Dartmouth researchers explored the type and number of connections in transcription factor networks (TFNs) to evaluate the role assortativity plays on robustness in a study published in PLOS Computational Biology in August. The study found that the assortativity signature contributes to a network's resilience against mutations.
"In simulations, it seems that varying the out-out assortativity of TFN models has a greater effect on robustness than varying any of the other three types of assortativity," said Dov A. Pechenick, PhD, lead author and former researcher at the Computational Genetics Laboratory at Dartmouth College, Hanover, NH. "We determined this by varying all four types of assortativity in the signature and then measuring robustness."
Transcription factors (TFs) are proteins that initiate and regulate the expression of a gene. To achieve their genetic mission, TFs also regulate one another's expression. Individual TFs connect to each other through connections that point in or out, forming a network. The direction of the coupling indicates regulatory control; an outbound connection by one TF stipulates its control over another, whereby it turns that TF on or off with respect to gene expression.
Many such connected pairings occur in a network, and their types determine a network's assortativity, which measures whether these pairings tend to occur between TFs that have similar numbers of connections. For example, when TFs in a pairing are likely to possess similar numbers of outgoing connections, out-out assortativity is high. If they are likely to possess very different numbers of incoming connections, in-in assortativity is low. Taken together, measures for the different kinds of assortativity create a signature. According to Pechenick, "There are four types of assortativity in directed networks, and the assortativity signature is a way of looking at all four at once."
Pechenick and his Dartmouth co-author
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The Geisel School of Medicine at Dartmouth