Conceptual models of entrainment, jet-lag, and seasonality

ABSTRACTUnderstanding entrainment of circadian rhythms is a central goal of chronobiology. Many factors, such as period, amplitude, Zeitgeber strength, and day-length, govern entrainment ranges and the phase of entrainment. Using global optimization, we derive conceptual models with just three free parameters (period, amplitude, relaxation rate) that reproduce known phenotypic features of vertebrate clocks: relatively small phase response curves (PRCs), fast re-entrainment after jet-lag, and seasonal variability to track light onset or offset. Since optimization found multiple sets of model parameters, we can study this model ensemble to gain insight into the underlying design principles. We find that amplitudes control the size of PRCs, that fast relaxation supports short jet-lag, and that specific periods allow reasonable seasonal phase shifts. Arnold onions of representative models visualize strong dependencies of entrainment on periods, relative Zeitgeber strength, and photoperiod.

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Speed vs. comfort: Testing the efficacy of a single question for predicting transit users’ route choices

10.31234/osf.io/drj2a ◽

2019 ◽

Author(s):

Joseph John Pyne Simons ◽

Ilya Farber

Keyword(s):

Data Collection ◽

Efficient Method ◽

Choice Task ◽

The Self ◽

Self Report ◽

Model Parameters ◽

Single Item ◽

Alternative Approach ◽

Single Question ◽

Insight Into

Not all transit users have the same preferences when making route decisions. Understanding the factors driving this heterogeneity enables better tailoring of policies, interventions, and messaging. However, existing methods for assessing these factors require extensive data collection. Here we present an alternative approach - an easily-administered single item measure of overall preference for speed versus comfort. Scores on the self-report item predict decisions in a choice task and account for a proportion of the differences in model parameters between people (n=298). This single item can easily be included on existing travel surveys, and provides an efficient method to both anticipate the choices of users and gain more general insight into their preferences.

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Complexity versus simplicity: an example of groundwater model ranking with the Akaike Information Criterion

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-9687-2012 ◽

2012 ◽

Vol 9 (8) ◽

pp. 9687-9714 ◽

Cited By ~ 6

Author(s):

I. Engelhardt ◽

J. G. De Aguinaga ◽

H. Mikat ◽

C. Schüth ◽

O. Lenz ◽

...

Keyword(s):

Akaike Information Criterion ◽

Conceptual Models ◽

Information Criterion ◽

Model Parameters ◽

Groundwater Model ◽

Piezometric Head ◽

Simulated Groundwater ◽

Pumping Rates ◽

Uncalibrated Model ◽

Piezometric Heads

Abstract. A groundwater model characterized by a lack of field data to estimate hydraulic model parameters and boundary conditions combined with many piezometric head observations was investigated concerning model uncertainty. Different conceptual models with a stepwise increase from 0 to 30 adjustable parameters were calibrated using PEST. Residuals, sensitivities, the Akaike Information Criterion (AIC), and the likelihood of each model were computed. As expected, residuals and standard errors decreased with an increasing amount of adjustable model parameters. However, the model with only 15 adjusted parameters was evaluated by AIC as the best option with a likelihood of 98%, while the uncalibrated model obtained the worst AIC value. Computing of the AIC yielded the most important information to assess the model likelihood. Comparing only residuals of different conceptual models was less valuable and would result in an overparameterization of the conceptual model approach. Sensitivities of piezometric heads were highest for the model with five adjustable parameters reflecting also changes of extracted groundwater volumes. With increasing amount of adjustable parameters piezometric heads became less sensitive for the model calibration and changes of pumping rates were no longer displayed by the sensitivity coefficients. Therefore, when too many model parameters were adjusted, these parameters lost their impact on the model results. Additionally, using only sedimentological data to derive hydraulic parameters resulted in a large bias between measured and simulated groundwater level.

