Genome-wide metabolic reconstructions have been widely applied to study metabolism at a genome scale. To date, most of the work in the field has been performed in the study of unicellular organisms, however, and many of the methods developed in this context do not transfer for the study of mammalian systems. In particular, (1) the larger size of mammalian reconstructions makes the application of computationally expensive algorithms infeasible. Also, (2) the optimization of a cellular objective, commonly defined to be biomass production in unicellular organisms, does not transfer to mammalian cells, where a cellular objective is neither well defined nor optimized. Finally, (3) the generalized human reconstruction needs to be tailored to specific tissues or cell lines for a context specific analysis, since only a subset of the metabolism defined in the genome takes place in each cell.
In this project, we aim to develop better methods for the analysis of mammalian systems using genome-scale models. We demonstrate that (1) the removal of currency metabolites and energy related loops from the model leads to a more feasible and biologically relevant application of pathway decomposition analysis. We also show that merging sets of fully coupled reactions, and using a combination of two algorithms, leads to a significantly faster implementation of Monte-Carlo sampling. Furthermore, (2) by fixing the cellular objective and optimizing metabolic resources, we demonstrate that a sub-optimal objective oriented approach can significantly improve flux prediction results. Finally, (3) we present a context-specific algorithm that is faster and yields better tissue-specific predictions when compared to previous methods.
After validating these methods, we apply them to the study of cancer cells in a subtype specific manner, and to the study of hypoxia adaptation in deer mice cardiomyocytes. Results from our predictions provided biological insight in both applications: including the role of Hexosamine synthesis pathways as an energy regulator in cancer cells, and the role of evolution in the adaptation of deer mice to altitude conditions.