A shared and limiting pool of ATP. If a mutation increases MICA by directing more energy to pump A, the pleiotropic consequences are that MICB is decreased because pump B is slowed. Thus, the shared and limiting pool of ATP explains the tradeoff. Linear and non-linear properties in the tradeoff lead to the different values of v. We imagine that both pumps and their stated properties operate in both the combined and separate cocktail equations. The difference between the equations emerges in the action of the drugs that are not pumped out of the bacterial cell. If drugs A and B are sufficiently similar such that they attack the same cellular or metabolic process, the single equation in combined is justified. This formulation is therefore an approximation of antagonistic or suppressive combinations. On the other hand, if drugs A and B are sufficiently different such that they attack distinct pathways, they can inflict double damage and two equations are required as in the separate equation. This formation serves as an approximation for additive drug combinations. Because there is evidence that synergistic drug GDC-0199 Bcl-2 inhibitor combinations increase the strength and frequency of multi-resistant mutants, our model is limited to the separate and combined cocktail strategies. Although we invoke efflux pumps, ATP pools, and targeted metabolic pathways, we accept that other scenarios are possible. However, we hope that our interpretation offers a first step in developing a conceptual framework for our model. A conceptual guidance could help the search for drug pairs to be used in experimental cocktails. Although much is known about the metabolic and genetic basis of drug resistance, our understanding of how tradeoffs and pleiotropy may constraint multiple-drug resistance remains limited. Are additive drug pairs, exemplified by the separate equation, more likely to be realized than antagonistic or suppressive pairs expressed by the combined equation? It is our hope that our model will stimulate the needed conceptual and experimental explorations to answer such questions. A final and critical question is whether the tradeoffs we require are possible and general. They have clearly been identified in clinically relevant pathogens. Evolutionary and ecological tradeoffs are often identified as genetic co-variances and have been well documented in a wide range of organisms. If tradeoffs are general, the results of our model could be applied to control more than just bacteria in clinical settings. Plants and pest insects or pathogenic fungi offer another system in which trials could be readily implemented. Of course, it remains to be determined if the constraints of tradeoffs can be broken by longterm evolution. However, unless evolutionary and ecological biology has oversold the importance of tradeoffs, it should be possible to use tradeoffs to our advantage in combating the evolution of antibiotic resistance, at least in the short term. Swine influenza virus is a major cause of acute respiratory infections of pig populations worldwide. The causative agents are type A influenza viruses, mainly of the H1N1, H3N2, or H1N2 subtypes. The main route of transmission is through direct contact between infected and uninfected animals, close contacts being particularly common during animal transport.