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Testing parallelism for the four-parameter logistic model with D-optimal design

Model Assisted Statistics and Applications ◽

10.3233/mas-200494 ◽

2020 ◽

Vol 15 (3) ◽

pp. 273-284

Author(s):

Lin Ying ◽

Hyun Seung Won

Keyword(s):

Optimal Design ◽

Logistic Model ◽

Test Preparation ◽

Test Sample ◽

Optimal Designs ◽

Model Parameters ◽

Reference Standard ◽

Response Curves ◽

Classical Design ◽

Intersection Union Test

In order to determine the potency of the test preparation relative to the standard preparation, it is often important to test parallelism between a pair of dose-response curves of reference standard and test sample. Optimal designs are known to be more powerful in testing parallelism as compared to classical designs. In this study, D-optimal design was implemented to study the parallelism and compare+ its performance with a classical design. We modified D-optimal design to test the parallelism in the four-parameter logistic (4PL) model using Intersection-Union Test (IUT). IUT method is appropriate when the null hypothesis is expressed as a union of sets, and by using this method complicated tests involving several parameters are easily constructed. Since D-optimal design minimizes the variances of model parameters, it can bring more power to the IUT test. A simulation study will be presented to compare the empirical properties of the two different designs.

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Mathematical modeling of microtubule dynamic instability: new insight into the link between gtp-hydrolysis and microtubule aging

ESAIM Mathematical Modelling and Numerical Analysis ◽

10.1051/m2an/2017025 ◽

2018 ◽

Vol 52 (6) ◽

pp. 2433-2456 ◽

Cited By ~ 2

Author(s):

Ayuna Barlukova ◽

Diana White ◽

Gérard Henry ◽

Stéphane Honoré ◽

Florence Hubert

Keyword(s):

Dynamic Instability ◽

Dynamic Properties ◽

Model Parameters ◽

Microtubule Dynamic ◽

Gtp Hydrolysis ◽

Unique Type ◽

Microtubule Targeting Agents ◽

Age Dependent ◽

Type Of Behavior ◽

Insight Into

Microtubules (MTs) are protein polymers that exhibit a unique type of behavior referred to as dynamic instability. That is, they undergo periods of growth (through the addition of GTP-tubulin) and shortening (through the subtraction of GDP-tubulin). Shortening events are very fast, where this transition is referred to as a catastrophe. There are many processes that regulate MT dynamic instability, however, recent experiments show that MT dynamics may be highly regulated by a MTs age, where young MTs are less likely to undergo shortening events than older ones. In this paper, we develop a novel modeling approach to describe how the age of a MT affects its dynamic properties. In particular, we extend on a previously developed model that describes MT dynamics, by proposing a new concept for GTP-tubulin hydrolysis (the process by which newly incorporated GTP-tubulin is hydrolyzed to lower energy GDP-tubulin). In particular, we assume that hydrolysis is mainly vectorial, age-dependent and delayed according to the GTP-tubulin incorporation into the MT. Through numerical simulation, we are able to show how MT age affects certain properties that define MT dynamics. For example, simulations illustrate how the aging process leads to an increase in the rate of GTP-tubulin hydrolysis for older MTs, as well as increases in catastrophe frequency. Also, since it has been found that MT dynamic instability is affected by chemotherapy microtubule-targeting agents (MTAs), we highlight the fact that our model can be used to investigate the action of MTAs on MT dynamics by varying certain model parameters.

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Cyclical Granulopoiesis in Chronic Granulocytic Leukemia: A Simulation Study

Blood ◽

10.1182/blood.v43.3.379.379 ◽

1974 ◽

Vol 43 (3) ◽

pp. 379-387 ◽

Cited By ~ 22

Author(s):

T. E. Wheldon ◽

J. Kirk ◽

Helen M. Finlay

Keyword(s):

