TY  - JOUR
A1  - Robaina-Estevez, Semidan
A1  - Daloso, Danilo M.
A1  - Zhang, Youjun
A1  - Fernie, Alisdair R.
A1  - Nikoloski, Zoran
T1  - Resolving the central metabolism of Arabidopsis guard cells
JF  - Scientific reports
N2  - Photosynthesis and water use efficiency, key factors affecting plant growth, are directly controlled by microscopic and adjustable pores in the leaf-the stomata. The size of the pores is modulated by the guard cells, which rely on molecular mechanisms to sense and respond to environmental changes. It has been shown that the physiology of mesophyll and guard cells differs substantially. However, the implications of these differences to metabolism at a genome-scale level remain unclear. Here, we used constraint-based modeling to predict the differences in metabolic fluxes between the mesophyll and guard cells of Arabidopsis thaliana by exploring the space of fluxes that are most concordant to cell-type-specific transcript profiles. An independent C-13-labeling experiment using isolated mesophyll and guard cells was conducted and provided support for our predictions about the role of the Calvin-Benson cycle in sucrose synthesis in guard cells. The combination of in silico with in vivo analyses indicated that guard cells have higher anaplerotic CO2 fixation via phosphoenolpyruvate carboxylase, which was demonstrated to be an important source of malate. Beyond highlighting the metabolic differences between mesophyll and guard cells, our findings can be used in future integrated modeling of multicellular plant systems and their engineering towards improved growth.
Y1  - 2017
U6  - https://doi.org/10.1038/s41598-017-07132-9
SN  - 2045-2322
VL  - 7
SP  - 1913
EP  - 1932
PB  - Nature Publ. Group
CY  - London
ER  - 
TY  - JOUR
A1  - Robaina-Estevez, Semidan
A1  - Nikoloski, Zoran
T1  - On the effects of alternative optima in context-specific metabolic model predictions
JF  - PLoS Computational Biology : a new community journal
N2  - The integration of experimental data into genome-scale metabolic models can greatly improve flux predictions. This is achieved by restricting predictions to a more realistic context-specific domain, like a particular cell or tissue type. Several computational approaches to integrate data have been proposed D generally obtaining context-specific (sub) models or flux distributions. However, these approaches may lead to a multitude of equally valid but potentially different models or flux distributions, due to possible alternative optima in the underlying optimization problems. Although this issue introduces ambiguity in context-specific predictions, it has not been generally recognized, especially in the case of model reconstructions. In this study, we analyze the impact of alternative optima in four state-of-the-art context-specific data integration approaches, providing both flux distributions and/or metabolic models. To this end, we present three computational methods and apply them to two particular case studies: leaf-specific predictions from the integration of gene expression data in a metabolic model of Arabidopsis thaliana, and liver-specific reconstructions derived from a human model with various experimental data sources. The application of these methods allows us to obtain the following results: (i) we sample the space of alternative flux distributions in the leaf-and the liver-specific case and quantify the ambiguity of the predictions. In addition, we show how the inclusion of l(1)-regularization during data integration reduces the ambiguity in both cases. (ii) We generate sets of alternative leaf-and liver-specific models that are optimal to each one of the evaluated model reconstruction approaches. We demonstrate that alternative models of the same context contain a marked fraction of disparate reactions. Further, we show that a careful balance between model sparsity and metabolic functionality helps in reducing the discrepancies between alternative models. Finally, our findings indicate that alternative optima must be taken into account for rendering the context-specific metabolic model predictions less ambiguous.
Y1  - 2017
U6  - https://doi.org/10.1371/journal.pcbi.1005568
SN  - 1553-734X
SN  - 1553-7358
VL  - 13
SP  - 750
EP  - 766
PB  - PLoS
CY  - San Fransisco
ER  -