20Plasmids harboring antibiotic resistance genes differ in their kinetic values as plasmid 21 conjugation rate, segregation rate by incompatibility with related plasmids, rate of 22 stochastic loss during replication, cost reducing the host-cell fitness, and frequency of 23 compensatory mutations to reduce plasmid cost, depending on the cell mutation 24 frequency. How variation in these values influence the success of a plasmid and their 25 resistance genes in complex ecosystems, as the microbiota? Genes are located in 26 plasmids, plasmids in cells, cells in populations. These populations are embedded in 27 ensembles of species in different human hosts, are able to exchange between them 28 bacterial ensembles during cross-infection and are located in the hospital or the 29 community setting, under various levels of antibiotic exposure. Simulations using new 30 membrane computing methods help predict the influence of plasmid kinetic values on 31 such multilevel complex system. In our simulation, conjugation frequency needed to be 32 at least 10 -3 to clearly influence the dominance of a strain with a resistant plasmid. Host 33 strains able to stably maintain two copies of similar plasmids harboring different 34 resistances, coexistence of these resistances can occur in the population. Plasmid loss 35 rates of 10 -4 or 10 -5 or plasmid fitness costs ≥0.06 favor the plasmids located in the most 36 abundant species. The beneficial effect of compensatory mutations for plasmid fitness 37 cost is proportional to this cost, only at high mutation frequencies (10 -3 -10 -5 ). 38Membrane computing helps set a multilevel landscape to study the effect of changes in 39 plasmid kinetic values on the success of resistant organisms in complex ecosystems. 40 41 42 43 44 Plasmid kinetics are widely assumed to necessarily influence the spread of antibiotic 45 resistance genes in bacterial populations and ecosystems (1-10). The main parameters 46 that affect plasmid kinetics are: a) the rate of plasmid conjugation/transfer (the rate at 47 which a bacterial cell harboring a conjugative plasmid [donor] transfers this plasmid to 48 a recipient cell; b) the segregation rate due to plasmid incompatibility (considering the 49 number of plasmid genome copies that are stably maintained in a bacterial cell); c) the 50 rate of plasmid cost (the reduction imposed by the presence [and transfer] of a plasmid 51 in the growth rate of the host bacterial cell); d) the rate of plasmid cost compensation 52 (measuring the effect of mutations reducing plasmid cost); e) the frequency of 53 mutational events in the plasmid or bacterial genome; and f) the rate of plasmid loss (the 54 rate at which plasmids are lost during the bacterial replication process). 55 However, the effects of these changes on the kinetics of plasmid resistance genes among 56 bacterial populations are necessarily influenced by numerous other factors acting in 57 actual biological ecosystems, such as the intestinal microbiota. Of these factors, our 58 previously published studies...

Membrane Computing is a bio-inspired computing paradigm, whose devices are the socalled membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested "membranesurrounded entities" able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, crosstransmission 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 evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.