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Sempere, J.-M.

Publications and source records attributed to Sempere, J.-M..

2 recordsLinked to original sources

Simulating the Influence of Conjugative Plasmids Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

Plasmids harboring antibiotic resistance genes differ in their kinetic values as plasmid conjugation rate, segregation rate by incompatibility with related plasmids, rate of stochastic loss during replication, cost reducing the host-cell fitness, and frequency of compensatory mutations to reduce plasmid cost, depending on the cell mutation frequency. How variation in these values influence the success of a plasmid and their resistance genes in complex ecosystems, as the microbiota? Genes are located in plasmids, plasmids in cells, cells in populations. These populations are embedded in ensembles of species in different human hosts, are able to exchange between them bacterial ensembles during cross-infection and are located in the hospital or the community setting, under various levels of antibiotic exposure. Simulations using new membrane computing methods help predict the influence of plasmid kinetic values on such multilevel complex system. In our simulation, conjugation frequency needed to be at least 10-3 to clearly influence the dominance of a strain with a resistant plasmid. Host strains able to stably maintain two copies of similar plasmids harboring different resistances, coexistence of these resistances can occur in the population. Plasmid loss rates of 10-4 or 10-5 or plasmid fitness costs [≥]0.06 favor the plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost, only at high mutation frequencies (10-3-10-5). Membrane computing helps set a multilevel landscape to study the effect of changes in plasmid kinetic values on the success of resistant organisms in complex ecosystems.

microbiology↗

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

Membrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested \"membrane-surrounded 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, 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 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.

microbiology↗