Finding differentially expressed modules.

<p>(A) Score based method selects the module with significant expression changes. (B) Correlation based method selects edges with correlation changes. The red and blue edges are correlated and anti-correlated edges, respectively. (C) Set cover based method selects a set of genes covering all samples. In this example, each sample has at least 2 differentially expressed genes and the genes are connected in the network.</p

Finding information propagation modules.

<p>(A) Shortest path approach to uncover information propagation. The shortest paths from a target gene (with hexagon shape) to each of three candidate genes are shown. The closest gene is identified as the most probable disease causing gene. (B) Flow based approach. The gene receiving the most significant amount of flow is identified as the disease gene. The information flow methods often follow Kirchhoff's current law (the amount of incoming information equals the amount of outgoing information).</p

Chapter 5: Network Biology Approach to Complex Diseases

<div><p>Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.</p> </div

Other types of biological networks.

<p>A) Topic model utilizing a patient similarity network. The network guides to find disease subtypes and their features (in the figure, the mutations in genes g₁ and g₂ are selected features for Subtype 1, while Subtype 2 has mutations in g₄ and g₅). Patients can be represented as mixtures of multiple subtypes. B) Disease network. A disease network can be constructed based on shared disease genes or the similarity of disease phenotypes. For example, the disease network on the right has an edge between two diseases if they share the same disease genes or phenotype features.</p

Identification of network modules enriched with genetic alterations.

<p>(A) Genomic regions with alterations. (B) Genes in the altered regions are mapped to the interaction network and modules enriched with such genes are identified.</p

Biological processes enriched in poised genes with background of all genes.

<p>Biological processes enriched in poised genes with background of all genes.</p

Biological processes enriched in non-poised genes with background of Pol II⁺ genes.

<p>Biological processes enriched in non-poised genes with background of Pol II⁺ genes.</p

Pol II density profiles of Fos and Jun in resting and activated B cells.

<p>Pol II density profiles in the genomic region along (a) Fos and (b) Jun genes in RESTB and ACTB cells. Data were normalized as reads per million reads mapped (TPM).</p

Statistical differences in the number of G4 sequence motifs between poised and non-poised genes, and between high and low ratio of expression at 30m over 72h after cell activation.

<p>We plot p-values of the Mann-Whitney-Wilcoxon tests in log scale. The relative position of the bars with respect to the central line indicates enriched category for a given gene group.</p

Distributions of Pol II density and its poising index.

<p>Violin plots showing (a) the distributions of Pol II promoter density, (b) Pol II gene body density, and (c) poising index values for 9710 genes in ACTB and 9290 genes in RESTB cells with Pol II⁺ promoters. Pol II densities were normalized to reads per kilobase per million reads mapped (RPKM). The y-axes are in a logarithmic scale.</p