Comparative study of three commonly used continuous deterministic methods for modeling gene regulation networks

BMC Bioinformatics

Table 5 Parameter matrix characteristics

	Case A			Case B			Case C
Data generation method	ANN	SS	GRLOT	ANN	SS	GRLOT	ANN	SS	GRLOT
Uniform degradation rate	x - -	x x x	x x x	x - -	x x x	x x x	x x x	x x x	x x x
Constant signal propagation	x - -	- x x	- x x
Asymmetric signal branching				x x x	- x x	x x x	x - -	- x x	- x x
Asymmetric co-regulation				- - -	- x -	- - -	- - -	x x -	- - -
Negative feedback							x x x	x x x	x x x
Positive feedback							x x -	x x x	x x x
Uniform degradation rate	x - -	x x x	x x x	x x x	x x x	x x x	x x x	x x x	x x x
Constant signal propagation	x - -	- x x	- x x
Asymmetric signal branching				x x x	- x x	x x x	x - -	x x -	x x x
Asymmetric co-regulation				x - x	x x x	x - x	x - -	x x -	x x x
Negative feedback							x x x	x x x	x x x
Positive feedback							x x x	x x x	x x x

Characteristics found in the parameter matrices of the reverse-engineered network models: Characteristics corresponding to the sparse data sets are depicted in the top rows of the table, those corresponding to the detailed data sets in the bottom rows. The symbol "x" shows that the feature was discovered by the method, "-" its was not discovered, and spaces indicate that the feature was not part of the case study. The reverse-engineering methods applied to each data set are in the order ANN, SS, GRLOT.

ISSN: 1471-2105