Changes in cellular metabolism can be deduced from how they affect the measurable metabolites in cell samples. We provide methods to compute metabolic reaction rates from changes in measurable metabolites over time. The methods provided are intended to overcome technical challenges, such as the inapplicability of a steady state assumption, heterogeneity of samples from different donors, and the lack of targeted metabolomics data. Solutions to these challenges involve identifying metabolites constrained even under non-steady state, using components analysis to find the donor consensus, and using an integer linear program to solve a set cover variant designed to generate targeted data from untargeted data. The methods are applied on data derived from diseased articular cells.

The results show that the reaction rates inferred from the incomplete data are biologically relevant, and that the minimal pathways captured ancillary processes that alternative approaches ignored. We conclude that, although the resulting rates and pathways are not conclusive, they provide useful guidance on experiments to pursue. On the experimental side, our findings have lead us to believe that osteoarthritic chondrocytes respond to compression by initiating protein synthesis, opening the possibility of physical therapy as a stimulus for cartilage regeneration.