Consistency of cybernetic variables with gene expression profiles: A more rigorous test

Pseudomonad reverse carbon catabolite repression, interspecies metabolite exchange, and consortial division of labor

Microorganisms acquire energy and nutrients from dynamic environments, where substrates vary in both type and abundance. The regulatory system responsible for prioritizing preferred substrates is known as carbon catabolite repression (CCR). Two broad classes of CCR have been documented in the literature. The best described CCR strategy, referred to here as classic CCR (cCCR), has been experimentally and theoretically studied using model organisms such as Escherichia coli. cCCR phenotypes are often used to generalize universal strategies for fitness, sometimes incorrectly. For instance, extremely competitive microorganisms, such as Pseudomonads, which arguably have broader global distributions than E. coli, have achieved their success using metabolic strategies that are nearly opposite of cCCR. These organisms utilize a CCR strategy termed 'reverse CCR' (rCCR), because the order of preferred substrates is nearly reverse that of cCCR. rCCR phenotypes prefer organic acids over glucose, may or may not select preferred substrates to optimize growth rates, and do not allocate intracellular resources in a manner that produces an overflow metabolism. cCCR and rCCR have traditionally been interpreted from the perspective of monocultures, even though most microorganisms live in consortia. Here, we review the basic tenets of the two CCR strategies and consider these phenotypes from the perspective of resource acquisition in consortia, a scenario that surely influenced the evolution of cCCR and rCCR. For instance, cCCR and rCCR metabolism are near mirror images of each other; when considered from a consortium basis, the complementary properties of the two strategies can mitigate direct competition for energy and nutrients and instead establish cooperative division of labor.

A Cybernetic Approach to Modeling Lipid Metabolism in Mammalian Cells

The goal-oriented control policies of cybernetic models have been used to predict metabolic phenomena such as the behavior of gene knockout strains, complex substrate uptake patterns, and dynamic metabolic flux distributions. Cybernetic theory builds on the principle that metabolic regulation is driven towards attaining goals that correspond to an organism’s survival or displaying a specific phenotype in response to a stimulus. Here, we have modeled the prostaglandin (PG) metabolism in mouse bone marrow derived macrophage (BMDM) cells stimulated by Kdo2-Lipid A (KLA) and adenosine triphosphate (ATP), using cybernetic control variables. Prostaglandins are a well characterized set of inflammatory lipids derived from arachidonic acid. The transcriptomic and lipidomic data for prostaglandin biosynthesis and conversion were obtained from the LIPID MAPS database. The model parameters were estimated using a two-step hybrid optimization approach. A genetic algorithm was used to determine the population of near optimal parameter values, and a generalized constrained non-linear optimization employing a gradient search method was used to further refine the parameters. We validated our model by predicting an independent data set, the prostaglandin response of KLA primed ATP stimulated BMDM cells. We show that the cybernetic model captures the complex regulation of PG metabolism and provides a reliable description of PG formation.