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A Network Model of Genetic Mutation

As the science of human genetics develops over time, increasing numbers of diseases are being traced and attributed to gene mutations.  A gene is a section of DNA that contains encoded instructions that allow a cell to synthesize a specific protein, and when no genetic mutations are present, we expect all biochemical cellular processes to operate smoothly.  When mutations are present, however, the protein for which the gene codes will be constructed incorrectly, and this can lead to disruptions and deviations as the cell attempts to operate.  Unfortunately, these detrimental alterations are fairly common, but this does offer scientists ample opportunity to conduct thorough analysis of mutant gene expression.  In the past, models have guaranteed that mutation would lead to a loss of the associated gene product, but as Quan Zhong and her colleagues discuss in the article, “Edgetic Perturbation Models of Human Inherited Behaviors,” a network model can provide a more accurate understanding of the consequences of genetic mutation.

In the network model of genetic mutation, the altered protein, or gene product, is modeled by a ‘node’ while the biophysical and biochemical interactions of these products are modeled by ‘edges’.  Zhong and her colleagues discuss various mutations that can be grouped under the two main classifications of ‘truncating’ and ‘in-frame’.  ‘Truncating’ mutations result in the complete loss of a gene product; therefore, they are analogous to node removal.  Furthermore, any cellular process that could be modeled as a network path crossing that specific lost node would no longer be possible.  ‘In-frame’ mutations, on the other hand, result in the production of a mutated gene product.  When this occurs, biochemical interactions involving the node may still be possible, but they will be different than what would have occurred if the protein were constructed correctly.  The authors of the article argue that this occurs because the mutated protein will gain and lose certain connections with other gene products that might not have been expressed by the optimal protein.  These connections are modeled by edges, and genetic changes resulting from their reorganization were aptly named edge-specific, or edgetic, perturbations.  To understand why changing the edges could change the overall interaction, you can simply conceptualize a network that models roads.  If you select a different path, which is analogous to the edge changes resulting from ‘in-frame’ mutation, you can make the same overall journey (execute the same biochemical interaction) but end up with a different travel time (different chemical inputs and outputs).

The network model of genetic mutation illustrates just one of the numerous fields to which network knowledge can be applied.  With simple analogs to nodes and edges, the understanding of network paths could be vital to the future discovery of Mendelian diseases.  In fact, by truly comprehending the edge-specific perturbations of ‘in-frame’ mutations, scientists could possibly identify the countermeasures necessary to treat these disorders.

Source 1 (The Main Article):

http://yulab.icmb.cornell.edu/PDF/ZhongMSB2009.pdf

Source 2 (A Summary):

http://www.nature.com/nrg/journal/v11/n1/full/nrg2720.html

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