Multi-Hierarchical Dynamics of Antimicrobial Resistance Simulated in a Nested Membrane Computing Model

The membrane-computing model in this work reproduces complex biological landscapes in the computer world. It uses nested membrane-surrounded entities able to divide, propagate and die, be transfer into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of multi-hierarchical interactive dynamics resulting from the probabilistic interaction along time of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges in the real world. Our model facilitates the simultaneous analysis of several features of interest in the prediction of the rules governing the multi-level evolutionary biology of antibiotic resistance. These include the overall integrated dynamics of species, populations with resistance phenotypes, and mobile genetic elements in different environments (hospital and community) and experimental landscapes. In the examples included here, we predict the effects of different rates of patients flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also predict the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial lineages in the evolution of resistance phenotypes. However, many other analyses are possible to implement. In summary, we provide a bunch of examples about the multi-hierarchical dynamics of antibiotic resistance using a novel computable model with reciprocity within and between levels of biological organization, a type of approach that can be easily expanded to fulfil the needs of risk-analysis and evolutionary predictions in a multiplicity of complex ecological landscapes.