A flexible state-space approach for the modeling of metabolic networks I: Development of mathematical methods

We introduce a novel, flexible, optimization-based mathematical framework for the modeling of arbitrarily complex metabolic networks: topological metabolic analysis (TMA). The framework is adapted from state-space approaches used by Manousiouthakis and co-workers for the representation of complex heat- and mass-exchanger networks. We offer a thorough discussion of the mathematics and general theory underlying the framework, and discuss certain mathematical advantages of our modeling representation in comparison with other commonly used techniques (MFA and FBA). We employ a novel aggregate objective function for use with our basic constraint model, including a generalized least-squares term (for fitting available experimental measurements) and a linear design term (for representing biological or engineering goals). Using a case-study taken from recent literature ( McKinlay et al., 2007), we demonstrate (among other benefits) the ability of this objective to identify alternate distinct-yet-equally optimal solutions for a given modeling problem. We also show that these solutions, obtained using only external metabolite uptake and secretion measurements, provide useful biological insights and compare favorably with solutions obtained on the basis of 13C isotope-tracing data.