Poster Title:Using Computations to Reconstruct, Analyze and Redesign Metabolism
Authors: Patrick F. Suthers, Alireza Zomorrodi, Sridhar Ranganathan, Vinay Satish Kumar and Costas D. Maranas
Poster Abstract: Metabolism is defined as the full complement of chemical transformations in living systems. Systems biology techniques are increasingly being used to elucidate and quantify the full range of molecules (e.g., metabolite concentrations) and transformations (e.g., reaction fluxes) at play. We will explore how computations can shed light onto the interdependence, fragility and redundancy of metabolism in living systems. We will discuss how we can pinpoint the essential core of genes needed to ensure life’s processes to better understand the organizational principles of metabolism and provide insights into the design of minimal organisms. We will next explore how we can speed up the process of building organism-specific metabolic models by automatically filling in connectivity gaps and restoring consistency with gene essentiality experiments. We will highlight ongoing genome-scale reconstruction efforts in our group (i.e., Mycoplasma genitalium, Salmonella enterica, etc.). Given these metabolic reconstructions, we will next discuss progress in the development of genome-scale isotope maps and the use of computations for deciphering metabolic flows given NMR or GC/MS spectra derived from C13 labeling experiments. Finally, optimization based techniques will be described for strain optimization by classifying all fluxes in metabolic models depending upon whether or not they must increase, decrease, or become equal to zero to meet a pre-specified overproduction target. The developed methodology was tested by exhaustively identifying all engineering interventions for various products including succinate as well as various chemicals identified as promising biofuels. The method recapitulates straight forward engineering targets but also reveals new non-intuitive ones that boost productivity by performing coordinated changes on seemingly unrelated pathways.