A systems approach to identifying correlated gene targets for the loss of colour pigmentation in plants
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2011
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Journal Article
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Abstract
The numerous diverse metabolic pathways by which plant compounds can be produced make it
difficult to predict how colour pigmentation is lost for different tissues and plants. This study employs
mathematical and in silico methods to identify correlated gene targets for the loss of colour pigmentation in plants
from a whole cell perspective based on the full metabolic network of Arabidopsis. This involves extracting a selfcontained
flavonoid subnetwork from the AraCyc database and calculating feasible metabolic routes or elementary
modes (EMs) for it. Those EMs leading to anthocyanin compounds are taken to constitute the anthocyanin
biosynthetic pathway (ABP) and their interplay with the rest of the EMs is used to study the minimal cut sets
(MCSs), which are different combinations of reactions to block for eliminating colour pigmentation. By relating the
reactions to their corresponding genes, the MCSs are used to explore the phenotypic roles of the ABP genes, their
relevance to the ABP and the impact their eliminations would have on other processes in the cell.
Simulation and prediction results of the effect of different MCSs for eliminating colour pigmentation
correspond with existing experimental observations. Two examples are: i) two MCSs which require the
simultaneous suppression of genes DFR and ANS to eliminate colour pigmentation, correspond to observational
results of the same genes being co-regulated for eliminating floral pigmentation in Aquilegia and; ii) the impact of
another MCS requiring CHS suppression, corresponds to findings where the suppression of the early gene CHS
eliminated nearly all flavonoids but did not affect the production of volatile benzenoids responsible for floral scent.
From the various MCSs identified for eliminating colour pigmentation, several correlate to existing
experimental observations, indicating that different MCSs are suitable for different plants, different cells, and
different conditions and could also be related to regulatory genes. Being able to correlate the predictions with
experimental results gives credence to the use of these mathematical and in silico analyses methods in the design
of experiments. The methods could be used to prioritize target enzymes for different objectives to achieve desired
outcomes, especially for less understood pathways.
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© 2011 Clark and Verwoerd; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
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