Computational and experimental approaches for modeling gene regulatory networks

Area of Science:

• Systems Biology
• Computational Biology
• Genomics

Background:

• Understanding cellular processes requires knowledge of genetic information processing.
• Dissecting eukaryotic gene regulatory networks is key to understanding gene expression patterns and phenotypes.
• Colon cancer progression involves complex transcriptional regulatory mechanisms.

Purpose of the Study:

• To review approaches for modeling eukaryotic gene regulatory networks.
• To discuss methods for reverse engineering these networks from experimental data.
• To illustrate modeling and reverse engineering using colon cancer as a case study.

Main Methods:

• Discusses four models: gene networks, transcriptional regulatory systems, Boolean networks, and dynamical Bayesian networks.
• Reviews functional genomics techniques: gene expression profiling, cis-regulatory element identification, TF target gene identification, and RNA interference.
• Focuses on reverse engineering transcriptional regulatory networks using gene perturbations and microarray data.

Main Results:

• Computational models can be constructed to predict experimental observations in gene regulation.
• Mathematical formulation of reverse engineering transcriptional regulatory networks by gene perturbations is presented.
• The role of experimental resolution in reconstructing accurate gene regulation models is discussed.

Conclusions:

• Modeling and reverse engineering are essential for elucidating complex gene regulatory mechanisms.
• Functional genomics data combined with reverse engineering algorithms can build predictive computational models.
• Inferring transcriptional regulatory systems from perturbation-based microarray data is a promising approach.