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This paper concerns the application of formal methods to biological systems, modeled specifically in BioAmbients, a variant of the Mobile Ambients calculus.
The present decade has witnessed the rapid shift in synthesis strategies from physicochemical methods to biological agents such as bacteria [7], fungi [8] and plants [9, 10] for the synthesis of nanoparticles.
However, when applying machine learning methods to biological data, the advantages of parsimony are less clear.
Table 3 shows that the application of bioinformatics methods to biological problems is the most common method for integrating bioinformatics and biology.
Furthermore, the sensitivity of these methods to biological variability or measurement noise in the provided profile for h n remains unclear and has never been tested.
Applying QM methods to biological problems brings an improved representation to these systems by the direct inclusion of inherently QM effects such as polarization and charge transfer.
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Furthermore, l-MTX was determined in a cell culture extract as a proof-of-principle experiment to show the applicability of the method to biological samples.
Finally, as a proof-of-principle experiment for the application of the developed method to biological samples, l-MTX was determined in a cell culture extract from human CEM leukemia cells.
His work as a cell biologist has centered on genetics and the use of genetic methods to understand biological functions.
There is a growing interest in improved test methods to assess biological effects of nanoparticles.
This work demonstrates the use of statistical methods to design biological decontamination experiments at the limits of decontamination technology and to analyze the resultant data.
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