Estimation of dynamic flux profiles from metabolic time series data

The authors propose here a systematic approach that supplements DFE and overcomes some of its shortcomings. Like DFE, the approach is model-free and requires only minimal assumptions. If sufficient time series data are available, the approach allows the determination of a subset of fluxes that enables the subsequent applicability of DFE to the rest of the flux system. The authors demonstrate the procedure with three artificial pathway systems exhibiting distinct characteristics and with actual data of the trehalose pathway in Saccharomyces cerevisiae.The results demonstrate that the proposed method successfully complements DFE under various situations and without a priori assumptions regarding the model representation. The proposed method also permits an examination of whether at all, to what degree, or within what range the available time series data can be validly represented in a particular functional format of a flux within a pathway system. Based on these results, further experiments may be designed to generate data points that genuinely add new information to the structure identification and parameter estimation tasks at hand.A grand challenge of biomathematical modeling is the conversion of a biological system into a computational structure that formalizes the underlying system. An important and very challenging component of this process is the estimation of parameter values. The task is typically pursued with one of two generic approaches, namely a forward (bottom-up) or an inverse (top-down) method. Until recently, essentially all models of metabolic pathway systems were developed according to the first strategy, that is, by characterizing model components and processes one at a time and subsequently merging all “local” information about kinetic reaction steps into one comprehensive dynamic model. Although this forward approach is theoretically straightforward, implementation procedures often fail and, moreover, have intrinsic disadvantages [1]. For instanc