Simulation of calcium homeostasis: modeling and parameter estimation

1983 ◽  
Vol 245 (5) ◽  
pp. R664-R672 ◽  
Author(s):  
S. Hurwitz ◽  
S. Fishman ◽  
A. Bar ◽  
M. Pines ◽  
G. Riesenfeld ◽  
...  
Keyword(s):  
Simulation Model ◽  
Plasma Calcium ◽  
Model Parameters ◽  
Estimation Of Parameters ◽  
Designed Experiments ◽  
Calcium Loading ◽  
Infusion Experiment ◽  
Dose Model ◽  
Edta Infusion

The system that regulates plasma calcium in the bird has been formalized into a model based on a series of differential equations and solved by computer simulation. Bone, kidney, and intestine have been considered as the control subsystems, with parathyroid hormone and 1,25-dihydroxycholecalciferol as the regulating hormones. The parameters used in the simulation model have been computed either from published results or by specifically designed experiments described here. For the estimation of parameters, an iterative procedure has been developed that was designed to minimize the sum of square errors between observed and system-simulated values. Parameters of 1,25-dihydroxycholecalciferol metabolism were experimentally obtained from the kinetic behavior of the 3H-labeled hormone in rachitic birds after a single dose. Model parameters have been adjusted using the results of in vivo calcium loading and validated by an EDTA infusion experiment. The simulation model has been used to study the hierarchy of the activities of the three control subsystems and of the regulating hormones, at different calcium intakes. Positive or negative errors in plasma calcium resulted in an asymmetry in the activities of the controlling systems, bone and kidney, whereas the intestine is characterized by its relatively long response time.

scholarly journals A systems engineering approach to validation of a pulmonary physiology simulator for clinical applications

2010 ◽  
Vol 8 (54) ◽  
pp. 44-55 ◽  
Author(s):  
A. Das ◽  
Z. Gao ◽  
P. P. Menon ◽  
J. G. Hardman ◽  
D. G. Bates
Keyword(s):  
Simulation Model ◽  
Systems Engineering ◽  
Best Practice ◽  
Optimization Methods ◽  
Population Heterogeneity ◽  
Model Parameters ◽  
General Applicability ◽  
Disease Heterogeneity ◽  
Pulmonary Physiology

Physiological simulators which are intended for use in clinical environments face harsh expectations from medical practitioners; they must cope with significant levels of uncertainty arising from non-measurable parameters, population heterogeneity and disease heterogeneity, and their validation must provide watertight proof of their applicability and reliability in the clinical arena. This paper describes a systems engineering framework for the validation of an in silico simulation model of pulmonary physiology. We combine explicit modelling of uncertainty/variability with advanced global optimization methods to demonstrate that the model predictions never deviate from physiologically plausible values for realistic levels of parametric uncertainty. The simulation model considered here has been designed to represent a dynamic in vivo cardiopulmonary state iterating through a mass-conserving set of equations based on established physiological principles and has been developed for a direct clinical application in an intensive-care environment. The approach to uncertainty modelling is adapted from the current best practice in the field of systems and control engineering, and a range of advanced optimization methods are employed to check the robustness of the model, including sequential quadratic programming, mesh-adaptive direct search and genetic algorithms. An overview of these methods and a comparison of their reliability and computational efficiency in comparison to statistical approaches such as Monte Carlo simulation are provided. The results of our study indicate that the simulator provides robust predictions of arterial gas pressures for all realistic ranges of model parameters, and also demonstrate the general applicability of the proposed approach to model validation for physiological simulation.

scholarly journals Optimally designed vs intuition-driven inputs: the study case of promoter activity modelling

2018 ◽  
Author(s):  
L. Bandiera ◽  
V. Kothamachu ◽  
E. Balsa-Canto ◽  
P. S. Swain ◽  
F. Menolascina
Keyword(s):  
Synthetic Biology ◽  
Predictive Ability ◽  
Inducible Promoter ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Designed Experiments ◽  
Additional Information ◽  
Basic Task ◽  
Engineering Discipline

