Chapter 5: Network Biology Approach to Complex Diseases

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.


1.
Construct coexpression networks [98] a. Download the three expression datasets from the following page: http://www.geneticsofgeneexpression.org/network/download There are 3 files which contain expression levels for 3 different populations.
b. Compute 3 population-specific correlations for each pair of the 4238 genes with expression data.
(Hint: There are 8,978,203 pairs.) For each population, the Pearson correlation coefficient between every pair of genes (x, y) can be calculated as follows: where n is the number of measurements for each population. d. Construct the correlation network by connecting gene pairs whose weighted average correlations are greater than the pre-defined threshold (e.g., 0.5). After selecting gene pairs whose weighted average correlations exceed the predefined threshold (e.g., � � > 0.5), import the selected pairs into Cytoscape (File → Import → Network from Table)  Suppose that in a co-expression network two genes are identified to have correlated expression patterns. Provide at least two possible biological explanations of this correlation.
(i) One gene can be a transcription factor regulating another gene (ii) Both genes are regulated by the same transcription factor. (iii) Members of the same protein complex or pathway are often (directly or indirectly) co-regulated.

3.
Some variants of information flow approaches that identify pathways of information flow from a mutated gene to a target gene with correlated expression require that the last but one node gene on such a pathway (the node preceding the target gene) to be a transcription factor. What is a justification for such requirement? What can be advantages and disadvantages of such a design?
It is reasonable to assume that the expression change of a gene is a direct consequence of the activity of a transcription factor. However we don't know transcription factors for most of the human genes which limits applicability of such approach to the human interactive.

4.
Consider a set cover approach to find a representative set of genes dys-regulated in a given set of cancer patients. The algorithm finds the smallest number of genes so that each disease case is covered at least k times. How does the number of selected genes depend on k?
Increasing k will increase the number of genes required for the cover. In particular if there are k 1 genes that are dis-regualted in all patients, then for k ≤ k 1 we will get k of such genes in the covering set. For k> k 1 different samples will have to be covered with different genes so the number of gene need to cover all cases k time will be larger than k.
If you suspect that data for 5% patients might be incorrect, how would you modify the optimization problem?
In such situation, it is reasonable to modify the set cover definition to require that all but 5% of cases are required to be covered k-times.

5.
A Steiner connecting a set of nodes does not need to be unique. In Figure 4, Find two different Steiner trees connecting genes C,T 1 ,T 2 , T 3, T 4 .
Two Steiner trees connecting genes C,T 1 ,T 2 , T 3, T 4 are shown in green. Both trees use 9 edges. In the graph shown in Figure 4, find the shortest paths connecting C with T 1 ,T 2 , T 3 , T 4 . Do the edges used by these paths correspond to a Steiner tree?
The shortest paths connecting C with T 1 ,T 2 , T 3 , and T 4 are shown in green. The edges used by these paths do not correspond to a Steiner since the do not minimize the number of edges needed to connect all nodes.