Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus.
Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus.
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DOI：
10.1186/1752-0509-5-36
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发表时间：
2011-02-26
影响因子：
--
通讯作者：
Millar AJ
中科院分区：
文献类型：
--
作者：
Sorokina O;Corellou F;Dauvillée D;Sorokin A;Goryanin I;Ball S;Bouget FY;Millar AJ
The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles. Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation. We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga Ostreococcus tauri. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in O. tauri in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by in silico analysis. A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in O. tauri directly from time-series gene expression data.
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影响因子：
56.9
作者：
Harmer, SL;Hogenesch, LB;Kay, SA
通讯作者：
Kay, SA
影响因子：
2.7
作者：
Loboda A;Kraft WK;Fine B;Joseph J;Nebozhyn M;Zhang C;He Y;Yang X;Wright C;Morris M;Chalikonda I;Ferguson M;Emilsson V;Leonardson A;Lamb J;Dai H;Schadt E;Greenberg HE;Lum PY
通讯作者：
Lum PY
DOI：
10.1073/pnas.0406674102
发表时间：
2005-02-01
影响因子：
11.1
作者：
Mikkelsen, R;Mutenda, KE;Blennow, A
通讯作者：
Blennow, A
影响因子：
3.2
作者：
DELRUE, B;FONTAINE, T;BALL, S
通讯作者：
BALL, S
影响因子：
14.8
作者：
Becker, Scott A.;Feist, Adam M.;Herrgard, Markus J.
通讯作者：
Herrgard, Markus J.