AbstractSynthetic biology is an emerging engineering discipline that aims at synthesising logical circuits into cells to accomplish new functions. Despite a thriving community and some notable successes, the basic task of assembling predictable gene circuits is still a key challenge. Mathematical models are uniquely suited to help solve this issue. Yet in biology they are perceived as expensive and laborious to obtain because low-information experiments have often been used to infer model parameters. How much additional information can be gained using optimally designed experiments? To tackle this question we consider a building block in Synthetic Biology, an inducible promoter in yeast S. cerevisiae. Using in vivo data we re-fit a mathematical model for such a system; we then compare in silico the quality of the parameter estimates when model calibration is done using typical (e.g. step inputs) and optimally designed experiments. We find that Optimal Experimental Design leads to ~70% improvement in the predictive ability of the inferred models. We conclude providing suggestions on how optimally designed experiments can be implemented in vivo.

scholarly journals Formulating the history matching problem with consistent error statistics

2021 ◽  
Author(s):  
Geir Evensen
Keyword(s):  
Simulation Model ◽  
Measurement Errors ◽  
History Matching ◽  
Critical Role ◽  
Bayes Theorem ◽  
Conditional Probability Density ◽  
Model Parameters ◽  
Rate Data ◽  
Matching Problem ◽  
Reservoir Prediction

AbstractIt is common to formulate the history-matching problem using Bayes’ theorem. From Bayes’, the conditional probability density function (pdf) of the uncertain model parameters is proportional to the prior pdf of the model parameters, multiplied by the likelihood of the measurements. The static model parameters are random variables characterizing the reservoir model while the observations include, e.g., historical rates of oil, gas, and water produced from the wells. The reservoir prediction model is assumed perfect, and there are no errors besides those in the static parameters. However, this formulation is flawed. The historical rate data only approximately represent the real production of the reservoir and contain errors. History-matching methods usually take these errors into account in the conditioning but neglect them when forcing the simulation model by the observed rates during the historical integration. Thus, the model prediction depends on some of the same data used in the conditioning. The paper presents a formulation of Bayes’ theorem that considers the data dependency of the simulation model. In the new formulation, one must update both the poorly known model parameters and the rate-data errors. The result is an improved posterior ensemble of prediction models that better cover the observations with more substantial and realistic uncertainty. The implementation accounts correctly for correlated measurement errors and demonstrates the critical role of these correlations in reducing the update’s magnitude. The paper also shows the consistency of the subspace inversion scheme by Evensen (Ocean Dyn. 54, 539–560 2004) in the case with correlated measurement errors and demonstrates its accuracy when using a “larger” ensemble of perturbations to represent the measurement error covariance matrix.

FE-basierte Analyse*/FE-based analysis of the influence of remachining on component characteristics adjusted by forming processes

2018 ◽  
Vol 108 (01-02) ◽  
pp. 41-46
Author(s):  
F. Vogel ◽  
M. Tiffe ◽  
M. Metzger ◽  
D. Prof. Biermann
Keyword(s):  
Simulation Model ◽  
Fe Analysis ◽  
Experimental Investigations ◽  
Model Parameters ◽  
Sequential Process ◽  
Process Chain ◽  
Innovative Material

Bei der Auslegung verknüpfter Prozessschritte zur Herstellung von Bauteilen mit gezielt eingestellten Eigenschaften finden vermehrt FE-basierte Simulationssysteme Anwendung, um den Aufwand experimenteller Untersuchungen insbesondere im Hinblick auf den gesteigerten Einsatz innovativer Werkstoffkonzepte gering zu halten. Im Folgenden wird die Ausarbeitung von Konzepten zur Anpassung von Parametern zur Materialmodellierung sowie zur Verknüpfung von Einzelsimulationen der Prozesskette erläutert.   Regarding the increased application of innovative material concepts in sequential process steps for manufacturing components with tailored properties, the FE-analysis can be used to reduce the effort of experimental investigations. In this article, the development of concepts for the adjustment of simulation model parameters and the conjunction of process chain single simulations are described.

Numerical and Experimental Analysis of Self Piercing Riveting Process with Carbon Fiber-Reinforced Plastic and Aluminium Sheets

2013 ◽  
Vol 554-557 ◽  
pp. 1045-1054 ◽  
Author(s):  
Welf Guntram Drossel ◽  
Reinhard Mauermann ◽  
Raik Grützner ◽  
Danilo Mattheß
Keyword(s):  
Finite Element ◽  
Carbon Fiber ◽  
Finite Element Model ◽  
Simulation Model ◽  
Process Parameters ◽  
Process Model ◽  
Element Model ◽  
Model Parameters ◽  
Fiber Reinforced ◽  
Carbon Fiber Reinforced

