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The authors show how the combination of the two modeling approaches enables to reveal specific relationships between the feedback structure and the dynamic behavior of the system.
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Finally, modeling approaches enable testable predictions regarding the role of non-genetic factors (noise, mechano-elastic forces, etc). in development.
The discrete modeling approach enables the modeler to model the containers as passive objects with a predefined behavior and to visualize the energy demand per second, as time continues on the basis of the occurrence of events.
The constraint-based modeling approach enables the development of genome-scale in silico models that can predict the behavior of complex biological systems and their responses to the environments.
Our multipronged modeling approach enables simultaneous monitoring and optimization of thermodynamic, economic and environmental performance.
The proposed modeling approach enables us to solve real-world problem instances with thousands of scheduled trips by direct application of standard optimization software.
A new numerical modeling approach enables us to calculate the spatial and temporal development of chemical water rock gas interactions, including scaling in oil reservoirs which undergo seawater injection.
Identification of the mechanism of chemical coupling of a phosphate ester monomer to zirconia via a computational modeling approach enables materials scientists to design new coupling agents that can resist hydrolytic degradation of bonds made by methacrylate resins to zirconia.
In particular, our modeling approach enables distinguishing between plastic and evolutionary responses to fishing.
This translational modeling approach enables the ability to select the optimal drug combinations and optimal dosing regimen for further exploration.
In the next three sections, we present examples of how our modeling approach enables analysis of methylation dynamics across cellular differentiations.
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