Information content of colored motifs in complex networks.

We study complex networks in which the nodes are tagged with different colors depending on their function (colored graphs), using information theory applied to the distribution of motifs in such networks. We find that colored motifs can be viewed as the building blocks of the networks (much more than the uncolored structural motifs can be) and that the relative frequency with which these motifs appear in the network can be used to define its information content. This information is defined in such a way that a network with random coloration (but keeping the relative number of nodes with different colors the same) has zero color information content. Thus, colored motif information captures the exceptionality of coloring in the motifs that is maintained via selection. We study the motif information content of the C. elegans brain as well as the evolution of colored motif information in networks that reflect the interaction between instructions in genomes of digital life organisms. While we find that colored motif information appears to capture essential functionality in the C. elegans brain (where the color assignment of nodes is straightforward), it is not obvious whether the colored motif information content always increases during evolution, as would be expected from a measure that captures network complexity. For a single choice of color assignment of instructions in the digital life form Avida, we find rather that colored motif information content increases or decreases during evolution, depending on how the genomes are organized, and therefore could be an interesting tool to dissect genomic rearrangements.

Automated data interpretation based on the concept of "negative signature mass" for mass-mapping disulfide structures of cystinyl proteins.

An efficient method for data processing and interpretation is needed to support and extend disulfide mass-mapping methodology based on partial reduction and cyanylation-induced cleavage to proteins containing more than four cystines. Here, the concept of "negative signature mass" is introduced as the novel feature of an algorithm designed to identify the disulfide structure of a cystinyl protein given an input of mass spectral data and an amino acid sequence. The "negative signature mass" process is different from the conventional approach in that it does not directly rule-in disulfide linkages, but rather eliminates linkages from a list of all possible theoretical linkages, with the goal of ruling out enough linkages so that only one disulfide structure can be constructed. The operating principles and the effectiveness of the algorithm are described in the context of analyzing ribonuclease A, a 124-residue protein containing eight cysteines in the form of four cystines (disulfides).