Systems Analysis of Arrestin Pathway Functions - Progress in Molecular Biology and Translational Science - page 436

together in equal protein amounts for MS analysis. Treatment-induced
changes in interactome content/stoichiometry will, therefore, be indicated
by either presence or absence of a specific protein, or a significant change in
the expression ratio of the various proteins in the complex between the
isotopic or nonisotopically labeled samples. 129,130 With the systematic appli-
cation of such procedures for a greater number of proteins, it should be
feasible in the future to construct functional protein complex maps that asso-
ciate physical interactome composition and subcellular distribution to cell
signaling or pathophysiological events. To this end, multiple currently avail-
able databases 131,132 (PSIMAP, http://metadatabase.org/wiki/PSIMAP ;
Human Interaction Database, http://interactome.dfci.harvard.edu/H_
sapiens/ ) and interactomic resources 133,134 (MAPPIT, http://www.crl-
mappit.be/mappit_toolbox/ ; PhosphoPOINT, http://kinase.bioinformat
ics.tw/ ) are now forming the basis of such an eventual cell signaling
“roadmap.”
3.5. Bioinformatic interpretation
In this chapter, we have previously discussed the various modes of systematic
analysis that can be applied to the investigation of GPCR-related biological
functions, for example, transcriptomic, proteomic, and interactomic. Con-
siderable technical effort has been exerted to facilitate the maximal level of
accurate and appropriate data extraction from these distinct experimental
processes. However, while being able to generate extensive volumes of data,
this output often reaches the bottleneck of effective and productive bioin-
formatic analysis. In this section, we describe the use of standardized as well
as more advanced modes of bioinformatic analysis that can effect a meaning-
ful and practical interpretation of mass analytical cell signaling data.
The simplest, and unfortunately still the most common, mechanism of
informatic interpretation of mass analytical data is to simply perform scien-
tific literature searches upon the highest and lowest regulated genes/proteins
from the primary data. This approach, while yielding some actionable data, is
now criticized for ignoring the correlated biological relevance of the mul-
tiple factors (genes/proteins) in the rest of the dataset that do not individually
demonstrate either large or significant differential regulation. If we apply the
concept that complete knowledge of all the gene-protein interactions does
not exist, it is perhaps more prudent to assume that all interactions could
occur and then only disregard analytical data for genes/proteins that one
can empirically disprove. If we consider that functional signaling responses