Refine
Year of publication
- 2017 (3)
Document Type
- Article (2)
- Doctoral Thesis (1)
Language
- English (3)
Is part of the Bibliography
- yes (3)
Keywords
- Bioinformatik (1)
- Constraint-basierte Modellierung (1)
- Datenintegration (1)
- Stoffwechselnetze (1)
- Systemsbiologie (1)
- bioinformatics (1)
- constraint-based modeling (1)
- data integration (1)
- metabolic networks (1)
- systems biology (1)
Institute
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.
All life-sustaining processes are ultimately driven by thousands of biochemical reactions occurring in the cells: the metabolism. These reactions form an intricate network which produces all required chemical compounds, i.e., metabolites, from a set of input molecules. Cells regulate the activity through metabolic reactions in a context-specific way; only reactions that are required in a cellular context, e.g., cell type, developmental stage or environmental condition, are usually active, while the rest remain inactive. The context-specificity of metabolism can be captured by several kinds of experimental data, such as by gene and protein expression or metabolite profiles. In addition, these context-specific data can be assimilated into computational models of metabolism, which then provide context-specific metabolic predictions.
This thesis is composed of three individual studies focussing on context-specific experimental data integration into computational models of metabolism. The first study presents an optimization-based method to obtain context-specific metabolic predictions, and offers the advantage of being fully automated, i.e., free of user defined parameters. The second study explores the effects of alternative optimal solutions arising during the generation of context-specific metabolic predictions. These alternative optimal solutions are metabolic model predictions that represent equally well the integrated data, but that can markedly differ. This study proposes algorithms to analyze the space of alternative solutions, as well as some ways to cope with their impact in the predictions.
Finally, the third study investigates the metabolic specialization of the guard cells of the plant Arabidopsis thaliana, and compares it with that of a different cell type, the mesophyll cells. To this end, the computational methods developed in this thesis are applied to obtain metabolic predictions specific to guard cell and mesophyll cells. These cell-specific predictions are then compared to explore the differences in metabolic activity between the two cell types. In addition, the effects of alternative optima are taken into consideration when comparing the two cell types. The computational results indicate a major reorganization of the primary metabolism in guard cells. These results are supported by an independent 13C labelling experiment.
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.