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Review
. 2006 Dec 29;2(12):e174.
doi: 10.1371/journal.pcbi.0020174.

Modularity and dynamics of cellular networks

Yuan Qi  1 , Hui Ge
Affiliations
Review

Modularity and dynamics of cellular networks

Yuan Qi et al. PLoS Comput Biol. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

Competing interests. The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. An Overview of Biological Network Analyses Based on “Omic” Data
Recent high-throughput technologies have produced massive amounts of gene expression, macromolecular interaction, or other type of “omic” data. Using a computational modeling approach, the architecture of cellular networks can be learned from these “omic” data, and topological or functional units (motifs and modules) can be identified from these networks. Comparisons of cellular networks across different species may reveal how network structures evolve. In particular, the evolutionary conservation of motifs and modules can be an indication of their biological importance. A dynamic view of cellular networks describes active network components and interactions under various conditions and time points. Network motifs and modules can also be time-dependent or condition-specific.
Figure 2
Figure 2. Network Motifs Found in E. coli Transcriptional Regulatory Networks
(Left) Feed-forward loop: TF X regulates TF Y, and both X and Y jointly regulate gene Z. (Middle) Single-input motif: TF X regulates genes Z1, Z2… and Zn. (Right) Multi-input motif: a set of TFs X1, X2… and Xn regulate a set of target genes Z1, Z2… and Zm. (Reproduced from [12].)
Figure 3
Figure 3. Yeast Transcriptional Regulatory Modules
Nodes represent modules, and boxes around the modules represent module groups. Directed edges represent regulatory relationship. The functional categories of the modules are color-coded. (Reproduced from [15].)
Figure 4
Figure 4. Dynamic Properties of Network Motifs
(Upper panels) Shows a feed-forward loop, where Y is an accumulation of X over time, and the product of X and Y passes a threshold (thin horizontal line) to activate Z. This loop rejects impulsive perturbations in X, and responds only to persistent activation. This is because Y increases gradually to pass the threshold. A similar rejection of impulsive fluctuations can be achieved by a feed-forward chain, where X activates Y and Y activates Z. However, a feed-forward chain responds slower (thin red curve) to the off signal than to the loop. (Lower panels) Shows a single-input motif, where X regulates Z1, Z2, and Z3 (n = 3). When X changes over time, Z1, Z2, and Z3 are activated and deactivated in order, based on their thresholds. In particular, Z1, which has the lowest threshold, is activated first and deactivated last. (Reproduced from [12].)
Figure 5
Figure 5. Bayesian Network Modeling of Molecular Interactions in Cell Signaling
Nodes in the network represent key signaling molecules. Directed edges represent predicted causal relationships between signaling molecules. Edges are categorized into different classes: (i) well-established interactions in the literature (“expected”); (ii) interactions that have been reported but weakly supported (“reported”); (iii) well-established interactions that Bayesian networks failed to predict (“missing”); (iv) predicted causal relationship in a direction opposite to the literature (“reversed”). (Reproduced from [39].)
Figure 6
Figure 6. Clustering Methods
Genes that share similar expression profiles across conditions are grouped together by clustering.
Figure 7
Figure 7. Topology-Based Network Analysis
Densely connected subgraphs can be identified from interaction networks, suggesting the existence of multi-component complexes.
Figure 8
Figure 8. Probabilistic Graphical Models
Directed acyclic graphical models are called Bayesian networks. In the shown Bayesian network, values of variable Y depend directly on values of X, and values of variable Z1 and Z2 depend directly on values of Y.
Figure 9
Figure 9. Integration of Multiple Datasets
The integration of a variety of datasets, including binary interactions, protein complexes, and expression profiles enables the identification of subnetworks that are active under certain conditions.
See this image and copyright information in PMC

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