Boolean networks

Introduction

A central goal of molecular biology is to understand the regulation of protein synthesis and its reactions to external and internal signals. All the cells in an organism carry the same genomic data, yet their protein makeup can be drastically different both temporally and spatially, due to regulation. Protein synthesis is regulated by many mechanisms at its different stages. These include mechanisms for controlling transcription initiation, RNA splicing, mRNA transport, translation initiation, post-translational modifications, and degradation of mRNA/protein. One of the main junctions at which regulation occurs is mRNA transcription. A major role in this machinery is played by proteins themselves that bind to regulatory regions along the DNA, greatly affecting the transcription of the genes they regulate.In recent years, technical breakthroughs in spotting hybridization probes and advances in genome sequencing efforts lead to development of DNA microarrays, which consist of many species of probes, either oligonucleotides or cDNA, that are immobilized in a predefined organization to a solid phase. By using DNA microarrays, researchers are now able to measure the abundance of thousands of mRNA targets simultaneously ( DeRisi et al.,1997 ; Lockhart et al., 1996; Wen et al., 1998). Unlike classical experiments, where the expression levels of only a few genes were reported, DNA microarray experiments can measure all the genes of an organism, providing a“genomic”viewpoint on gene expression. As a consequence, this technology facilitates new experimental approaches for understanding gene expression and regulation (Iyer et al., 1999; Spellman et al., 1998).

A central focus of genomic research concerns understanding the manner in which cells execute and control the enormous number of operations required for their function. Biological systems behave in an exceedingly parallel and extraordinarily integrated fashion. Feedback and damping are routine even for the most common activities. Thus, in this area of genomic biology, single gene perspectives are becoming increasingly limited for gaining insight into biological processes. Network applications are becoming increasingly important for making progress in our understanding of the manner in which genes and molecules collectively form a biological system and harnessing this understanding in educated intervention for correcting human diseases. Such approaches inevitably require computational and formal methods to process massive amounts of data, understand general principles governing the system under study, and make useful predictions about system behavior in the presence of known conditions. There is a rather wide spectrum of approaches for modeling gene regulatory networks, each with its own assumptions, data requirements, and goals. The group of the most popular models includes: Boolean, Probabilistic Boolean and Bayesian networks.