Sentence examples for biophysical simulations from inspiring English sources

Currently, biophysical simulations from DCM constitute a serious computational hindrance.

By combining biophysical simulations of coarse-grained proteins and analysis of proteins-complex crystallographic structures, we seek to elucidate those mechanisms.

In this paper, we extend the applicability of REST2 for quantitative biophysical simulations through a robust and generic implementation in greatly scalable MD software NAMD.

Biophysical simulations were used to estimate rCBF and BOLD responses as functions of (a) a prior stimulus, (b) epoch length (for a fixed SOA), (c) SOA (for a fixed number of events), and (d) stimulus amplitude.

Indeed, using ad hoc software and current computational capabilities, it is now possible to predict substrate access to the active site in biophysical simulations, and electron transfer efficiency in biochemical simulations, reducing in orders of magnitude the time of experimental work in oxidoreductase screening and engineering.

For the biophysical simulations, namely induced-fit docking and substrate migration, we used the state-of-the-art Protein Energy Landscape Exploration server (PELE) [ 70].

For this purpose, Modern Portfolio Theory and risk simulation were coupled with the process-based biophysical simulation model WaNuLCAS 4.0.

The biophysical simulation model exemplified by the APSIM model explained well the observed changes in SOC at the various locations without specifically fitting the model to the observed data, despite large variation in the calculated data.

The methodology is based on biophysical simulation of crop growth and nitrogen leaching (WAVE) in combination with economic optimisation (linear programming) and enables a normative environmental-economic evaluation of site specific N-management to be made.

This first quantitative description of fodder beet canopy forming processes can assist the development of predictive approaches, such as biophysical simulation models, for application in research, extension and teaching.

The quantitative understanding of canopy formation processes under constraining growth conditions is an essential step for the development of biophysical simulation models of new crops for research and predictive purposes.