In this study a numerical simulation model was designed for representing the joining process of carbon fiber-reinforced plastics (CFRP) and aluminum alloy with semi-tubular self-piercing rivet. The first step towards this goal is to analyze the piercing process of CFRP numerical and experimental. Thereby the essential process parameters, tool geometries and material characteristics are determined and in finite element model represented. Subsequently the finite element model will be verified and calibrated by experimental studies. The next step is the integration of the calibrated model parameters from the piercing process in the extensive simulation model of self-piercing rivet process. The comparison between the measured and computed values, e.g. process parameters and the geometrical connection characteristics, shows the reached quality of the process model. The presented method provides an experimental reliable characterization of the damage of the composite material and an evaluation of the connection performances, regarding the anisotropic property of CFRP.

Calibration of microscopic traffic-flow models using multiple data sources

2010 ◽  
Vol 368 (1928) ◽  
pp. 4497-4517 ◽  
Author(s):  
Serge Hoogendoorn ◽  
Raymond Hoogendoorn
Keyword(s):  
Driving Simulator ◽  
Likelihood Estimation ◽  
Joint Estimation ◽  
Data Sources ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Ratio Test ◽  
Estimation Of Parameters ◽  
Flow Models ◽  
Car Following

Parameter identification of microscopic driving models is a difficult task. This is caused by the fact that parameters—such as reaction time, sensitivity to stimuli, etc.—are generally not directly observable from common traffic data, but also due to the lack of reliable statistical estimation techniques. This contribution puts forward a new approach to identifying parameters of car-following models. One of the main contributions of this article is that the proposed approach allows for joint estimation of parameters using different data sources, including prior information on parameter values (or the valid range of values). This is achieved by generalizing the maximum-likelihood estimation approach proposed by the authors in previous work. The approach allows for statistical analysis of the parameter estimates, including the standard error of the parameter estimates and the correlation of the estimates. Using the likelihood-ratio test, models of different complexity (defined by the number of model parameters) can be cross-compared. A nice property of this test is that it takes into account the number of parameters of a model as well as the performance. To illustrate the workings, the approach is applied to two car-following models using vehicle trajectories of a Dutch freeway collected from a helicopter, in combination with data collected with a driving simulator.

The Influence of Measured Trajectory Based Model Parameters Correcting on Simulation Precision of Power System Dynamic Frequency

2013 ◽  
Vol 448-453 ◽  
pp. 2545-2550
Author(s):  
Gang Mu ◽  
Ming Li ◽  
Jun An ◽  
Xing Wei Xu ◽  
Shuai Shao
Keyword(s):  
Numerical Simulation ◽  
Simulation Model ◽  
Process Simulation ◽  
Northeast China ◽  
Operation Mode ◽  
Measured Data ◽  
Model Parameters ◽  
Conservative Operation ◽  
Generator Frequency ◽  
Dynamic Frequency

Although numerical simulation is an important method of researching dynamic frequency process, obvious deviations have been found between numerical simulation and the measured trajectory in many accidents. And the existing simulation model and parameters cannot describe the actual dynamic process of frequency accurately. Research was carried out on the influence of four parameters to the dynamic frequency process, which based on the WSCC system. The four parameters include the inertia constant of generator, generator frequency coefficient, dead band and turbine intermediate superheating coefficient. Northeast China power grid and measured data are used to verify the above research conclusion. Checking the dynamic frequency process simulation model and parameters can improve the accuracy of dynamic frequency process simulation on the base of the measured trajectory and the physical characteristics of the parameters. It can also give efficient foundation for the setting work of UFLS, overcoming the previous conservative operation mode and so on.

scholarly journals In Silico Performance of a Recellularized Tissue-Engineered Transcatheter Aortic Valve

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Christopher Noble ◽  
Joshua Choe ◽  
Susheil Uthamaraj ◽  
Milton Deherrera ◽  
Amir Lerman ◽  
...  
Keyword(s):  
Mechanical Behavior ◽  
Heart Valves ◽  
Mechanical Response ◽  
Mechanical Performance ◽  
Fe Analysis ◽  
Model Parameters ◽  
Biaxial Testing ◽  
Principal Stresses

