Predicting growth optimization strategies with metabolic/expression models
Systems biology strives to understand complex multi-component biological processes and capture knowledge of their function through models. With metabolic and gene expression models (ME-models), we can mathematically and simultaneously represent the majority of these processes, including transcription, translation, and metabolism. This enables us to compute the molecular constituents of a cell as a function of genetic and environmental parameters. ME-models represent an improvement in current capabilities to predict phenotypes, as demonstrated by the reconstruction and validation of a ME-model for the acetogen Clostridium ljungdahlii. C. ljungdahlii can grow autotrophically on carbon monoxide (CO), and/or carbon dioxide + hydrogen (CO2+H2) and fix these gases into multicarbon organics, an ability that can be redirected to produce biocommodities. The C. ljungdahlii ME-model was able to improve growth rate predictions, identify previously unknown secretion products, and compute the transcriptome of C. ljungdahlii accurately.
ME-models offer the opportunity to systematically explore the interface between protein and function. First, perturbations of tRNA co-expression in ME-models revealed unique organization solutions to two different selective pressures: Optimization of growth through minimal co-expression of tRNAs, and efficiency of resources through optimal grouping of tRNAs. Second, because of the incorporation of protein translocation and membrane function, a ME-model was able to recapitulate acetate production during glucose consumption due to membrane overcrowding. Third, a ME-model highlighted how variations in nickel availability impacts metalloproteins, thereby controlling growth and secretion rates of fermentation products. Thus, three features that could constrain the proteome of an organism – genome architecture, ultra-structure, and media requirements – were successfully interrogated using ME-models.