Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism.

Diauxie, or the sequential consumption of carbohydrates in bacteria such as Escherichia coli, has been hypothesized to be an evolutionary strategy which allows the organism to maximize its instantaneous specific growth-giving the bacterium a competitive advantage. Currently, the computational techniques used in industrial biotechnology fall short of explaining the intracellular dynamics underlying diauxic behavior. In particular, the understanding of the proteome dynamics in diauxie can be improved. We developed a robust iterative dynamic method based on expression- and thermodynamically enabled flux models to simulate the kinetic evolution of carbohydrate consumption and cellular growth. With minimal modeling assumptions, we couple kinetic uptakes, gene expression, and metabolic networks, at the genome scale, to produce dynamic simulations of cell cultures. The method successfully predicts the preferential uptake of glucose over lactose in E. coli cultures grown on a mixture of carbohydrates, a manifestation of diauxie. The simulated cellular states also show the reprogramming in the content of the proteome in response to fluctuations in the availability of carbon sources, and it captures the associated time lag during the diauxie phenotype. Our models suggest that the diauxic behavior of cells is the result of the evolutionary objective of maximization of the specific growth of the cell. We propose that genetic regulatory networks, such as the lac operon in E. coli, are the biological implementation of a robust control system to ensure optimal growth.

Metabolic Modeling with MetaFlux.

The MetaFlux software supports creating, executing, and solving quantitative metabolic flux models using flux balance analysis (FBA). MetaFlux offers four modes of operation: (1) solving mode executes an FBA model for an individual organism or for an organism community, (2) gene knockout mode executes an FBA model with one or many gene knockouts, (3) development mode assists the user in creating and improving FBA models, and (4) flux variability analysis mode generates a report of the robustness of an FBA model. MetaFlux also solves dynamic FBA (dFBA) for both individual organisms and communities of organisms. MetaFlux can be used in two different environments: on your local computer, which requires the installation of the Pathway Tools software, or through the web, which does not require installation of Pathway Tools. On your local computer, MetaFlux offers all four modes of operation, whereas the web environment provides only the solving mode.Several visualization tools are available to analyze model solutions. The Cellular Overview tool graphically shows the reaction fluxes on an organism's metabolic map once a model is solved. The Omics Dashboard provides a hierarchical approach to visualizing reaction fluxes, organized by metabolic subsystems. For a community of organisms, plotting of accumulated biomasses and metabolites can be performed using the Gnuplot tool.In this chapter, we present eight methods using MetaFlux. Five solving mode methods illustrate execution of models for individual organisms and for organism communities. One method illustrates the gene knockout mode. Two methods for the development mode illustrate steps for developing new metabolic models.