Table 2.

Advantages and disadvantages of reverse engineering methods.

Model	Advantages	Disadvantages
Boolean network	Large-scale network Better handling of computational complexity	Deterministic description Binary abstraction with information loss
Bayesian network	Handle incomplete and noisy data Learning about causality Integrating of prior knowledge Interpretation of network topology (hubs, modules)	Computational complexity Handling of feedback-loops not possible
Dynamic Bayesian network	Handling of feedback-loops and incomplete and noisy data Learning about causality Integrating of prior knowledge Handling of time series and causal relationship from perturbations Interpretation of network topology (hubs, modules)	Computational complexity Deriving regulatory networks using a multivariate approach considering only the best-scoring network due to limitation of computational time
Differential equations	Handling of negative feedback-loops Great physical accuracy Good performance	Computational complexity Small number of genes Require experimental parameters
Correlation analysis	Large-scale network Interpretation of network topology (hubs, modules)	Dependency of accuracy on the set of thresholds Integration of prior knowledge For linear or monotonical interactions
Mutual Information	Large-scale network Better handling of computational complexity through pairwise comparison Identifying causal relationship of TF-gene prediction Handling of feedback-loopsHandling of feedback-loopsHandling of feedback-loopsHandling of feedback-loops Reducing false positives and extract causal rather than associative links in gene networks Non-linear and non-monotonically dependencies	Dependency of accuracy on the set of thresholds Integration of prior knowledge