Metabolites consumed and produced by microorganisms for mass and energy conservation may cause changes in a microorganism’s environment. The microorganisms are unable to tolerate a particular environment for a long period. They may leave their old existence to find a new environment to sustain life. Essentially, organisms need to maintain their metabolic processes to survive in the new environment. Limitations of experimental studies to explore cell functions and regulations in detail result in insufficient information to explain processes of metabolic expressions under environments of organisms. Consequently, mathematical modeling and computer simulations have been conducted to combine all possible cellular metabolic fluxes into single or multiple connected networks. Metabolic modeling based on linear programming (LP) subjected to constraints with an optimization approach is often applied metabolic reconstruction. The LP objective function is maximized to obtain an optimal value of biomass flux. Optimal solutions in LP problems can be used to explain how metabolites function in metabolic reactions. As an LP problem may have many optimal solutions, this study proposes a method for enumerating all alternate optimal solutions to evaluate important reactions of metabolic pathways in microorganisms. The algorithm for generating alternate optimal solutions is implemented in MetModelGUI, a Java-based software for creating and analyzing metabolic reconstructions. The algorithm is applied to models of five microorganisms: Trypanosoma cruzi, Thermobifida fusca, Helicobacter pylori, Cryptococcus neoformans and Clostridium thermocellum. The results are analyzed using principal component analysis, and insight into the essential and non-essential pathways for each organism is derived

The decreasing costs of next generation sequencing technologies and the increasing speeds at which they work have lead to an abundance of 'omic datasets. The need for tools and methods to analyze, annotate, and model these datasets to better understand biological systems is growing. Here we present a novel software pipeline to reconstruct the metabolic model of an organism in silico starting from its genome sequence and a novel compilation of biological databases to better serve the generation of metabolic models. We validate these methods using five Gardnerella vaginalis strains and compare the gene annotation results to NCBI and the FBA results to Model SEED models. We found that our gene annotations were larger and highly similar in terms of function and gene types to the gene annotations downloaded from NCBI. Further, we found that our FBA models required a minimal addition of transport reactions, sources, and escapes indicating that our draft pathway models were very complete. We also found that on average our solutions contained more reactions than the models obtained from Model SEED due to a large amount of baseline reactions and gene products found in ASGARD.