470815 Thermodynamic Analysis of the Rigidity of Metabolic Nodes Via a Dynamic Flux Balance Approach

Wednesday, November 16, 2016: 9:24 AM
Continental 8 (Hilton San Francisco Union Square)
Tolutola Oyetunde, Energy, Environmental and Chemical Engineering, Washington University in St. Louis, St. Louis, MO, Yinjie J. Tang, Department of Energy, Environmental and Chemical Engineering, Washington University, St. Louis, MO and Cynthia Lo, Washington University in St. Louis, St. Louis, MO

The knowledge of operation and regulation of the fluxes in metabolic networks enables rational design and manipulation of cell metabolism for medical or industrial purposes. To understand the functions and flexibility of metabolic networks, quantification of time-dependent changes of intracellular metabolite concentrations and metabolic turnovers under various internal and external perturbations are crucial. However, experimental determination of intracellular metabolite concentrations is tedious and challenging.

Our approach enables the elucidation of the dynamic relationship among the reaction driving forces (Gibbs free energies), metabolite pool sizes and flux rates. This allows us to quantify the relative rigidity of different metabolite nodes as a function of the net thermodynamic driving forces. We demonstrate our method using a published genome scale E. colimodel (iAF1260: http://systemsbiology.ucsd.edu/InSilicoOrganisms/Ecoli/EcoliSBML) by following steps. First, we perform a dynamic flux balance analysis. This step employs a pseudo steady state technique using simple kinetic growth experimental data as constraints, which predicts the time-rate change of intracellular concentrations. Second, the metabolite concentration bounds can be narrowed by using Gibbs free energy constrained flux balance analysis for each pseudo steady state. Third, the dynamics of intracellular metabolite concentrations and the time-rate change of Gibbs free energy are resolved using a set of self-consistent calculations based on the thermodynamic equilibrium relationship.

Our computational framework employs Gibbs free energies for revealing metabolite pool sizes and node rigidity under different genetic and environmental perturbations. The simulations are largely consistent with the reported experimental measurements of intracellular metabolites. Our methodology used in conjunction with other constraint based techniques provides guidelines for metabolism analysis and rational metabolic engineering.

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See more of this Session: In silico Systems Biology
See more of this Group/Topical: Food, Pharmaceutical & Bioengineering Division