Mathematical Model ◽

Model Parameters ◽

Causal Association ◽

Maturation Time ◽

Chronic Granulocytic Leukemia ◽

Ample Evidence ◽

Delayed Maturation ◽

Kinetics Of ◽

Granulocytic Leukemia ◽

Insight Into

Abstract There exists ample evidence that granulopoiesis is an actively controlled process. The observation of cyclical granulopoiesis in chronic granulocytic leukemia (CGL) suggests that control is deranged rather than abolished in this disorder. Analysis of the kinetics of granulopoiesis in CGL may provide some insight into the nature of the derangement. To facilitate analysis, a mathematical model of the granulopoietic control system is proposed and examined using computer simulation. With model parameters initially chosen to represent normal granulopoiesis, the minimal changes necessary to represent granulopoiesis in CGL were investigated. This analysis indicates that two separate changes seem to be required: (1) the granulocyte maturation time must be increased and (2) the precursor input to the granulocytic pathway of development must also be increased. A causal association between delayed maturation and rising precursor input is suggested, and some possible mechanisms of association are proposed.

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A mathematical model of metabolic insulin signaling pathways

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.00571.2001 ◽

2002 ◽

Vol 283 (5) ◽

pp. E1084-E1101 ◽

Cited By ~ 124

Author(s):

Ahmad R. Sedaghat ◽

Arthur Sherman ◽

Michael J. Quon

Keyword(s):

Experimental Data ◽

Mathematical Model ◽

Insulin Receptor ◽

Signaling Pathways ◽

Insulin Signaling ◽

Molecular Mechanisms ◽

Glucose Transporter ◽

Insulin Dose ◽

Model Parameters ◽

Insight Into

We develop a mathematical model that explicitly represents many of the known signaling components mediating translocation of the insulin-responsive glucose transporter GLUT4 to gain insight into the complexities of metabolic insulin signaling pathways. A novel mechanistic model of postreceptor events including phosphorylation of insulin receptor substrate-1, activation of phosphatidylinositol 3-kinase, and subsequent activation of downstream kinases Akt and protein kinase C-ζ is coupled with previously validated subsystem models of insulin receptor binding, receptor recycling, and GLUT4 translocation. A system of differential equations is defined by the structure of the model. Rate constants and model parameters are constrained by published experimental data. Model simulations of insulin dose-response experiments agree with published experimental data and also generate expected qualitative behaviors such as sequential signal amplification and increased sensitivity of downstream components. We examined the consequences of incorporating feedback pathways as well as representing pathological conditions, such as increased levels of protein tyrosine phosphatases, to illustrate the utility of our model for exploring molecular mechanisms. We conclude that mathematical modeling of signal transduction pathways is a useful approach for gaining insight into the complexities of metabolic insulin signaling.

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Solids Flow Models for Gas - Flowing Solids - Fixed Bed Contactors

International Journal of Chemical Reactor Engineering ◽

10.2202/1542-6580.2228 ◽

2010 ◽

Vol 8 (1) ◽

Author(s):

Nikola M Nikacevic ◽

Milorad P. Dudukovic

Keyword(s):

Dispersion Model ◽

Fixed Bed ◽

Axial Dispersion ◽

Step Response ◽

Model Parameters ◽

Response Curves ◽

Flow Models ◽

Solids Holdup ◽

Axial Dispersion Model ◽

Static Zone

Three solids flow models for gas flowing solids fixed bed contactors are analyzed. They all presume axial dispersion in the dynamic, freely flowing zone, but they differ in the interpretation of the stagnant zone. The models have been examined and the model parameters have been optimized on the basis of two types of tracer experiments. One provides step response curves for flowing solids at the exit and the other presents the response curves of the static flowing solids holdup. The model which assumes axial dispersion and exchange between dynamic and two active static zones, most accurately describes the solids flow pattern. A simpler model which presumes exchange between dynamic and one static zone can be used if there is no need for a precise description of the behavior of stagnant particles. The most simple axial dispersion model is not realistic, as it does not explain stagnancy at all, which was experimentally observed for the gas flowing solids fixed bed contactors.

