A Comparison of Genetic Network Models

Abstract

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 di erent 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 di er. This paper compares di erent 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 c haracteristics are suggested and employed to compare the models: 1 inferential power; 2 predictive p o w er; 3 robustness; 4 consistency; 5 stability and 6 computational cost. Where possible, synthetic time series data are employed to investigate some of these properties.

Importance of Input Perturbations and Stochastic Gene Expression in the Reverse Engineering of Genetic Regulatory Networks: Insights From an Identifiability Analysis of an In Silico Network

“…The present work is a departure from previous efforts in that the analysis of a specific in silico system itself is presented, rather than a novel reverse engineering approach. Given that our study considers only one specific network, it may suffer in comparison to other studies that considered several in silico networks (Wessels et al 2001;Yeung et al 2002;Smith et al 2003). Our network is, however, of greater mechanistic detail than those considered in the previous studies.…”

“…Wessels et al (2001) explored several approaches for reverse engineering genetic regulatory networks from gene expression data, but they constrained the complexity of their in silico systems by the reverse engineering approaches themselves. The studies of Zak et al (2001a), Smith et al (2002Smith et al ( , 2003, and Yeung et al (2002) more closely paralleled the experimental situation in that their reverse engineering techniques differed from the systems used to generate the simulation data.…”

Neural Model of the Genetic Network

“…Comparative analysis of performance of the neural network-based models can be found in Ref. 12. Recurrent neural networks can also deal with feedback, which can occur in natural gene control processes, and they are flexible enough to fit experimental data.…”

Computational Neurogenetic Modeling: A Methodology to Study Gene Interactions Underlying Neural Oscillations

“…This nonlinear equation is inspired by discrete models from [6,7], based on the fact that gene expression data are gathered in discrete time intervals [8]. We work with normalized gene expression values, expressed by a nonlinear sigmoid σ(x) ∈ (0, 1 …”