A View of P Systems from Information Theory

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-54072-6_22In this work we propose new view of P systems by using the framework of Information Theory. Given a cell-like P system with communication and evolution rules, we analyze the amount of information that it holds as the result of symbol movements across the membranes. Under this approach, we propose new definitions and results related to the information of P systems and their entropy. In addition, we propose a new working manner for P systems based only in the entropy evolution during the computation time.Work partially supported by the Spanish Ministry of Economy and Competitiveness under EXPLORA Research Project SAF2013-49788-EXP.Sempere Luna, JM. (2017). A View of P Systems from Information Theory. En International Conference on Membrane Computing. Springer Verlag (Germany). 352-362. https://doi.org/10.1007/978-3-319-54072-6 22S35236

On Compensation Loops in Genomic Duplications

Electronic version of an article published as International Journal of Foundations of Computer Science 2020 31:01, 133-142, DOI: 10.1142/S0129054120400092 © World Scientific Publishing Company https://www.worldscientific.com/worldscinet/ijfcs[EN] In this paper, we investigate the compensation loops, a DNA rearrangement in chromosomes due to unequal crossing over. We study the e fect of compensation loops over the gene duplication, and we formalize it as a restricted case of gene duplication in general. We study this biological process under the point of view of formal languages, and we provide some results about the languages de fined in this way.Sempere Luna, JM. (2020). On Compensation Loops in Genomic Duplications. International Journal of Foundations of Computer Science. 31(1):133-142. https://doi.org/10.1142/S0129054120400092S133142311Bovet, D. P., & Varricchio, S. (1992). On the regularity of languages on a binary alphabet generated by copying systems. Information Processing Letters, 44(3), 119-123. doi:10.1016/0020-0190(92)90050-6Dassow, J., Mitrana, V., & Salomaa, A. (1997). Context-free evolutionary grammars and the structural language of nucleic acids. Biosystems, 43(3), 169-177. doi:10.1016/s0303-2647(97)00036-1Ehrenfeucht, A., & Rozenberg, G. (1984). On regularity of languages generated by copying systems. Discrete Applied Mathematics, 8(3), 313-317. doi:10.1016/0166-218x(84)90129-xLeupold, P., Martín-Vide, C., & Mitrana, V. (2005). Uniformly bounded duplication languages. Discrete Applied Mathematics, 146(3), 301-310. doi:10.1016/j.dam.2004.10.003Leupold, P., & Mitrana, V. (2007). Uniformly bounded duplication codes. RAIRO - Theoretical Informatics and Applications, 41(4), 411-424. doi:10.1051/ita:2007021Leupold, P., Mitrana, V., & Sempere, J. M. (2003). Formal Languages Arising from Gene Repeated Duplication. Lecture Notes in Computer Science, 297-308. doi:10.1007/978-3-540-24635-0_22Rozenberg, G., & Salomaa, A. (Eds.). (1997). Handbook of Formal Languages. doi:10.1007/978-3-642-59126-

Modeling of Decision Trees Through P systems

[EN] In this paper, we propose a decision-tree modeling in the framework of membrane computing. We propose an algorithm to obtain a P system that is equivalent to any decision tree taken as input. In our case, and unlike previous proposals, we formulate the concepts of decision trees endogenously, since there is no external agent involved in the modeling. The tree structure can be defined naturally by the topology of the regions in the P system and the decision rules are defined by communication rules of the P system.Sempere Luna, JM. (2019). Modeling of Decision Trees Through P systems. New Generation Computing. 37(3):325-337. https://doi.org/10.1007/s00354-019-00052-4325337373Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Chapman & Hall, Boca Raton (1984)Cardona, M., Colomer, M.A., Margalida, A., Palau, A., Pérez-Hurtado, I., Pérez-Jiménez, M.J., Sanuy, D.: A computational modeling for real ecosystems based on P systems. Nat. Comput. 10(1), 39–53 (2011)Cecilia, J.M., García, J.M., Guerrero, G.D., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J.: Simulation of P systems with active membranes on CUDA. Brief. Bioinform. 11(3), 313–322 (2010)Díaz-Pernil, D., Peña-Cantillana, F., Gutiérrez-Naranjo, M.A.: Self-constructing Recognizer P Systems. In: Proceedings of the Thirteenth Brainstorming Week on Membrane Computing. Fénix Editora, pp. 137–154 (2014)Fayyad, U.M., Irani, K.B.: On the handling of continuous-valued attributes in decision tree generation. Mach. Learn. 8, 87–102 (1992)Kingsford, C., Salzberg, S.L.: What are decision trees ? Nat. Biotechnol. 26(9), 1011–1013 (2008)Martín-Vide, C., Păun, Gh, Pazos, J., Rodríguez-Patón, A.: Tissue P systems. Theor. Comput. Sci. 296, 295–326 (2003)Martínez-del-Amor, M.A., García-Quismondo, M., Macías-Ramos, L.F., Valencia-Cabrera, L., Riscos-Núñez, A., Pérez-Jiménez, M.J.: Simulating P systems on GPU devices: a survey. Fund. Inf. 136(3), 269–284 (2015)Mitchell, T.: Machine Learning. McGraw-Hill, New York City (1997)Păun, Gh: Membrane Computing, An Introduction. Springer, Berlin (2002)Păun, Gh, Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Burlington (1993)Sempere, J.M.: A View of P systems from information theory. In: Proceedings of the 17th international conference on membrane computing (CMC 2016) LNCS vol. 10105. Springer, pp. 352–362 (2017)Sammut, C., Webb, G.I. (eds.): Encyclopedia of Machine Learning. Springer, Berlin (2011)Wang, J., Hu, J., Peng, H., Pérez-Jiménez, M.J., Riscos-Núñez, A.: Decision tree models induced by membrane systems. Rom. J. Inf. Sci. Technol. 18(3), 228–239 (2015)Zhang, C., Ma, Y. (eds.): Ensemble Machine Learning, Methods and Applications. Springer, Berlin (2012)Zhang, X., Wang, B., Ding, Z., Tang, J., He, J.: Implementation of membrane algorithms on GPU. J. Appl. Math. 2014, 7 (2014

