In order to identify targets for metabolic engineering interventions or understand the physiology of the cell, the first and foremost prerequisite is the knowledge or a hypothesis about the intracellular network state(s). However, even with the advanced metabolomics and fluxomics methods currently at our disposal, it is still impossible or rather difficult to determine the exact intracellular flux states due to the number of reactions in the real system compounded with the reversibility of many of these reactions. As such, it may not be feasible to elucidate exactly the single state of the cell but to propose what could be all the possible states under given fermentation conditions, and ask what are the most important and consistent factors for achieving our metabolic engineering target given these states. In this contribution, we developed a framework for identifying all the possible intracellular flux and thermodynamic states using available experimental information provided, such as substrates/products profiles and any additional information such as metabolite concentrations and regulatory constraints that can be systematically integrated to differentiate all the possible directional profiles. Using the methods from Thermodynamics-based Flux Balance Analysis (TFBA), we then perform sampling of the flux and metabolite concentration states, which ensures network thermodynamic consistency, and we employ statistical techniques in order to extract representative flux and concentration profiles for the different network states. With these representative flux and concentration states, we can next perform kinetic analysis in order to identify targets for metabolic engineering and select those that are consistently important across these possible states.