Motivation
The understanding of metabolic interactions has grown rapidly in recent years with metabolic network reconstructions constantly increasing in scope and quality. These networks can be manipulated into models which allow for fluxes to be estimated using methods such as flux balance analysis. However, the solution space of fluxes is large, and the inability to provide dynamic behaviour of the system can make understanding it under changing conditions difficult. It is under these circumstances where kinetic models can prove more valuable. However, collecting kinetic information for large scale networks is time consuming and expensive, and existing data is difficult to find and collate.
Results
We develop and validate a novel methodology which uses the flux information of one steady state to define the kinetics of the system and produce a reasonable approximation of the steady state behaviour of the real system. We show that first approximation models, in the first instance, show a poor ability to extrapolate beyond the initial conditions. However, metabolic control analysis directs our attention towards the most important reactions of the network. When experimentally-elucidated kinetics are used for those reactions, and the remainder of the network modelled using empirical rate laws that are inspired in enzyme kinetics, predictability improves dramatically