A Comparison of Genetic Network Models

With the completion of the sequencing of the human genome, the need for tools capable of unraveling the interaction and functionality of genes becomes extremely urgent. In answer to this quest, the advent of microarray technology provides the opportunity to perform large scale gene expression analyses. Recently, genetic networks were proposed as a possible methodology for modeling genetic interactions. Since then, a wide variety of different models have been introduced. However, it is, in general, unclear what the strengths and weaknesses of each of these approaches are and where these models overlap and differ. This paper compares different genetic modeling approaches that attempt to extract the gene regulation matrix from expression data. A taxonomy of continuous genetic network models is proposed and the following important characteristics are suggested and employed to compare the models: (1) inferential power; (2) predictive power; (3) robustness; (4) consistency; (5) stability and (6) computational cost. Where possible, synthetic time series data are employed to investigate some of these properties.