On the Languages Accepted by Watson-Crick Finite Automata with Delays

[EN] In this work, we analyze the computational power of Watson-Crick finite automata (WKFA) if some restrictions over the transition function in the model are imposed. We consider that the restrictions imposed refer to the maximum length difference between the two input strands which is called the delay. We prove that the language class accepted by WKFA with such restrictions is a proper subclass of the languages accepted by arbitrary WKFA in general. In addition, we initiate the study of the language classes characterized by WKFAs with bounded delays. We prove some of the results by means of various relationships between WKFA and sticker systems.This work has been developed with the financial support of the European Union's Horizon 2020 research and innovation programme under grant agreement No. 952215 corresponding to the TAILOR project.Sempere Luna, JM. (2021). On the Languages Accepted by Watson-Crick Finite Automata with Delays. Mathematics. 9(8):1-12. https://doi.org/10.3390/math9080813S1129

Preface: 11th Workshop on Non-classical Models of Automata and Applications (NCMA 2019)

Holzer, M.; Sempere Luna, JM. (2021). Preface: 11th Workshop on Non-classical Models of Automata and Applications (NCMA 2019). RAIRO - Theoretical Informatics and Applications. 55:1-2. https://doi.org/10.1051/ita/2021009S125

Generating networks of genetic processors

[EN] The Networks of Genetic Processors (NGPs) are non-conventional models of computation based on genetic operations over strings, namely mutation and crossover operations as it was established in genetic algorithms. Initially, they have been proposed as acceptor machines which are decision problem solvers. In that case, it has been shown that they are universal computing models equivalent to Turing machines. In this work, we propose NGPs as enumeration devices and we analyze their computational power. First, we define the model and we propose its definition as parallel genetic algorithms. Once the correspondence between the two formalisms has been established, we carry out a study of the generation capacity of the NGPs under the research framework of the theory of formal languages. We investigate the relationships between the number of processors of the model and its generative power. Our results show that the number of processors is important to increase the generative capability of the model up to an upper bound, and that NGPs are universal models of computation if they are formulated as generation devices. This allows us to affirm that parallel genetic algorithms working under certain restrictions can be considered equivalent to Turing machines and, therefore, they are universal models of computation.This research was partially supported by TAILOR, a project funded by EU Horizon 2020 research and innovation programme under GA No 952215.Campos Frances, M.; Sempere Luna, JM. (2022). Generating networks of genetic processors. Genetic Programming and Evolvable Machines. 23(1):133-155. https://doi.org/10.1007/s10710-021-09423-713315523

A Model of Antibiotic Resistance Evolution Dynamics Through P Systems with Active Membranes and Communication Rules

