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. 2010 Feb 26;6(2):e1000655.
doi: 10.1371/journal.pcbi.1000655.

How to understand the cell by breaking it: network analysis of gene perturbation screens

Florian Markowetz  1
Affiliations

How to understand the cell by breaking it: network analysis of gene perturbation screens

Florian Markowetz. PLoS Comput Biol. .
No abstract available

PubMed Disclaimer

Conflict of interest statement

The author has declared that no competing interests exist.

Figures

Figure 1
Figure 1. Cellular networks underlying observable phenotypes.
(A) Phenotypes are the response of the cell to external signals mediated by cellular networks and pathways. The goal of computation is to reconstruct these networks from the observed phenotypes. (B) Global molecular phenotypes like gene expression allow a view inside the cell but also have limitations. This is exemplified here in a cartoon pathway adapted from showing a cascade of five genes/proteins (1–5). Proteins 1–3 form a kinase cascade, 4 is a transcription factor acting on 5. Up-regulation of 1 starts information flow in the cascade and results in 5 being turned on. In gene expression data this is visible as a correlation between 1 and 5 (represented as an undirected edge in the model). Experimentally perturbing a gene, say 3, removes the corresponding protein from the cascade, breaks the information flow, and results in an expression change at 5 (represented as an arrow in the model). However, the different phosphorylation and activation states of proteins 2–4 will most probably not be visible as changes in gene expression. Thus, because of the pathway mostly acting on the protein level most parts of the cascade (dashed arrows in the model) can not be inferred from gene expression data directly.
Figure 2
Figure 2. Functional annotation of hits by enrichment analysis.
(A) In the first approach a cutoff is applied to select the hits with strongest phenotypes. A hyper-geometric test then evaluates if the overlap between the hits and a given gene set is surprisingly large (or small) compared to the overlap with a random set. (B) A second approach does not need a cutoff. It maps the gene set (black bars) onto the observed phenotypes and quantifies if there is a significant trend or if the genes are spread out uniformly over the whole range.
Figure 3
Figure 3. Extracting rich subnetworks.
Different patterns in the graph point to a common cellular mechanism causing a phenotype: (A) hits in a low-dimensional screen (red nodes) clustering in highly connected subnetworks, and (B) high correlation between high-dimensional phenotypes of target genes connected in the background network. The black graph represents any type of background network.
See this image and copyright information in PMC

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