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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; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be 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 multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths 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 multilevel analysis of complex microbial landscapes. ; IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples—just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power. ; This work was supported by the European Commission, Seven Framework Program (EVOTAR; FP7-HEALTH-282004) to F. Baquero, T. Coque, V. Fernandez-Lanza, and M. Campos; the Instituto de Salud Carlos III of Spain (Plan Estatal de IDi 2013-2016, grant PI15-00818 and FIS18-1942; CIBERESP, grant CB06/02/0053, and the EU Joint Programming Initiative JPIAMR2016-AC16/00036 to F. Baquero; the Regional Government of Madrid (InGEMICS-C; S2017/BMD-3691) to T. Coque and F. Baquero; and SAF2015-65878-R (MINECO, Spain) and PrometeoII/2014/065 (Generalitat Valenciana, Spain) to A. Moya (all cofinanced by the European Development Regional Fund [ERDF] "A Way to Achieve Europe"). ; Peer reviewed

In this article, we introduce ARES (Antibiotic Resistance Evolution Simulator) a software device that simulates P-system model scenarios with five types of nested computing membranes oriented to emulate a hierarchy of eco-biological compartments, i.e. a) peripheral ecosystem; b) local environment; c) reservoir of supplies; d) animal host; and e) host's associated bacterial organisms (microbiome). Computational objects emulating molecular entities such as plasmids, antibiotic resistance genes, antimicrobials, and/or other substances can be introduced into this framework and may interact and evolve together with the membranes, according to a set of pre-established rules and specifications. ARES has been implemented as an online server and offers additional tools for storage and model editing and downstream analysis ; This work has also been supported by grants BFU2012-39816-C02-01 (co-financed by FEDER funds and the Ministry of Economy and Competitiveness, Spain) to AL and Prometeo/2009/092 (Ministry of Education, Government of Valencia, Spain) and Explora Ciencia y Explora Tecnologia/SAF2013-49788-EXP (Spanish Ministry of Economy and Competitiveness) to AM. IRF is recipient of a "Sara Borrell" postdoctoral fellowship (Ref. CD12/00492) from the Ministry of Economy and Competitiveness (Spain). We are also grateful to the Spanish Network for the Study of Plasmids and Extrachromosomal Elements (REDEEX) for encouraging and funding cooperation among Spanish microbiologists working on the biology of mobile genetic elements (Spanish Ministry of Science and Innovation, reference number BFU2011-14145-E). ; Campos Frances, M.; Llorens, C.; Sempere Luna, JM.; Futami, R.; Rodríguez, I.; Carrasco, P.; Capilla, R. (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct. 10(41):1-13. https://doi.org/10.1186/s13062-015-0070-9 ; S ; 1 ; 13 ; 10 ; 41 ; Baquero F, Coque TM, Canton R. Counteracting antibiotic resistance: breaking ...