A Novel FBA Strategy for Metabolic Modeling of Cyanobacteria

Metabolic modeling refers to modeling the gamut of metabolic reactions occurring in an organism. Flux Balance Analysis (FBA) is a constraint based metabolic modeling strategy. The main premise in FBA is that an organism is designed by nature to maximize its’ chances of survival. In quantitative terms this translates to the hypothesis that an organism tries to maximize its growth rate through the metabolic reactions occurring within the organism. Thus in FBA, the biomass flux (the flux of the reaction describing the formation of biomass from precursor metabolites) is the objective function that needs to be maximized. The constraints in the model arise from mass balances on the metabolites. While internal metabolites are assumed to be at steady state, external metabolites (nutrients or metabolites excreted by an organism) can be at unsteady state. The steady state assumption stems from the hypothesis of an organism trying to maximize its’ growth rate. In such a scenario, all absorbed nutrients must finally end up as biomass and hence the initial and final amounts of internal metabolites must remain the same. Thus considering the overall behavior, internal metabolites must remain at steady state. In the FBA model, all fluxes are assumed to be unbounded. This assumption would hold true when enough amounts of enzymes are present in an organism at all times to catalyze any amount of substrate. Since enzyme levels in an organism vary in response to extracellular stimuli, leaving the flux of a reaction unconstrained would not be a proper assumption. In this work, a FBA model is proposed which accounts for the fact that enzyme levels within cells vary with perturbations in the environment to which the cells are exposed to. In the proposed model the fluxes of reactions are not left unconstrained. Instead, the flux of a reaction is constrained depending on the amount of the corresponding enzyme. Enzyme levels are assumed to be directly proportional to the corresponding mRNA levels and information about the latter is obtained from microarray experimental data. Technique presented by Sengupta et al. (2010) is used to interpret the microarray data. This technique is adapted from a normalization strategy proposed by Chen et al. (1997).

           Cyanobacteria are photosynthetic prokaryotes. It is believed that they are one the earliest forms of bacteria to have evolved on earth. It is also believed that they were responsible for introducing oxygen into the earth’s atmosphere. Further, cyanobacteria produce useful chemicals such as hydrogen (a potential alternate fuel) by virtue of their metabolism. Cyanobacteria thus form a very important species of bacteria to be studied both from a fundamental as well as a commercial viewpoint.  In this work, we modeled the metabolism of Synechocystis species strain PCC 6803 using the proposed FBA modeling strategy. The metabolic model was borrowed from Shastri and Morgan (2005). Microarray experimental data of the organism was obtained from the KEGG database which contains gene expression data of the organism under a variety of environmental conditions. Several metabolic topologies were obtained which were different from the topology obtained using the usual FBA modeling strategy. For example, one topology was obtained in which the pentose phosphate pathway became active but was not active in the metabolic map obtained from the usual FBA. The proposed FBA strategy thus gives additional insights into the metabolism of an organism that cannot be realized from a usual FBA.

                                                                  References

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