(562e) Improving the Efficiency of 13C Isotopic Instationary Metabolic Flux Analysis
Isotope-assisted metabolic flux analysis (MFA) is a powerful experimental tool for quantifying metabolism and a cornerstone of metabolic engineering. This technique can be classified into two categories based on how sampling is performed: steady state MFA, which involves sampling at an isotopic steady state or at least a pseudo-steady state, and instationary MFA, which involves multiple, transient sampling before steady state is attained. Compared with steady state MFA, instationary MFA can lead to shorter experimental times and is even indispensable for quantifying fluxes in certain metabolic scenarios such as photosynthetic CO2 assimilation. However, this technique is relatively less popular as compared with steady state MFA due to two major difficulties associated with implementing it. One is the experimental requirement of obtaining precise measurements of pool sizes, or metabolite concentrations, which is unnecessary in steady state MFA. The second, computational hurdle is that instead of algebraic equations, systems of ordinary differential equations (ODEs) need to be solved repeatedly in instationary MFA, which results in huge computational cost. In this presentation we will introduce two ways to reduce this computational burden. Firstly, we will introduce a novel analytical method to solve the ODE systems in instationary MFA. This analytical method requires significantly less computational time over the commonly used numerical method when applied to many common metabolic networks. Additionally, we present a new metabolic network decomposition method that is similar to the concept of ‘bondomers’. While the concept of ‘bondomers’ can only be applied to experiments that employ a mixture of uniformly labeled and naturally abundant carbon sources, the decomposition method presented here can be employed for several more labeling experiment scenarios. We anticipate that the combination of these two improvements will make the solution to the mathematical models of instationary MFA more efficient and thus extend the application of instationary MFA to larger metabolic networks.