyond traditional model calibration or how to better identify realistic model parameters: sub-period calibration

2012 ◽  
Vol 9 (2) ◽  
pp. 1885-1918
Author(s):  
S. Gharari ◽  
M. Hrachowitz ◽  
F. Fenicia ◽  
H. H. G. Savenije
Keyword(s):  
Model Calibration ◽  
Model Performance ◽  
Realistic Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
New Approach ◽  
Consistent Model ◽  
Set Up

Abstract. Conceptual hydrological models often rely on calibration for the identification of their parameters. As these models are typically designed to reflect real catchment processes, a key objective of an appropriate calibration strategy is the determination of parameter sets that reflect a "realistic" model behavior. Previous studies have shown that parameter estimates for different calibration periods can be significantly different. This questions model transposability in time, which is one of the key conditions for the set-up of a "realistic" model. This paper presents a new approach that selects parameter sets that provide a consistent model performance in time. The approach consists of confronting model performance in different periods, and selecting parameter sets that are as close as possible to the optimum of each individual sub-period. While aiding model calibration, the approach is also useful as a diagnostic tool, illustrating tradeoffs in the identification of time consistent parameter sets. The approach is demonstrated in a case study where we illustrate the multi-objective calibration of the HyMod hydrological model to a Luxembourgish catchment.

scholarly journals Towards Biophysical Markers of Depression Vulnerability

2021 ◽  
Author(s):  
D.A. Pinotsis ◽  
S. Fitzgerald ◽  
C. See ◽  
A. Sementsova ◽  
A. S. Widge
Keyword(s):  
Feature Space ◽  
Neural Model ◽  
Biophysical Model ◽  
Model Parameters ◽  
Biophysical Parameters ◽  
Biophysical Models ◽  
Classical Statistics ◽  
Heterogeneity Variance ◽  
Brain Data ◽  
Low Dimensional

AbstractA major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. We constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

scholarly journals Characterization and Quantification of Selenoprotein P: Challenges to Mass Spectrometry

2021 ◽  
Vol 22 (12) ◽  
pp. 6283
Author(s):  
Jérémy Lamarche ◽  
Luisa Ronga ◽  
Joanna Szpunar ◽  
Ryszard Lobinski
Keyword(s):  
Mass Spectrometry ◽  
State Of The Art ◽  
Animal Health ◽  
Primary Standard ◽  
Selenoprotein P ◽  
Primary Standards ◽  
Analytical Approaches ◽  
Careful Investigation ◽  
Accurate Quantification

Selenoprotein P (SELENOP) is an emerging marker of the nutritional status of selenium and of various diseases, however, its chemical characteristics still need to be investigated and methods for its accurate quantitation improved. SELENOP is unique among selenoproteins, as it contains multiple genetically encoded SeCys residues, whereas all the other characterized selenoproteins contain just one. SELENOP occurs in the form of multiple isoforms, truncated species and post-translationally modified variants which are relatively poorly characterized. The accurate quantification of SELENOP is contingent on the availability of specific primary standards and reference methods. Before recombinant SELENOP becomes available to be used as a primary standard, careful investigation of the characteristics of the SELENOP measured by electrospray MS and strict control of the recoveries at the various steps of the analytical procedures are strongly recommended. This review critically discusses the state-of-the-art of analytical approaches to the characterization and quantification of SELENOP. While immunoassays remain the standard for the determination of human and animal health status, because of their speed and simplicity, mass spectrometry techniques offer many attractive and complementary features that are highlighted and critically evaluated.

Experience with the IMMa tyre test bench for the determination of tyre model parameters using genetic techniques

2005 ◽  
Vol 43 (sup1) ◽  
pp. 253-266 ◽  
Author(s):  
J. A. Cabrera ◽  
A. Ortiz ◽  
E. Carabias ◽  
A. Simón
Keyword(s):  
Test Bench ◽  
Model Parameters ◽  
Tyre Model ◽  
Genetic Techniques

Factors affecting in vitro and in vivo antioxidant effects. Experimental conditions and nature of oxidants determine antioxidant efficacy

2021 ◽  
pp. 1-9
Author(s):  
Etsuo Niki
Keyword(s):  
Biological Molecules ◽  
Experimental Conditions ◽  
Factors Affecting ◽  
Beneficial Effects ◽  
Multiple Oxidants ◽  
Antioxidant Effects

Reactive oxygen and nitrogen species have been implicated in the onset and progression of various diseases and the role of antioxidants in the maintenance of health and prevention of diseases has received much attention. The action and effect of antioxidants have been studied extensively under different reaction conditions in multiple media. The antioxidant effects are determined by many factors. This review aims to discuss several important issues that should be considered for determination of experimental conditions and interpretation of experimental results in order to understand the beneficial effects and limit of antioxidants against detrimental oxidation of biological molecules. Emphasis was laid on cell culture experiments and effects of diversity of multiple oxidants on antioxidant efficacy.

scholarly journals Parameter stability and consistency in an alongshore-current model determined with Markov chain Monte Carlo

2008 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
B. G. Ruessink
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Parameter Stability ◽  
Small Uncertainty ◽  
Best Fit ◽  
Stability And Consistency

When a numerical model is to be used as a practical tool, its parameters should preferably be stable and consistent, that is, possess a small uncertainty and be time-invariant. Using data and predictions of alongshore mean currents flowing on a beach as a case study, this paper illustrates how parameter stability and consistency can be assessed using Markov chain Monte Carlo. Within a single calibration run, Markov chain Monte Carlo estimates the parameter posterior probability density function, its mode being the best-fit parameter set. Parameter stability is investigated by stepwise adding new data to a calibration run, while consistency is examined by calibrating the model on different datasets of equal length. The results for the present case study indicate that various tidal cycles with strong (say, >0.5 m/s) currents are required to obtain stable parameter estimates, and that the best-fit model parameters and the underlying posterior distribution are strongly time-varying. This inconsistent parameter behavior may reflect unresolved variability of the processes represented by the parameters, or may represent compensational behavior for temporal violations in specific model assumptions.

Multi-day flow forecasting using the Kalman filter

1991 ◽  
Vol 18 (2) ◽  
pp. 320-327 ◽  
Author(s):  
Murray A. Fitch ◽  
Edward A. McBean
Keyword(s):  
Kalman Filter ◽  
Prediction Model ◽  
Linear Prediction ◽  
Measured Data ◽  
System Model ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Forecasting Performance ◽  
Optimal Forecasting ◽  
Linear Prediction Model

A model is developed for the prediction of river flows resulting from combined snowmelt and precipitation. The model employs a Kalman filter to reflect uncertainty both in the measured data and in the system model parameters. The forecasting algorithm is used to develop multi-day forecasts for the Sturgeon River, Ontario. The algorithm is shown to develop good 1-day and 2-day ahead forecasts, but the linear prediction model is found inadequate for longer-term forecasts. Good initial parameter estimates are shown to be essential for optimal forecasting performance. Key words: Kalman filter, streamflow forecast, multi-day, streamflow, Sturgeon River, MISP algorithm.

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