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However, the potential implications of these extended methods in realistic scenarios have not previously been systematically explored.
An important drawback of this evaluation framework is that it focuses on a very limited number of regions (10), which results in very little discrimination between most methods in realistic settings.
The ability to estimate parameters of sequence evolution with realistic models, and then computationally evolve sequences using these fitted parameters is crucial to better characterize the behavior of reconstruction methods in realistic settings.
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Molecular mechanics and molecular dynamics techniques are also used to produce atomistic models independently from any experimental input, thereby providing a test of the viability of the reverse Monte Carlo method in generating realistic models for amorphous polymeric systems.
As a preliminary application, we assess the performance of the method in a realistic model the FCC phase of dense atomic hydrogen, in which the calculated result shows that the acceleration rate is about 3 to 4-fold for a two-level implementation, and can be increased up to 10 times if extrapolation is used.
Next, an ODE model of cellular signal transduction has been used to illustrate our method in a realistic setting.
The above results show the performance of the rescoring method in a realistic structure prediction setup similar to, e.g. the CASP assessment.
A cadaver of a 250-g female Wistar rat was used to test our method in a realistic complex attenuation distribution.
Thus, the method is applicable in realistic situations where using only the D* does not seem a viable alternative.
We finally perform a small simulation study to explore the properties of the proposed methods in a more realistic setting with three covariates (two binary and one continuous) and a continuous outcome.
Despite advances in the development of approximate methods, spatial stochastic simulation in realistic geometries is still challenging.
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