Acidithiobacillus (A.) ferrooxidans is an important biomining microorganism which plays a role in the industrial extraction of metals from sulfide ores due to its ability to obtain energy from the oxidation of iron and sulfur. During industrial processes, A. ferrooxidans is subjected to extreme stresses, including low pH, nutrient depletion, oxidative stress and exposure to toxic heavy metals, high temperatures and high salinity. Numerous studies have been carried out to identify the mechanisms used by A. ferrooxidans to survive these stresses. The sequencing of the genomes of members of the Acidithiobacillus species has provided crucial insights into the metabolic capabilities of these microorganisms. However, genome sequences alone are unable to predict physiological outcomes and metabolic capabilities of cells as a whole integrated system. Implementing constraint based reconstruction analysis can be a useful tool to overcome this challenge. Constraint based metabolic model reconstruction involves, i) systematically organizing information from genome annotations and ‘omics’ data sets in order to identify genes, proteins, reactions and metabolites that participate in important metabolic pathways, ii) setting constraints so that internal reaction rates (fluxes) of metabolites can be computed on the basis of a mathematical stoichiometric pathway model. Correlating metabolic pathways and the predictive outcomes of changes in metabolites and reactions, in turn, can be used to qualitatively and quantitatively elucidate biological information and allow better understanding of metabolic capabilities under different scenarios. Metabolic modelling and flux balance analyses have been extensively performed for a range of prokaryotes, eukaryotes and archaea used in medical, biotechnological and environmental applications, but only few studies have been performed on biomining microorganisms. Therefore, generating constraint based metabolic flux models for biomining microorganisms such as A. ferrooxidans under different environmental stresses provides a way of in silico determination of metabolic capabilities associated with the growth, survival and functionality of these microorganisms, which can ultimately help to inform enhanced extraction of metals from sulfide ores.