Steady state metabolic network dynamics analysis (SMDA) is a recently proposed computational metabolomics tool that (i) captures a metabolic network and its rules via a metabolic network database, (ii) mimics the reasoning of a biochemist, given a set of metabolic observations, and (iii) locates efficiently all possible metabolic activation/inactivation (flux) alternatives. However, a number of factors may cause the SMDA algorithm to eliminate feasible flux scenarios. These factors include (i) inherent error margins in observations (measurements), (ii) lack of knowledge to classify measurements as normal versus abnormal, and (iii) choosing a highly constrained metabolic subnetwork to query against. In this work, we first present and formalize these obstacles. Then, we propose techniques to eliminate them and present an experimental evaluation of our proposed techniques.