In the past decade, over 50 genome-scale metabolic reconstructions have been
built for a variety of single- and multi- cellular organisms. These
reconstructions have enabled a host of computational methods to be leveraged for
systems-analysis of metabolism, leading to greater understanding of observed
phenotypes. These methods have been sparsely applied to comparisons between
multiple organisms, however, due mainly to the existence of differences between
reconstructions that are inherited from the respective reconstruction processes
of the organisms to be compared. To circumvent this obstacle, we developed a
novel process, termed metabolic network reconciliation, whereby non-biological
differences are removed from genome-scale reconstructions while keeping the
reconstructions as true as possible to the underlying biological data on which
they are based. This process was applied to two organisms of great importance to
disease and biotechnological applications, Pseudomonas
aeruginosa and Pseudomonas putida, respectively.
The result is a pair of revised genome-scale reconstructions for these organisms
that can be analyzed at a systems level with confidence that differences are
indicative of true biological differences (to the degree that is currently
known), rather than artifacts of the reconstruction process. The reconstructions
were re-validated with various experimental data after reconciliation. With the
reconciled and validated reconstructions, we performed a genome-wide comparison
of metabolic flexibility between P. aeruginosa and P.
putida that generated significant new insight into the underlying
biology of these important organisms. Through this work, we provide a novel
methodology for reconciling models, present new genome-scale reconstructions of
P. aeruginosa and P. putida that can be
directly compared at a network level, and perform a network-wide comparison of
the two species. These reconstructions provide fresh insights into the metabolic
similarities and differences between these important
Pseudomonads, and pave the way towards full comparative
analysis of genome-scale metabolic reconstructions of multiple species