title: Gap detection for genome-scale constraint-based models.
abstract: Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism. Several challenges arise when building an in silico constraint-based model of an organism that need to be addressed before flux balance analysis (FBA) can be applied for simulations. An algorithm called FBA-Gap is presented here that aids the construction of a working model based on plausible modifications to a given list of reactions that are known to occur in the organism. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture. The utility of the algorithm is demonstrated in creating a new model organism and is applied to four existing working models for generating hypotheses about culture media. In modifying a partial metabolic reconstruction so that biomass may be produced using FBA, the proposed method is more efficient than a previously proposed method in that fewer new reactions are added to complete the model. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used.
authors.affiliation: Center for the Study of Biological Complexity, Virginia Commonwealth University, P.O. Box 843083, Richmond, VA 23284, USA ; Department of Statistical Sciences and Operations Research, Virginia Commonwealth University, P.O. Box 843083, Richmond, VA 23284, USA.
authors.forename: J Paul
authors.initials: JP
authors.lastname: Brooks
authors.forename: William P
authors.initials: WP
authors.lastname: Burns
authors.forename: Stephen S
authors.initials: SS
authors.lastname: Fong
authors.forename: Chris M
authors.initials: CM
authors.lastname: Gowen
authors.forename: Seth B
authors.initials: SB
authors.lastname: Roberts