Metabolic modeling has proven to be a valuable tool in the investigation of cellular physiology, especially in the post-genomic era. However, the sheer complexity of even the simplest metabolic networks obstructs the exact determination of intracellular states, despite the ample supply of –omics data. To overcome this difficulty we attempt to postulate simplifying assumptions, relevant to the conditions and available data. Identifying the gamut of possible feasible states allows us to query for factors that are crucial in interpreting the observed physiology or in developing metabolic engineering strategies. Towards this end, we developed a framework called Flux Directionality Profile Analysis (FDPA) that enables both the complete enumeration and the characterization of all possible intracellular flux states. The proposed methodology allows for the seamless incorporation of available experimental data, when available, such as reaction fluxes, uptake rates, intra- and extra-cellular metabolite concentration measurements and splitting ratios at metabolic branching points. Additionally, FDPA employs a set of speculative metabolic objectives in order to rank the thermodynamically feasible intracellular flux states based on their performance against one (or more) of these objectives. We are therefore able to isolate internal flux states that are both thermodynamically consistent and relevant to the observed physiology.