Emergence of Orchestrated and Dynamic Metabolism of Saccharomyces cerevisiae.

Microbial metabolism is a fundamental cellular process that involves many biochemical events and is distinguished by its emergent properties. While the molecular details of individual reactions have been increasingly elucidated, it is not well understood how these reactions are quantitatively orchestrated to produce collective cellular behaviors. Here we developed a coarse-grained, systems, and dynamic mathematical framework, which integrates metabolic reactions with signal transduction and gene regulation to dissect the emergent metabolic traits of Saccharomyces cerevisiae. Our framework mechanistically captures a set of characteristic cellular behaviors, including the Crabtree effect, diauxic shift, diauxic lag time, and differential growth under nutrient-altered environments. It also allows modular expansion for zooming in on specific pathways for detailed metabolic profiles. This study provides a systems mathematical framework for yeast metabolic behaviors, providing insights into yeast physiology and metabolic engineering.

The Reporting Recommendations Intended for Pharmaceutical Risk Minimization Evaluation Studies: Standards for Reporting of Implementation Studies Extension (RIMES-SE).

INTRODUCTION: The Reporting recommendations Intended for pharmaceutical risk Minimization Evaluation Studies (RIMES) was developed to improve the quality of reporting of risk minimization program evaluations. In light of continued inadequacies in study reporting, and high-profile program implementation failures, we updated the RIMES Checklist to incorporate additional concepts from the Standards for Reporting of Implementation studies (StaRI). METHODS: The development of the updated checklist, the RIMES-StaRI Extension (RIMES-SE), entailed developing a study protocol and drafting an initial pool of items based on a mapping of the RIMES against the StaRI checklist. A modified e-Delphi exercise was then conducted to determine the importance and understandability of items for checklist inclusion. An expert workshop and an online commentary period for additional feedback followed. RESULTS: The RIMES-SE contains 27 items. It includes two signature features of the StaRI Checklist: 1) a dual strand of items (represented in two columns) describing the risk minimization program (the 'intervention') and the corresponding implementation strategy; and 2) applicable to an array of different research methodologies. CONCLUSIONS: The RIMES-SE Statement and Checklist extends the reporting guidelines set forth in the original RIMES Checklist via inclusion of key implementation science concepts. It is intended to improve the quality and transparency of reporting of risk minimization evaluation studies so as to advance drug safety science.

Controlling circuitry underlies the growth optimization of Saccharomyces cerevisiae.

Microbial growth emerges from coordinated synthesis of various cellular components from limited resources. In Saccharomyces cerevisiae, cyclic AMP (cAMP)-mediated signaling is shown to orchestrate cellular metabolism; however, it remains unclear quantitatively how the controlling circuit drives resource partition and subsequently shapes biomass growth. Here we combined experiment with mathematical modeling to dissect the signaling-mediated growth optimization of S. cerevisiae. We showed that, through cAMP-mediated control, the organism achieves maximal or nearly maximal steady-state growth during the utilization of multiple tested substrates as well as under perturbations impairing glucose uptake. However, the optimal cAMP concentration varies across cases, suggesting that different modes of resource allocation are adopted for varied conditions. Under settings with nutrient alterations, S. cerevisiae tunes its cAMP level to dynamically reprogram itself to realize rapid adaptation. Moreover, to achieve growth maximization, cells employ additional regulatory systems such as the GCN2-mediated amino acid control. This study establishes a systematic understanding of global resource allocation in S. cerevisiae, providing insights into quantitative yeast physiology as well as metabolic strain engineering for biotechnological applications.