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Figure 2

Figure 2. From: Principles and Strategies for Developing Network Models in Cancer.

Combining computationally-guided experiment design with RNAi screens has enormous untapped potential. Although genome-wide datasets are the most comprehensive, they are also expensive to perform at the large scale required to cover all contexts. A more economical approach is to refine our understanding with iterative cycles of experimentation and computation. Computational hypotheses derived from one dataset are used to design the experiments for collecting the next dataset (figure 2). For example, protein interaction maps and microarray expression data were used to nominate high likelihood genes for characterization in a RNAi screen that dissects interactions between influenza and its host (Shapira et al., 2009). This approach deepened our understanding of how the virus manipulates or is controlled by key host defenses through direct and indirect interactions with 4 major host pathways.

Dana Pe’er, et al. Cell. ;144(6):864-873.
Figure 1

Figure 1. From: Principles and Strategies for Developing Network Models in Cancer.

Molecular networks are not static; rather, they exhibit dynamic adaptations in response to both internal states and external signals. Influences that determine network context can be divided into four categories. (1) Genetic background strongly determines network behavior and gives rise to significant differences across individuals (and even cells in the special case of cancer). (2) Cell lineages have dramatically different network structures because of epigenetic changes and differential expression of genes. (3) Tissue milieu can reprogram networks and their behaviors, as stromal cells do for tumors. (4) Exogenous signals, such as nutrients and other chemicals affect networks (Figure 1). Ultimately, health or disease emerges from an individual’s integration of internal and external cues.

Dana Pe’er, et al. Cell. ;144(6):864-873.

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