Comparison of Optimal Thermodynamic Models of the Tricarboxylic Acid Cycle from Heterotrophs, Cyanobacteria, and Green Sulfur Bacteria

We
have applied a new stochastic simulation approach to predict
the metabolite levels, material flux, and thermodynamic profiles of
the oxidative TCA cycles found in E. coli and Synechococcus sp. PCC 7002, and
in the reductive TCA cycle typical of chemolithoautotrophs and phototrophic
green sulfur bacteria such as Chlorobaculum tepidum. The simulation approach is based on modeling states using statistical
thermodynamics and employs an assumption similar to that used in transition
state theory. The ability to evaluate the thermodynamics of metabolic
pathways allows one to understand the relationship between coupling
of energy and material gradients in the environment and the self-organization
of stable biological systems, and it is shown that each cycle operates
in the direction expected due to its environmental niche. The simulations
predict changes in metabolite levels and flux in response to changes
in cofactor concentrations that would be hard to predict without an
elaborate model based on the law of mass action. In fact, we show
that a thermodynamically unfavorable reaction can still have flux
in the forward direction when it is part of a reaction network. The
ability to predict metabolite levels, energy flow, and material flux
should be significant for understanding the dynamics of natural systems
and for understanding principles for engineering organisms for production
of specialty chemicals.