 Beyond these practical benefits, however, are epistemic advantages that allow bacterial models to be compared very readily to mathematical and computational models. Population size has theoretical virtues on top of the practical ones. Evolutionary theory predicts very different outcomes for small versus large populations (Charlesworth 2002), and key theoretical tools assume infinite populations (see the  SEP entry on population genetics).  The very large population sizes of most bacteria mean they are good proxies for these idealized equations. Microbial experimental systems can not only serve as proxies for theoretical models, but also show when something is fundamentally wrong with the mathematical assumptions. In the case discussed above, Gause wanted to model the Lotka-Volterra equations with a microbial analogue experiment. But the mathematical model assumes that both populations can rebound continuously from extremely low numbers, and that stochastic processes do not affect these deterministic outcomes. In many actual populations, the growth curves simply cannot rebound from very low values and the stable oscillations do not occur, just as Gause found. Computational simulations of the Lotka-Volterra equations similarly find that extinction of predators or both predator and prey is inevitable unless additional resource limitations are introduced (Wilensky &amp; Reisman 2006; Weisberg &amp; Reisman 2008). The actual details of which models do better on what dimensions do not matter here. The emphasis is on how modellers can shift back and forth from mathematics to computational simulations to microbial experiments because of basic structural similarities that are not overwhelmed by organismal complications (O’Malley &amp; Parke 2018).