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Accounting for relaxation during pulse effects for long pulses and fast relaxation times in surface nuclear magnetic resonance

Geophysics ◽

10.1190/geo2016-0567.1 ◽

2017 ◽

Vol 82 (6) ◽

pp. JM23-JM36 ◽

Cited By ~ 13

Author(s):

Denys Grombacher ◽

Ahmad A. Behroozmand ◽

Esben Auken

Keyword(s):

Nuclear Magnetic Resonance ◽

Magnetic Resonance ◽

Water Content ◽

Relaxation Times ◽

Initial Amplitude ◽

Fast Relaxation ◽

Direct Modeling ◽

Geophysical Technique ◽

Insight Into ◽

Nuclear Magnetic

Surface nuclear magnetic resonance (NMR) is a geophysical technique providing noninvasive insight into aquifer properties. To ensure that reliable water content estimates are produced, accurate modeling of the excitation process is necessary. This requires that relaxation during pulse (RDP) effects be accounted for because they may lead to biased water content estimates if neglected. In surface NMR, RDP is not directly included into the excitation modeling, rather it is accounted for by adjusting the time at which the initial amplitude of the signal is calculated. Previous work has demonstrated that estimating the initial amplitude of the signal as the value obtained by extrapolating the observed signal to the middle of the pulse can greatly improve performance for the on-resonance pulse. To better understand the reliability of these types of approaches (which do not directly include RDP in the modeling), the performance of these approaches is tested using numerical simulations for a broad range of conditions, including for multiple excitation pulse types. Hardware advances that now allow the routine measurement of much faster relaxation times (where these types of approaches may lead to poor water content estimates) and a recent desire to use alternative transmit schemes demand a flexible protocol to account for RDP effects in the presence of fast relaxation times for arbitrary excitation pulses. To facilitate such a protocol, an approach involving direct modeling of RDP effects using estimates of the subsurface relaxation times is presented to provide more robust and accurate water content estimates under conditions representative of surface NMR.

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Multi-model ensemble hydrologic prediction and uncertainties analysis

Proceedings of the International Association of Hydrological Sciences ◽

10.5194/piahs-364-249-2014 ◽

2014 ◽

Vol 364 ◽

pp. 249-254 ◽

Cited By ~ 1

Author(s):

S. Jiang ◽

L. Ren ◽

X. Yang ◽

M. Ma ◽

Y. Liu

Keyword(s):

Parameter Optimization ◽

Optimization Algorithms ◽

Model Parameters ◽

Model Ensemble ◽

Streamflow Prediction ◽

Parameter Uncertainties ◽

Input Uncertainty ◽

Daily Streamflow ◽

Shuffled Complex Evolution ◽

Modelling Uncertainties

Abstract. Modelling uncertainties (i.e. input errors, parameter uncertainties and model structural errors) inevitably exist in hydrological prediction. A lot of recent attention has focused on these, of which input error modelling, parameter optimization and multi-model ensemble strategies are the three most popular methods to demonstrate the impacts of modelling uncertainties. In this paper the Xinanjiang model, the Hybrid rainfall–runoff model and the HYMOD model were applied to the Mishui Basin, south China, for daily streamflow ensemble simulation and uncertainty analysis. The three models were first calibrated by two parameter optimization algorithms, namely, the Shuffled Complex Evolution method (SCE-UA) and the Shuffled Complex Evolution Metropolis method (SCEM-UA); next, the input uncertainty was accounted for by introducing a normally-distributed error multiplier; then, the simulation sets calculated from the three models were combined by Bayesian model averaging (BMA). The results show that both these parameter optimization algorithms generate good streamflow simulations; specifically the SCEM-UA can imply parameter uncertainty and give the posterior distribution of the parameters. Considering the precipitation input uncertainty, the streamflow simulation precision does not improve very much. While the BMA combination not only improves the streamflow prediction precision, it also gives quantitative uncertainty bounds for the simulation sets. The SCEM-UA calculated prediction interval is better than the SCE-UA calculated one. These results suggest that considering the model parameters' uncertainties and doing multi-model ensemble simulations are very practical for streamflow prediction and flood forecasting, from which more precision prediction and more reliable uncertainty bounds can be generated.

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