This chapter discusses the basics of modeling data, using the basic application objects and the powerful collection classes available in both Cocoa and Carbon such as dictionaries.
The effects of key modeling assumptions and data used in the calculations are presented and discussed.
Detailed information about the modeling approach and the data used can be found in Lehmann (2013).
Our results emphasize the need for sensitivity analysis to examine model construction and forms of biological data used for modeling transcriptional processes, in order to determine the significance of estimated parameter values for thermodynamic models.
In addition to awareness of the limitations of data used in modeling, acknowledgment of the limitations of the models themselves is also important.
We first check whether the model obtained by constraint-based modeling method can reproduce the experimental data used for parameter estimation, which can give us the information about quality of "in-sample fit" (how good is the model fitting the data used for parameter estimation).
Table 2 Summary of grid data used in static modeling Cell dimensions (in feet) Length 400 Width 400 Thickness 20 Number of cells in the X-direction 20 Number of cells in the Y-direction 20 Number of cells in the Z-direction 5 Total number of cells 2000.
The input data used in our modeling process are the dimensions (i.e. diameter and compaction) of the chromatin fiber.
Adjusted coefficient of determination (R -square) values are also listed together with the number of data used in the modeling process at each station.
Using our recently developed multi-trait GWAS method, we can detour complicated multivariate modeling for binary data using single-trait GWAS summary statistics, i.e. genome-wide effects estimates (log odds-ratios) and standard errors for each of the hypersensitive response traits.
