Network modelling of gene regulation

Abstract

Gene regulatory network (GRN) modelling has gained increasing attention in the past decade. Many computational modelling techniques have been proposed to facilitate the inference and analysis of GRN. However, there is often confusion about the aim of GRN modelling, and how a gene network model can be fully utilised as a tool for systems biology. The aim of the present article is to provide an overview of this rapidly expanding subject. In particular, we review some fundamental concepts of systems biology and discuss the role of network modelling in understanding complex biological systems. Several commonly used network modelling paradigms are surveyed with emphasis on their practical use in systems biology research.

Keywords: Bioinformatics; Gene regulatory network; Systems biology.

Fig. 1

This cartoon illustrates some of the key processes that regulate eukaryotic gene expression. The message is clear: gene expression is tightly regulated by a complex system of interconnected components inside and outside a cell

Fig. 2

Network modelling provides a natural means to combine diverse experimental data and prior biological knowledge for data analysis in a principled manner

Fig. 3

A transcription factor-binding network of Escherichia coli constructed using the data from RegulonDB (Salgado et al. 2006) (a). This network consists of 1,306 genes and 2,981 interactions. Network structure can be quantitatively assessed by topological features and network motifs. Some common network motifs are shown (b). In this example, green nodes are regulators and yellow nodes are non-regulators

Fig. 4

Construction of two condition-specific gene coexpression networks from an eight-gene microarray dataset. At first, no obvious pattern emerges from visual inspection of the heat map of this simulated dataset. Nonetheless, comparison of the coexpression networks constructed from the two conditions (conditions 1 and 2) reveals a large shift in coexpression pattern among these eight genes. This simple example illustrates the power of gene coexpression network analysis

Fig. 5

A four-gene Boolean network is shown in (a) and its corresponding state transition function is shown in (b). By emulating all position state transitions trajectories, we can construct the entire state-space of a given Boolean network model. The state space of our example network is shown in (c). In this state-space diagram, attractor states are represented by green nodes while transient states are represented by blue nodes. Analysis of the state space network structure can reveal interesting dynamical properties of the Boolean network. (d) An example Petri net. The orange circles represent places, and the blue rectangles represent transitions. The black circles inside a place are the tokens, and the distribution of tokens among all the places represents a marking of a Petri net

Fig. 6

An example Bayesian network with three nodes. The directed acyclic graph shown in (a) depicts the conditional dependency structure among the three variables. Dynamic information can be encoded in a dynamic Bayesian network (b), where the dependency structure between successive time points (t and t+1) is explicitly modelled. An intervention to a variable in a Bayesian network, say node B in (c), has the effect of removing all its incoming edges in the network (say A→B). A Bayesian network which is consistent with all interventional events is called a causal Bayesian network. A Bayesian network is only one member of a larger family of probabilistic graphical models. Other popular probabilistic graphical models include Markov random fields (d) and factor graphs (e). All these probabilistic graphical models are well suited for integrative data analysis of multiple data types since they are flexible and are inherently capable of dealing with noise in the experimental data