Commercially available heart valves have many limitations, such as a lack of remodeling, risk of calcification, and thromboembolic problems. Many state-of-the-art tissue-engineered heart valves (TEHV) rely on recellularization to allow remodeling and transition to mechanical behavior of native tissues. Current in vitro testing is insufficient in characterizing a soon-to-be living valve due to this change in mechanical response; thus, it is imperative to understand the performance of an in situ valve. However, due to the complex in vivo environment, this is difficult to accomplish. Finite element (FE) analysis has become a standard tool for modeling mechanical behavior of heart valves; yet, research to date has mostly focused on commercial valves. The purpose of this study has been to evaluate the mechanical behavior of a TEHV material before and after 6 months of implantation in a rat subdermis model. This model allows the recellularization and remodeling potential of the material to be assessed via a simple and inexpensive means prior to more complex ovine orthotropic studies. Biaxial testing was utilized to evaluate the mechanical properties, and subsequently, constitutive model parameters were fit to the data to allow mechanical performance to be evaluated via FE analysis of a full cardiac cycle. Maximum principal stresses and strains from the leaflets and commissures were then analyzed. The results of this study demonstrate that the explanted tissues had reduced mechanical strength compared to the implants but were similar to the native tissues. For the FE models, this trend was continued with similar mechanical behavior in explant and native tissue groups and less compliant behavior in implant tissues. Histology demonstrated recellularization and remodeling although remodeled collagen had no clear directionality. In conclusion, we observed successful recellularization and remodeling of the tissue giving confidence to our TEHV material; however, the mechanical response indicates the additional remodeling would likely occur in the aortic/pulmonary position.

Biological activity of 24,24-difluoro-1,25-dihydroxyvitamin D3

1983 ◽  
Vol 244 (2) ◽  
pp. E159-E163
Author(s):  
S. Okamoto ◽  
Y. Tanaka ◽  
H. F. DeLuca ◽  
Y. Kobayashi ◽  
N. Ikekawa
Keyword(s):  
Biological Activity ◽  
Calcium Transport ◽  
Plasma Calcium ◽  
Epiphyseal Plate ◽  
Dihydroxyvitamin D3 ◽  
Responsive Systems ◽  
1,25 Dihydroxyvitamin D3 ◽  
Bone Calcium ◽  
Calcium And Phosphorus

The biological activity of 24,24-difluoro-1,25-dihydroxyvitamin D3 was compared with 1,25-dihydroxyvitamin D3 in the rat. The 24,24-difluoro-1,25-dihydroxyvitamin D3 has a potency of approximately 5-10 times that of 1,25-dihydroxyvitamin D3 in the known in vivo vitamin D responsive systems. These systems include intestinal calcium transport, bone calcium mobilization, calcification of epiphyseal plate cartilage, and elevation of plasma calcium and phosphorus concentrations. Thus, 24,24-difluoro-1,25-dihydroxyvitamin D3 is the first known analogue with higher potency than 1,25-dihydroxyvitamin D3 in vivo.

scholarly journals Determinants of Brain Rhythm Burst Statistics

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Arthur S. Powanwe ◽  
André Longtin
Keyword(s):  
Optimal Parameter ◽  
First Passage Time ◽  
Stochastic Averaging ◽  
Gamma Oscillations ◽  
Passage Time ◽  
Recurrent Network ◽  
Stochastic Averaging Method ◽  
Model Parameters ◽  
Brain Rhythm

AbstractBrain rhythms recorded in vivo, such as gamma oscillations, are notoriously variable both in amplitude and frequency. They are characterized by transient epochs of higher amplitude known as bursts. It has been suggested that, despite their short-life and random occurrence, bursts in gamma and other rhythms can efficiently contribute to working memory or communication tasks. Abnormalities in bursts have also been associated with e.g. motor and psychiatric disorders. It is thus crucial to understand how single cell and connectivity parameters influence burst statistics and the corresponding brain states. To address this problem, we consider a generic stochastic recurrent network of Pyramidal Interneuron Network Gamma (PING) type. Using the stochastic averaging method, we derive dynamics for the phase and envelope of the amplitude process, and find that they depend on only two meta-parameters that combine all the model parameters. This allows us to identify an optimal parameter regime of healthy variability with similar statistics to those seen in vivo; in this regime, oscillations and bursts are supported by synaptic noise. The probability density for the rhythm’s envelope as well as the mean burst duration are then derived using first passage time analysis. Our analysis enables us to link burst attributes, such as duration and frequency content, to system parameters. Our general approach can be extended to different frequency bands, network topologies and extra populations. It provides the much needed insight into the biophysical determinants of rhythm burst statistics, and into what needs to be changed to correct rhythms with pathological statistics.

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