This characterization of cell behavior naturally leads to a math programming modeling paradigm.
A fundamental assumption of FBA is that organisms can function optimally (often as a result of adaptive evolution) in that they make optimal use of scarce resources to serve the needs of the organism. FBA simulations give insight into the relative rates at which reactions occur when the cell is optimized for a specific objective. Introductionįlux balance analysis (FBA) is the use of a linear program (LP) to model the flow of metabolites through the network of reactions in a cell. The proposed method is also more accurate than other approaches in that only biologically plausible reactions and exchange reactions are used. 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 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. When applied to a working model, the algorithm gives a hypothesis concerning a minimal medium for sustaining the cell in culture.
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. 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. Constraint-based metabolic models are currently the most comprehensive system-wide models of cellular metabolism.
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