Evolutionary dynamics is the quantitative description of evolution, in terms of population sizes, mutation rates and selective pressure. Antibiotic resistance is an evolutionary process, therefore strategies to prevent or minimise resistance, or to reverse it, require an understanding of evolutionary dynamics. The field of evolutionary dynamics began in 1943 when Luria and Delbrück described the incidence of phage resistance in bacteria in terms of population size and mutation rate. The genetic changes that cause antibiotic resistance are often point mutations, but other changes, such as gene amplification or gene deletion may be involved. Resistance rates may be much higher than mutation rates, because multiple changes in the bacterial genome may result in resistance. To cure a bacterial infection it is not necessary that an antibiotic eliminate every bacterial cell, simply to reduce the bacterial count to a level that can be controlled by the host immune system, so approaches that activate host immunity, such as vaccines, have a role to play in preventing antibiotic resistance. Evolutionary modelling describes the emergence of antibiotic resistance as a process in which antibiotics provide selective pressure. An effective tactic for minimizing or delaying resistance is the use of combination therapy with non-cross-resistant antibiotics, and evolutionary modelling can compare the likely efficacy of different timings, dose ratios and treatment schedules. Resistance breakers – agents that prevent or reverse antibiotic resistance – are another potential approach. When resistance is caused by activation of bacterial efflux pumps, for example, pump inhibitors can act as resistance breakers. In addition to antibiotic resistance caused by spontaneous genetic changes – vertical gene transmission – horizontal gene transmission (HGT) provides mechanisms by which resistance genes can be transferred into previously sensitive bacterial cells independently of cell division, e.g. by a plasmid. The dynamics of HGT show radical differences from vertically transmitted resistance. When a bacterium can exist in both intracellular and extracellular spaces the dynamics of resistance become more complex, partly because evolution is faster in small environments. From the study of evolutionary dynamics, important conclusions can be drawn: early use of molecular diagnostics can minimise resistance. Used in conjunction with drug resistance databases and artificial intelligence (AI) software to optimise combination design, early use of diagnostics has the potential to reduce the incidence of antibiotic resistance by many orders of magnitude.

Evolutionary dynamics has no need of vast abstract spaces, like all the possible viable animals, DNA sequences, sets of proteins or biological laws. Better, as the theoretical biologist Stuart A. Kauffman proposes, to think of evolutionary dynamics as the exploration in time by the biosphere of what can happen next: the “adjacent possible”. Lee Smolin, “Time Reborn” (2014).