Plants are exposed to continual changes in the environment. The daily alternation between light and darkness results in massive recurring changes in the carbon budget, and leads to widespread changes in transcript levels. These diurnal changes are superimposed on slower changes in the environment. Quantitative molecular information about the numbers of ribosomes, of transcripts for 35 enzymes in central metabolism and their loading into polysomes is used to estimate translation rates in Arabidopsis rosettes, and explore the consequences for important sub‐processes in plant growth. Translation rates for individual enzyme are compared with their abundance in the rosette to predict which enzymes are subject to rapid turnover every day, and which are synthesized at rates that would allow only slow adjustments to sustained changes of the environment, or resemble those needed to support the observed rate of growth. Global translation rates are used to estimate the energy costs of protein synthesis and relate them to the plant carbon budget, in particular the rates of starch degradation and respiration at night. ### Synopsis In the following article, we quantify ribosome and transcript concentrations, and polysome composition in Arabidopsis rosettes. These data are used to predict translation rates, globally, and for individual enzymes in central metabolism. We then explore the consequences of these molecular events for growth. First, we compare the rates of synthesis with protein abundance to predict which enzymes are likely to be subject to rapid turnover and which are synthesized at rates similar to those required for growth. Second, we estimate the costs of protein synthesis and relate them to the whole plant carbon budget. Plants experience continual changes in the environment. One of the most striking is the daily alternation between light and darkness. This leads to a repeated alternation between two states; a large positive balance of energy and carbon in the light period, and a deficit in the dark period. It is buffered by storing some newly fixed C as starch, and remobilizing it during the night ([Smith and Stitt, 2007][1]). We do not know how energetically expensive processes like protein synthesis are coordinated with these dramatic diurnal changes in the energy budget. Plants also experience changes in the environment over a time range of days or weeks due to changing weather patterns and seasons, raising the question how they integrate their response over a wide range of time spans. Although there are large diurnal changes for thousands of transcripts ([Usadel et al , 2008][2]), most enzymes show only small diurnal changes in their maximum activity, and require days to adjust when plants are transferred to new conditions. [Gibon et al (2004b)][3] proposed that translation is so slow that several days are required to produce a major change in protein abundance. This would buffer enzymatic capacities against recurring changes caused by the light–dark cycle, while allowing them to adjust to sustained changes in the surroundings. To test this hypothesis, it was necessary to obtain information about rates of protein synthesis. Protein synthesis occurs by the recruitment of transcripts to ribosomes, to form polysomes. The rate of translation of a given transcript species depends upon transcript abundance, the proportion present in polysomes, the number of ribosomes present on the transcript, and their speed of progression along the transcript. Real time RT–PCR combined with spiked external RNA standards was used to quantify the abundance of cytosolic, plastidic and mitochondrial ribosomal RNA and 98 transcripts, including 84 for enzymes involved in central metabolism. This was done in whole rosettes, and in fractions from polysome density gradients. The estimated rates of synthesis of proteins varied by >1000‐fold. The measurements were carried out in the dark and the light periods. Ribosomal and transcript loading into polysomes and the estimated rates of synthesis of most proteins decreased by up to twofold in the dark. Protein abundance was estimated in two ways; from maximum enzyme activities, corrected by literature values for specific activity, and from MS analyses using the emPAI index. Comparison of the resulting estimates for protein abundance revealed a highly significant agreement (Pearson's R 2=0.590). The estimated rates of protein synthesis were compared with protein abundance to calculate how many days it would require to synthesize all the protein in an Arabidopsis rosette ( T P, this term is used instead of T 0.5, because part of the newly synthesized protein represents the flux to growth). For some enzymes like nitrate reductase and ADP glucose pyrophosphorylase, the estimated rate of synthesis was high compared with their abundance. These enzymes also show marked changes of their maximum activity during diurnal cycles. However, estimated rates of synthesis of most enzymes were low compared with the amount present in the leaf, resulting in a T P of four or more days. This resembles that needed for growth. An independent estimate of the global rate of protein synthesis was obtained using information about the ribosome concentration (about 0.1 nmol ribosomes g−1 FW). This would support a maximum rate of protein synthesis of approximately 3 mg protein g−1 FW day−1, allowing synthesis of all proteins in the rosette (15 mg g−1 FW) in about 5 days, resembling the rate required for the observed rate of growth. There are considerable energetic costs associated with protein synthesis; conversion of one molecule of nitrate to amino acids requires 5 ATP molecules, and addition of an amino acid to a growing peptide chain requires >5 ATP molecules. ATP is supplied by photophosphorylation in the light period, and by respiration in the dark period. The latter is thermodynamically less efficient, and requires transient storage...

