Biology Reference
In-Depth Information
BUILDING BRIDGES OVER THE UNKNOWN
One of the simplest ways to address gaps in biological knowledge is
to further develop the genotype-phenotype experimental technologies.
For instance, antibody specificity in protein microarrays needs to be
addressed to successfully monitor global network properties using this
technique. A recent study showed that only a small number of
antigen-antibody pairs exhibited the kind of specificity that is required in
order to perform highly parallel quantitative analysis, that is, in order to
monitor global network properties [88,89]. Similarly, protein microarrays
are believed to be capable of mapping signaling pathways and their indi-
vidual components, but they are hampered by the fact that they cannot
yet replicate the unique conditions that are necessary to preserve proper
protein folding and enzyme activity, among other in vivo properties [89].
Additionally, new tools for managing, integrating, and understand-
ing the wealth of data that is being amassed would be very beneficial
[9]. For example, a recent study analyzed the galactose metabolic path-
way with microarrays coupled with mass spectrometry and identified
new possibilities for the regulation of galactose and interpathway inter-
actions [9,66]. Such novel techniques provide the means for building
bridges over certain information “disconnects.”
However, as was discussed above, there are types of information that
may never be deciphered from experiment. To compensate for these
kinds of gaps, several different research avenues are currently being
pursued. Among these, a number of mathematical approaches that
account for the degree of unknowability have been developed. For
instance, the “constraint-based” approach is grounded in the fact that
all expressed phenotypes must satisfy basic constraints that are imposed
upon the molecular functions of all cells [90]. Physical laws like the con-
servation of mass and energy must be upheld, just as environmental
factors, including nutrient availability, pH, temperature, and osmolarity,
must be satisfied. These constraints have been described mathemati-
cally through the use of “balances” (constraints that are associated with
conserved quantities or with phenomena like osmotic pressure) and
“bounds” (constraints that limit the numerical ranges of individual
variables and parameters like concentration or flux), and the solution
space for these mathematical descriptions provides the range of valid
states of a reconstructed network. Thus, being able to identify con-
straints and state them mathematically helps narrow the spectrum of
possible phenotypes, and therefore provides an approach to enhancing
the understanding of cellular network function that would otherwise be
difficult. These approaches have had success with metabolism and reg-
ulation, and initial analysis of signaling networks shows promise [72].
Another example of a research avenue for handling the gaps in knowl-
edge involves reverse-engineering biological networks. As discussed
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