scholarly journals A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

2018 ◽  
Vol 9 ◽  
Author(s):  
Osvaldo D. Kim ◽  
Miguel Rocha ◽  
Paulo Maia
Keyword(s):  
Metabolic Engineering ◽  
Dynamic Modeling ◽  
Strain Optimization ◽  
Modeling Approaches

Empirical Dynamic Modeling

2020 ◽  
pp. 251-268
Author(s):  
Michael J. Fogarty ◽  
Jeremy S. Collie
Keyword(s):  
Dynamic Modeling ◽  
Nearest Neighbor ◽  
Wide Spectrum ◽  
Species Abundance ◽  
Mechanistic Modeling ◽  
Parametric Models ◽  
Ecological Processes ◽  
Regression Methods ◽  
Modeling Approaches ◽  
Dynamical Behaviors

Empirical Dynamic Modeling offers a flexible complement to standard mechanistic modeling approaches. Because it makes no assumptions concerning the structural form of ecological processes, it can provide an effective approach to dealing with model uncertainty. The method uses non-linear, non-parametric models. It can accommodate a wide spectrum of dynamical behaviors and makes no equilibrium assumptions. The approach is predicated on the idea that within a time series of observations of an ecological variable (e.g. population or species abundance) is encoded information on the factors that have affected that variable over time (e.g. the effects of predators or prey, competitors, environmental change, etc.). The method employs state-space reconstruction to decode this embedded information, and applies nearest-neighbor and kernel regression methods of forecasting. Forecast skill is used directly as a criterion for model selection and validation. It has been proven effective in application to fisheries forecasting problems, often outperforming standard modeling approaches.

scholarly journals Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches

Epidemics ◽  
2013 ◽  
Vol 5 (4) ◽  
pp. 197-207 ◽  
Author(s):  
Marisa C. Eisenberg ◽  
Gregory Kujbida ◽  
Ashleigh R. Tuite ◽  
David N. Fisman ◽  
Joseph H. Tien
Keyword(s):  
Dynamic Modeling ◽  
Modeling Approaches

scholarly journals VA2 GATHERING INFORMATION BY COMPARISON OF DIFFERENT DYNAMIC MODELING APPROACHES FOR EPIDEMIC MODELS

2010 ◽  
Vol 13 (7) ◽  
pp. A250
Author(s):  
G Zauner ◽  
N Popper ◽  
F Miksch ◽  
C Urach ◽  
P Einzinger ◽  
...  
Keyword(s):  
Dynamic Modeling ◽  
Epidemic Models ◽  
Modeling Approaches

A PHILOSOPHICAL FOUNDATION FOR UNIFICATION OF DYNAMIC MODELING METHODS BASED ON HIGHER-ORDER POTENTIALITIES AND THEIR REDUCERS

2008 ◽  
Vol 11 (06) ◽  
pp. 831-860 ◽  
Author(s):  
TIBOR BOSSE ◽  
JAN TREUR
Keyword(s):  
Cognitive Science ◽  
Dynamic Modeling ◽  
Higher Order ◽  
Philosophical Foundation ◽  
Modeling Methods ◽  
Modeling Approaches ◽  
Model Dynamics ◽  
Philosophical Framework

In the development of disciplines addressing dynamics, a major role was played by the assumption that processes can be modeled by introducing state properties, called potentialities, anticipating in which respect a next state will be different. A second assumption often made is that these state properties can be related to other state properties, called reducers. This paper proposes a philosophical framework in terms of potentialities and their reducers, which can be used to obtain a common philosophical foundation for methods in AI, cognitive science and beyond to model dynamics. Based on this framework a metamodel for dynamic modeling approaches is described. The philosophical framework and the metamodel together provide a unified foundation for numerical, symbolic, and hybrid dynamic modeling approaches used in a large variety of disciplines.

Multivariate modular metabolic engineering for pathway and strain optimization

2014 ◽  
Vol 29 ◽  
pp. 156-162 ◽  
Author(s):  
Bradley Walters Biggs ◽  
Brecht De Paepe ◽  
Christine Nicole S Santos ◽  
Marjan De Mey ◽  
Parayil Kumaran Ajikumar
Keyword(s):  
Metabolic Engineering ◽  
Strain Optimization
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