The 2-C-methylerythritol 4-phosphate (MEP) pathway supplies precursors for plastidial isoprenoid biosynthesis including carotenoids, redox cofactor side chains, and biogenic volatile organic compounds. We examined the first enzyme of this pathway, 1-deoxyxylulose 5-phosphate synthase (DXS), using metabolic control analysis. Multiple Arabidopsis (Arabidopsis thaliana) lines presenting a range of DXS activities were dynamically labeled with ¹³CO₂ in an illuminated, climate-controlled, gas exchange cuvette. Carbon was rapidly assimilated into MEP pathway intermediates, but not into the mevalonate pathway. A flux control coefficient of 0.82 was calculated for DXS by correlating absolute flux to enzyme activity under photosynthetic steady-state conditions, indicating that DXS is the major controlling enzyme of the MEP pathway. DXS manipulation also revealed a second pool of a downstream metabolite, 2-C-methylerythritol-2,4-cyclodiphosphate (MEcDP), metabolically isolated from the MEP pathway. DXS overexpression led to a 3- to 4-fold increase in MEcDP pool size but to a 2-fold drop in maximal labeling. The existence of this pool was supported by residual MEcDP levels detected in dark-adapted transgenic plants. Both pools of MEcDP are closely modulated by DXS activity, as shown by the fact that the concentration control coefficient of DXS was twice as high for MEcDP (0.74) as for 1-deoxyxylulose 5-phosphate (0.35) or dimethylallyl diphosphate (0.34). Despite the high flux control coefficient for DXS, its overexpression led to only modest increases in isoprenoid end products and in the photosynthetic rate. Diversion of flux via MEcDP may partly explain these findings and suggests new opportunities to engineer the MEP pathway.

Summary: The Python Simulator for Cellular Systems (PySCeS) is an extendable research tool for the numerical analysis and investigation of cellular systems. Availability: PySCeS is distributed as Open Source Software under the GNU General Public Licence and is available for download from http://pysces.sourceforge.net Contact:bgoli@sun.ac.za

Natural genetic diversity provides a powerful resource to investigate how networks respond to multiple simultaneous changes. In this work, we profile maximum catalytic activities of 37 enzymes from central metabolism and generate a matrix to investigate species-wide connectivity between metabolites, enzymes, and biomass. Most enzyme activities change in a highly coordinated manner, especially those in the Calvin-Benson cycle. Metabolites show coordinated changes in defined sectors of metabolism. Little connectivity was observed between maximum enzyme activities and metabolites, even after applying multivariate analysis methods. Measurements of posttranscriptional regulation will be required to relate these two functional levels. Individual enzyme activities correlate only weakly with biomass. However, when they are used to estimate protein abundances, and the latter are summed and expressed as a fraction of total protein, a significant positive correlation to biomass is observed. The correlation is additive to that obtained between starch and biomass. Thus, biomass is predicted by two independent integrative metabolic biomarkers: preferential investment in photosynthetic machinery and optimization of carbon use.

The kinetic parameters in vitro of the components of the phosphoenolpyruvate:glycose phosphotransferase system (PTS) in enteric bacteria were collected. To address the issue of whether the behavior in vivo of the PTS can be understood in terms of these enzyme kinetics, a detailed kinetic model was constructed. Each overall phosphotransfer reaction was separated into two elementary reactions, the first entailing association of the phosphoryl donor and acceptor into a complex and the second entailing dissociation of the complex into dephosphorylated donor and phosphorylated acceptor. Literature data on theK _m values and association constants of PTS proteins for their substrates, as well as equilibrium and rate constants for the overall phosphotransfer reactions, were related to the rate constants of the elementary steps in a set of equations; the rate constants could be calculated by solving these equations simultaneously. No kinetic parameters were fitted. As calculated by the model, the kinetic parameter values in vitro could describe experimental results in vivo when varying each of the PTS protein concentrations individually while keeping the other protein concentrations constant. Using the same kinetic constants, but adjusting the protein concentrations in the model to those present in cell-free extracts, the model could reproduce experiments in vitro analyzing the dependence of the flux on the total PTS protein concentration. For modeling conditions in vivo it was crucial that the PTS protein concentrations be implemented at their high in vivo values. The model suggests a new interpretation of results hitherto not understood; in vivo, the major fraction of the PTS proteins may exist as complexes with other PTS proteins or boundary metabolites, whereas in vitro, the fraction of complexed proteins is much smaller.

The effect of different total enzyme concentrations on the flux through the bacterial phospho enol pyruvate:carbohydrate phosphotransferase system (PTS) in vitro was determined by measuring PTS-mediated carbohydrate phosphorylation at different dilutions of cell-free extract of Escherichia coli . The dependence of the flux on the protein concentration was more than linear but less than quadratic. The combined flux–response coefficient of the four enzymes constituting the glucose PTS decreased slightly from values of ≈1.8 with increasing protein concentrations in the assay. Addition of the macromolecular crowding agents polyethylene glycol (PEG) 6000 and PEG 35000 led to a sharper decrease in the combined flux–response coefficient, in one case to values of ≈1. PEG 6000 stimulated the PTS flux at lower protein concentrations and inhibited the flux at higher protein concentrations, with the transition depending on the PEG 6000 concentration. This suggests that macromolecular crowding decreases the dissociation rate constants of enzyme complexes. High concentrations of the microsolute glycerol did not affect the combined flux–response coefficient. The data could be explained with a kinetic model of macromolecular crowding in a two-enzyme group-transfer pathway. Our results suggest that, because of the crowded environment in the cell, the different PTS enzymes form complexes that live long on the time-scale of their turnover. The implications for the metabolic behavior and control properties of the PTS, and for the effect of macromolecular crowding on nonequilibrium processes, are discussed.