A major problem associated with the reverse engineering of genetic networks from micro-array data is how to reliably find genetic interactions when faced with a relatively small number of arrays compared to the number of genes. To cope with this dimensionality problem, it is imperative to employ additional (biological) knowledge about genetic networks, such as limited connectivity, redundancy, stability and robustness, to sensibly constrain the modeling process. Recently, we have shown that by applying single criteria, the inference of genetic interactions under realistic conditions can be significantly improved. In this paper, we study the problem of how to combine constraints by formulating it as a multi-criterion optimization problem.
Currently, the need arises for tools capable of unraveling the functionality of genes based on the analysis of microarray measurements. Modeling genetic interactions by means of genetic network models provides a methodology to infer functional relationships between genes. Although a wide variety of different models have been introduced so far, it remains, 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: inferential power; predictive power; robustness; consistency; stability and computational cost. Where possible, synthetic time series data are employed to investigate some of these properties. The comparison shows that although genetic network modeling might provide valuable information regarding genetic interactions, current models show disappointing results on simple artificial problems. For now, the simplest models are favored because they generalize better, but more complex models will probably prevail once their bias is more thoroughly understood and their variance is better controlled.