This paper describes a software package, Polynome, offered as a web service, that helps users construct Boolean network models based on experimental data and biological input.
The input data is based on experimental results or inverse fitting, with a clearly identification and justification.
Uncertainty quantification of input parameters are presented first based on experimental data, inspection capability, and geometric reasoning.
The top-ranked candidate genes were used as input, and protein-protein interactions based on experimental evidence, and functional groupings were selected as evidence.
An input-output model is proposed, based on experimental data, and then a control algorithm is used to obtain imposed performances for the closed loop system.
The empirical relation is correlated with the input parameters in the present system based on experimental results.
Mathematical correlations based on experimental findings for important scale-up parameters as, for example, the power input and the gas/liquid mass transfer are discussed in detail.
ANOVA was used to analyze the statistical model for the relationship between independent variables as inputs and dependent variables as responses and their interactions based on experimental data while ignoring some outliers.
Rather than querying the database based on experimental characteristics, it is possible to perform a BLAST search for a user-input protein sequence, or sequence fragment, against the entire EPICDB.
However, approaches based on experimental properties, such as water solubility or octanol-water distribution coefficient, are prone to experimental errors in the input variables, which may result in some severe statistical problems (Lohninger, 1994; Nguyen et al., 2005; Sabljic, 1991; Sabljic and Piver, 1992).
Instead, Dr. Pines and his collaborators have devised a more complicated model based on experimental observations.
