New Method to Compare Organism Functionality

CONGA can identify differences in reaction and gene content

The Science

Systems biology approaches to bioenergy and environmental research are enabled by reliable models of processes in living cells. Advances in genome sequencing and computational modeling have led to the development of over 100 genome-scale network reconstructions (constraint-based models). Rapid increases in this number are expected, so methods that use algorithms to compare functional characteristics between organisms will be increasingly important. Scientists at the University of Wisconsin have reported a novel approach that embeds two constraint-based models into an optimization model. This combination identifies those genes and reaction pathways that contribute most to differences in metabolic functionality. The authors identified several differences in metabolism in two cyanobacteria that have potential for biofuel production, Synechococcus and Cyanothece. For example, they demonstrated the necessity for a particular protein (plastocyanin) for photosynthesis in Cyanothece, but not in Synechococcus. The new approach also aids the curation of constraint-base models by identifying pathways that are coded by the organism, but that are missing from the model.

Summary

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. Researchers have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented in the article.

Principal Investigator

Jennifer L. Reed
University of Wisconsin–Madison

Co-Principal Investigator

Joshua J. Hamilton
University of Wisconsin–Madison

References

Hamilton, J. J., and J. L. Reed. 2012. “Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models,” PLoS ONE 7(4), e34670. DOI:10.1371/journal.pone.0034670.