Genomic data now allow the large-scale manual or semi-automated reconstruction of metabolic networks. A network reconstruction represents a highly curated organism-specific knowledge base. A few genome-scale network reconstructions have appeared for metabolism in the baker’s yeast Saccharomyces cerevisiae. These alternative network reconstructions differ in scope and content, and further have used different terminologies to describe the same chemical entities, thus making comparisons between them difficult. The formulation of a ‘community consensus’ network that collects and formalizes the ‘community knowledge’ of yeast metabolism is thus highly desirable. We describe how we have produced a consensus metabolic network reconstruction for S. cerevisiae. Special emphasis is laid on referencing molecules to persistent databases or using database-independent forms such as SMILES or InChI strings, since this permits their chemical structure to be represented unambiguously and in a manner that permits automated reasoning. The reconstruction is readily available via a publicly accessible database and in the Systems Biology Markup Language, and we describe the manner in which it can be maintained as a community resource. It should serve as a common denominator for system biology studies of yeast. Similar strategies will be of benefit to communities studying genome-scale metabolic networks of other organisms.

Changes in protein conformation can affect protein function, but methods to probe these structural changes on a global scale in cells have been lacking. To enable large-scale analyses of protein conformational changes directly in their biological matrices, we present a method that couples limited proteolysis with a targeted proteomics workflow. Using our method, we assessed the structural features of more than 1,000 yeast proteins simultaneously and detected altered conformations for ~300 proteins upon a change of nutrients. We find that some branches of carbon metabolism are transcriptionally regulated whereas others are regulated by enzyme conformational changes. We detect structural changes in aggregation-prone proteins and show the functional relevance of one of these proteins to the metabolic switch. This approach enables probing of both subtle and pronounced structural changes of proteins on a large scale.

Background: Uncovering the operating principles underlying cellular processes by using 'omics' data is often a difficult task due to the high-dimensionality of the solution space that spans all interactions among the bio-molecules under consideration. A rational way to overcome this problem is to use the topology of bio-molecular interaction networks in order to constrain the solution space. Such approaches systematically integrate the existing biological knowledge with the 'omics' data.

A strategy is presented that combines metabolic fluxes with targeted phosphoproteomics measurements to drive testable hypotheses for the functionality of post-translational regulation in S. cerevisiae central metabolism.

Background: Genome-scale flux models are useful tools to represent and analyze microbial metabolism. In this work we reconstructed the metabolic network of the lactic acid bacteria Lactococcus lactis and developed a genome-scale flux model able to simulate and analyze network capabilities and whole-cell function under aerobic and anaerobic continuous cultures. Flux balance analysis (FBA) and minimization of metabolic adjustment (MOMA) were used as modeling frameworks.