Background: Recent advances with high-throughput methods in life-science research have increased the need for automatized data analysis and visual exploration techniques. Sophisticated bioinformatics tools are essential to deduct biologically meaningful interpretations from the large amount of experimental data, and help to understand biological processes.

Polyhydroxyalkanoates (PHAs) are polyoxoesters that are produced by diverse bacteria and that accumulate as intracellular granules. Phasins are granule-associated proteins that accumulate to high levels in strains that are producing PHAs. The accumulation of phasins has been proposed to be dependent on PHA production, a model which is now rigorously tested for the phasin PhaP of Ralstonia eutropha. R. eutropha phaC PHA synthase and phaP phasin gene replacement strains were constructed. The strains were engineered to express heterologous and/or mutant PHA synthase alleles and a phaP-gfp translational fusion in place of the wild-type alleles of phaC and phaP. The strains were analyzed with respect to production of polyhydroxybutyrate (PHB), accumulation of PhaP, and expression of the phaP-gfp fusion. The results suggest that accumulation of PhaP is strictly dependent on the genetic capacity of strains to produce PHB, that PhaP accumulation is regulated at the level of both PhaP synthesis and PhaP degradation, and that, within mixed populations of cells, PhaP accumulation within cells of a given strain is not influenced by PHB production in cells of other strains. Interestingly, either the synthesis of PHB or the presence of relatively large amounts of PHB in cells (>50% of cell dry weight) is sufficient to enable PhaP synthesis. The results suggest that R. eutropha has evolved a regulatory mechanism that can detect the synthesis and presence of PHB in cells and that PhaP expression can be used as a marker for the production of PHB in individual cells.

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement.

The accumulation of storage compounds is an important aspect of cereal seed metabolism. Due to the agronomical importance of the storage reserves of starch, protein, and oil, the understanding of storage metabolism is of scientific interest, with practical applications in agronomy and plant breeding. To get insight into storage patterning in developing cereal seed in response to environmental and genetic perturbation, a computational analysis of seed metabolism was performed. A metabolic network of primary metabolism in the developing endosperm of barley (Hordeum vulgare), a model plant for temperate cereals, was constructed that includes 257 biochemical and transport reactions across four different compartments. The model was subjected to flux balance analysis to study grain yield and metabolic flux distributions in response to oxygen depletion and enzyme deletion. In general, the simulation results were found to be in good agreement with the main biochemical properties of barley seed storage metabolism. The predicted growth rate and the active metabolic pathway patterns under anoxic, hypoxic, and aerobic conditions predicted by the model were in accordance with published experimental results. In addition, the model predictions gave insight into the potential role of inorganic pyrophosphate metabolism to maintain seed metabolism under oxygen deprivation.