scholarly journals Grammatical Immune System Evolution for Reverse Engineering Nonlinear Dynamic Bayesian Models

2008 ◽  
Vol 6 ◽  
pp. CIN.S694 ◽  
Author(s):  
B.A. McKinney ◽  
D. Tian
Keyword(s):  
Time Series ◽  
Immune System ◽  
Nonlinear Dynamic ◽  
Dynamic Models ◽  
Time Series Data ◽  
Nonlinear System Identification ◽  
Series Data ◽  
Estrogen Metabolism ◽  
System Evolution ◽  
Immune System Evolution

An artificial immune system algorithm is introduced in which nonlinear dynamic models are evolved to fit time series of interacting biomolecules. This grammar-based machine learning method learns the structure and parameters of the underlying dynamic model. In silico immunogenetic mechanisms for the generation of model-structure diversity are implemented with the aid of a grammar, which also enforces semantic constraints of the evolved models. The grammar acts as a DNA repair polymerase that can identify recombination and hypermutation signals in the antibody (model) genome. These signals contain information interpretable by the grammar to maintain model context. Grammatical Immune System Evolution (GISE) is applied to a nonlinear system identification problem in which a generalized (nonlinear) dynamic Bayesian model is evolved to fit biologically motivated artificial time-series data. From experimental data, we use GISE to infer an improved kinetic model for the oxidative metabolism of 17β-estradiol (E2), the parent hormone of the estrogen metabolism pathway.

scholarly journals Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data

2020 ◽  
Vol 23 (4) ◽  
pp. 607-619 ◽  
Author(s):  
Matthew P. Adams ◽  
Scott A. Sisson ◽  
Kate J. Helmstedt ◽  
Christopher M. Baker ◽  
Matthew H. Holden ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Time Series Data ◽  
Ecological Networks ◽  
Series Data ◽  
Management Decisions ◽  
Noisy Time Series

scholarly journals Evaluating the within-host dynamics of Ranavirus infection with mechanistic disease models and experimental data

2019 ◽  
Author(s):  
Joseph R. Mihaljevic ◽  
Amy L. Greer ◽  
Jesse L. Brunner
Keyword(s):  
Time Series ◽  
Immune System ◽  
Model Comparison ◽  
Time Series Data ◽  
Formal Model ◽  
Immune Defense ◽  
Series Data ◽  
System Response ◽  
Mechanistic Models ◽  
Viral Titer

AbstractMechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen population growth and immune system processes) and among-host dynamics (i.e., transmission). Rarely, however, have within-host models been synthesized with data to infer processes, validate hypotheses, or generate new theories. In this study we use mechanistic models and empirical, time-series data of viral titer to better understand the growth of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral growth and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: the viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, that can be validated in future studies.

scholarly journals Evaluating the Within-Host Dynamics of Ranavirus Infection with Mechanistic Disease Models and Experimental Data

Viruses ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 396 ◽  
Author(s):  
Joseph R. Mihaljevic ◽  
Amy L. Greer ◽  
Jesse L. Brunner
Keyword(s):  
Experimental Data ◽  
Time Series ◽  
Immune System ◽  
Viral Replication ◽  
Time Series Data ◽  
Immune Defense ◽  
Series Data ◽  
System Response ◽  
Mechanistic Models ◽  
Viral Titer

Mechanistic models are critical for our understanding of both within-host dynamics (i.e., pathogen replication and immune system processes) and among-host dynamics (i.e., transmission). Within-host models, however, are not often fit to experimental data, which can serve as a robust method of hypothesis testing and hypothesis generation. In this study, we use mechanistic models and empirical, time-series data of viral titer to better understand the replication of ranaviruses within their amphibian hosts and the immune dynamics that limit viral replication. Specifically, we fit a suite of potential models to our data, where each model represents a hypothesis about the interactions between viral replication and immune defense. Through formal model comparison, we find a parsimonious model that captures key features of our time-series data: The viral titer rises and falls through time, likely due to an immune system response, and that the initial viral dosage affects both the peak viral titer and the timing of the peak. Importantly, our model makes several predictions, including the existence of long-term viral infections, which can be validated in future studies.

Human fetuses have nonlinear cardiac dynamics

1999 ◽  
Vol 87 (2) ◽  
pp. 530-537 ◽  
Author(s):  
Lynn J. Groome ◽  
Donna M. Mooney ◽  
Scherri B. Holland ◽  
Lisa A. Smith ◽  
Jana L. Atterbury ◽  
...  
Keyword(s):  
Time Series ◽  
Dynamic Models ◽  
Time Series Data ◽  
Low Risk ◽  
Series Data ◽  
Correlated Noise ◽  
Human Fetuses ◽  
Original Time Series ◽  
Uncorrelated Noise ◽  
Original Time

Approximate entropy (ApEn) is a statistic that quantifies regularity in time series data, and this parameter has several features that make it attractive for analyzing physiological systems. In this study, ApEn was used to detect nonlinearities in the heart rate (HR) patterns of 12 low-risk human fetuses between 38 and 40 wk of gestation. The fetal cardiac electrical signal was sampled at a rate of 1,024 Hz by using Ag-AgCl electrodes positioned across the mother’s abdomen, and fetal R waves were extracted by using adaptive signal processing techniques. To test for nonlinearity, ApEn for the original HR time series was compared with ApEn for three dynamic models: temporally uncorrelated noise, linearly correlated noise, and linearly correlated noise with nonlinear distortion. Each model had the same mean and SD in HR as the original time series, and one model also preserved the Fourier power spectrum. We estimated that noise accounted for 17.2–44.5% of the total between-fetus variance in ApEn. Nevertheless, ApEn for the original time series data still differed significantly from ApEn for the three dynamic models for both group comparisons and individual fetuses. We concluded that the HR time series, in low-risk human fetuses, could not be modeled as temporally uncorrelated noise, linearly correlated noise, or static filtering of linearly correlated noise.

scholarly journals Pulsation Properties of Hydrodynamic Models with Dimensional Analysis

1993 ◽  
Vol 139 ◽  
pp. 204-205
Author(s):  
Toshiki Aikawa
Keyword(s):  
Time Series ◽  
Light Curve ◽  
Dimensional Analysis ◽  
Dynamic Models ◽  
Time Series Data ◽  
Series Data ◽  
Hydrodynamic Models ◽  
Stellar Envelopes

AbstractWe demonstrate that the dimension deduced from time series data of hydro-dynamic models for chaotic pulsation is a function of luminosity. The dimension is proposed as a good quantity to guess stellar parameters and the physics of stellar envelopes like as the pulsation periods and light curve shapes used for regular variables.

FITTING POPULATION DYNAMIC MODELS TO TIME-SERIES DATA BY GRADIENT MATCHING

Ecology ◽  
2002 ◽  
Vol 83 (8) ◽  
pp. 2256-2270 ◽  
Author(s):  
Stephen P. Ellner ◽  
Yodit Seifu ◽  
Robert H. Smith
Keyword(s):  
Time Series ◽  
Population Dynamic ◽  
Dynamic Models ◽  
Time Series Data ◽  
Series Data
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