Essential metabolism for a minimal cell.

JCVI-syn3A, a robust minimal cell with a 543 kbp genome and 493 genes, provides a versatile platform to study the basics of life. Using the vast amount of experimental information available on its precursor, , we assembled a near-complete metabolic network with 98% of enzymatic reactions supported by annotation or experiment. The model agrees well with genome-scale in vivo transposon mutagenesis experiments, showing a Matthews correlation coefficient of 0.

Population FBA predicts metabolic phenotypes in yeast.

Using protein counts sampled from single cell proteomics distributions to constrain fluxes through a genome-scale model of metabolism, Population flux balance analysis (Population FBA) successfully described metabolic heterogeneity in a population of independent Escherichia coli cells growing in a defined medium. We extend the methodology to account for correlations in protein expression arising from the co-regulation of genes and apply it to study the growth of independent Saccharomyces cerevisiae cells in two different growth media. We find the partitioning of flux between fermentation and respiration predicted by our model agrees with recent 13C fluxomics experiments, and that our model largely recovers the Crabtree effect (the experimentally known bias among certain yeast species toward fermentation with the production of ethanol even in the presence of oxygen), while FBA without proteomics constraints predicts respirative metabolism almost exclusively.

Towards a computational model of a methane producing archaeum.

Progress towards a complete model of the methanogenic archaeum Methanosarcina acetivorans is reported. We characterized size distribution of the cells using differential interference contrast microscopy, finding them to be ellipsoidal with mean length and width of 2.9  μ m and 2.

Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population.

Stochastic gene expression can lead to phenotypic differences among cells even in isogenic populations growing under macroscopically identical conditions. Here, we apply flux balance analysis in investigating the effects of single-cell proteomics data on the metabolic behavior of an in silico Escherichia coli population. We use the latest metabolic reconstruction integrated with transcriptional regulatory data to model realistic cells growing in a glucose minimal medium under aerobic conditions.