Chapter Review - Metabolomics
So far we have studied techniques to monitor
Transcription (microarrays, northerns)
Translation into proteins (antibody methods, westerns)
But the presence of a protein does not mean that it is active. As an example, consider the regulation of the synthesis and degredation of glycogen.
The
When sampling tissue you must use the same developmental state each time.
When sampling tissue you must take it at the same time of day because many genes are regulated on circadian clock.
There is difficulty in taking a snapshot of all metabolic substrates at the same time.
GC- MS
HPLC-MS
NMR
Large datasets: transcriptome x proteome x metabolome x localization x circadian time
So, how do we identify relationships between changed in one of the above and phenotypic or disease states?
Principle Component
Analysis is a popular method to identify the variables that contribute
most to a state change.
"
Technically speaking, PCA is a linear transformation
that transforms the data to a new coordinate system such that the greatest
variance by any projection of the data comes to lie on the first coordinate
(called the first principal component), the second greatest variance
on the second coordinate, and so on. PCA can be used for dimensionality
reduction in a dataset while retaining those characteristics of the dataset
that contribute most to its variance, by keeping lower-order principal
components and ignoring higher-order ones." (Wikipedia)
KEGG metabolic pathway database