Skip to main content
eScholarship
Open Access Publications from the University of California

UC San Diego

UC San Diego Electronic Theses and Dissertations bannerUC San Diego

Predicting growth optimization strategies with metabolic/expression models

  • Author(s): Liu, Joanne
  • Advisor(s): Zengler, Karsten;
  • Lewis, Nathan
  • et al.
Abstract

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.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View