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The model was first calibrated with control irrigation plus 150 kg N ha−1 treatment (I1N3), then evaluated (2012) and validated (2013) by utilizing experimental data.
Estimation of kinetic parameters is an important and difficult step in the development of models, but calculations of unknown kinetic parameters can be achieved by utilizing experimental data and model-based technique.
As discussed above, utilizing experimental data is essential to extract relevant biological conclusions regarding the particular scenario being modeled.
Upon utilizing experimental data for VEGF receptor density and accounting for even distribution of receptors on the abluminal and luminal endothelial surfaces, we found that the free VEGF in both the normal tissue and tumor decreased 3.2- and 1.1-fold, respectively, as compared to Baseline Model 2. Lastly, we investigated the effect of parenchymal receptors on the concentration of free VEGF.
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In contrast to most of the existing models, no curve-fitting is required, as the kinetic rate equation utilizes experimental data directly.
These methods utilize experimental data (lysimeter, river streamflow, groundwater-table variations) as well as soil water-balance and physoil water-balanceling concepts.
To train the neural network of the TCC, this research utilized experimental data reported by previous researchers from the 1980's to the 2000's.
In this paper, we adapt the model to utilize experimental data by including information on different drug concentrations.
The model will utilize experimental data on the types of mutants that arise in the context of different treatments.
Moreover, for the TF binding site predictions, we utilized experimental data retrieved from ChIP-Seq experiments.
All p-values lower than 1.0e-20 1.0e-20t to 0. In addition to HCNEs, weretilized experimental data desetibing toe location of the transcriptional co-activator p30. to analyze the enhancer densIny additiontomiRNAs.
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