title: Probabilistic pathway construction.
abstract: Expression of novel synthesis pathways in host organisms amenable to genetic manipulations has emerged as an attractive metabolic engineering strategy to overproduce natural products, biofuels, biopolymers and other commercially useful metabolites. We present a pathway construction algorithm for identifying viable synthesis pathways compatible with balanced cell growth. Rather than exhaustive exploration, we investigate probabilistic selection of reactions to construct the pathways. Three different selection schemes are investigated for the selection of reactions: high metabolite connectivity, low connectivity and uniformly random. For all case studies, which involved a diverse set of target metabolites, the uniformly random selection scheme resulted in the highest average maximum yield. When compared to an exhaustive search enumerating all possible reaction routes, our probabilistic algorithm returned nearly identical distributions of yields, while requiring far less computing time (minutes vs. years). The pathways identified by our algorithm have previously been confirmed in the literature as viable, high-yield synthesis routes. Prospectively, our algorithm could facilitate the design of novel, non-native synthesis routes by efficiently exploring the diversity of biochemical transformations in nature.
authors.affiliation: Department of Computer Science, Tufts University, Medford, MA 02155, USA.
authors.forename: Mona
authors.initials: M
authors.lastname: Yousofshahi
authors.forename: Kyongbum
authors.initials: K
authors.lastname: Lee
authors.forename: Soha
authors.initials: S
authors.lastname: Hassoun
mesh_terms: D000465:Algorithms; D008954:Models, Biological