Baquero, F.; Campos Frances, M.; Llorens, C.; Sempere Luna, JM. (2018). A Model of Antibiotic Resistance Evolution Dynamics Through P Systems with Active Membranes and Communication Rules. Lecture Notes in Computer Science. 11270:33-44. https://doi.org/10.1007/978-3-030-00265-7_3S334411270Barbacari, N., Profir, A., Zelinschi, C.: Gene regulatory network modeling by means of membrane computing. In: Proceedings of the 7th International Workshop on Membrane Computing WMC 2006. LNCS, vol. 4361, pp. 162–178 (2006)Besozzi, D., Cazzaniga, P., Cocolo, S., Mauri, G., Pescini, D.: Modeling diffusion in a signal transduction pathway: the use of virtual volumes in P systems. Int. J. Found. Comput. Sci. 22(1), 89–96 (2011)Campos, M.: A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biol. Direct 10(1), 41 (2015)Ciobanu, G., Păun, Gh., Pérez-Jiménez, M.J.: Applications of Membrane Computing. Springer, Heidelberg (2006). https://doi.org/10.1007/3-540-29937-8Colomer, M.A., Margalida, A., Sanuy, D., Pérez-Jiménez, M.J.: A bio-inspired model as a new tool for modeling ecosystems: the avian scavengeras a case study. Ecol. Model. 222(1), 33–47 (2011)Colomer, M.A., Martínez-del-Amor, M.A., Pérez-Hurtado, I., Pérez-Jiménez, M.J., Riscos-Núñez, A.: A uniform framework for modeling based on P systems. In: Li, K., Nagar, A.K., Thamburaj, R. (eds.) IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2010), vol. 1, pp. 616–621 (2010)Dassow, J., Păun, Gh.: On the power of membrane computing. TUCS Technical Report No. 217 (1998)Frisco, P., Gheorghe, M., Pérez-Jiménez, M.J. (eds.): Applications of Membrane Computing in Systems and Synthetic Biology. ECC, vol. 7. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-03191-0Păun, Gh.: Computing with membranes. J. Comput. Syst. Sci. 61(1), 108–143 (2000)Păun, Gh.: Membrane Computing: An Introduction. Springer, Heidelberg (2002). https://doi.org/10.1007/978-3-642-56196-2Păun, Gh., Rozenberg, G., Salomaa, A. (eds.): The Oxford Handbook of Membrane Computing. Oxford University Press, Oxford (2010)World Health Organization: Antimicrobial Resistance: Global Report on Surveillance (2014

Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model

This is a pre-copyedited, author-produced version of an article accepted for publication in [insert journal title] following peer review. The version of record [insert complete citation information here] is available online at: xxxxxxx [insert URL and DOI of the article on the OUP website].[EN] Membrane computing is a natural computing procedure inspired in the compartmental structure of living cells. This approach allows mimicking the complex structure of biological processes, and, when applied to transmissible diseases, can simulate a virtual `epidemic¿ based on interactions between elements within the computational model according to established conditions. General and focused vaccination strategies for controlling SARS-Cov-2 epidemics have been simulated for 2.3 years fromthe emergence of the epidemic in a hypothetical town of 10320 inhabitants in a country with mean European demographics where COVID-19 is imported. The age and immunological-response groups of the hosts and their lifestyles were minutely examined. The duration of natural, acquired immunity influenced the results; the shorter the duration, the more endemic the process, resulting in higher mortality, particularly among elderly individuals. During epidemic valleys between waves, the proportion of infected patients belonging to symptomatic groups (mostly elderly) increased in the total population, a population that largely benefits from standard double vaccination, particularly with boosters. There was no clear difference when comparing booster shots provided at 4 or 6 months after standard doubledose vaccination. Vaccines even of moderate efficacy (short-term protection) were effective in decreasing the number of symptomatic cases. Generalized vaccination of the entire population (all ages) added little benefit to overall mortality rates, and this situation also applied for generalized lockdowns. Elderly-only vaccination and lockdowns, even without general interventions directed to reduce population transmission, is sufficient for dramatically reducing mortality.This work was partially fund by the Fundación del Conocimiento Madri+d from the Madrid' Autonomous Community through a research contract (AVATAR-EPAMEC) within the Health Start Plus Program, promoted by the Carlos III Health Research Institute (ITEMAS), Ministry of Science, Innovation and Universities of Spain.Campos Frances, M.; Sempere Luna, JM.; Galán, JC.; Moya, A.; Cantón, R.; Llorens, C.; Baquero, F. (2022). Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model. microLife. 3:1-13. https://doi.org/10.1093/femsml/uqac018113

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

[EN] Bacterial plasmids harboring antibiotic resistance genes are critical in the spread of antibiotic resistance. It is known that plasmids differ in their kinetic values, i.e., conjugation rate, segregation rate by copy number incompatibility with related plasmids, and rate of stochastic loss during replication. They also differ in cost to the cell in terms of reducing fitness and in the frequency of compensatory mutations compensating plasmid cost. However, we do not know how variation in these values influences the success of a plasmid and its resistance genes in complex ecosystems, such as the microbiota. Genes are in plasmids, plasmids are in cells, and cells are in bacterial populations and microbiotas, which are inside hosts, and hosts are in human communities at the hospital or the community under various levels of cross-colonization and antibiotic exposure. Differences in plasmid kinetics might have consequences on the global spread of antibiotic resistance. New membrane computing methods help to predict these consequences. In our simulation, conjugation frequency of at least 10(-3) influences the dominance of a strain with a resistance plasmid. Coexistence of different antibiotic resistances occurs if host strains can maintain two copies of similar plasmids. Plasmid loss rates of 10(-4) or 10(-5) or plasmid fitness costs of >= 0.06 favor plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost at high mutation frequencies (10(-3) to 10(-5)). The results of this computational model clearly show how changes in plasmid kinetics can modify the entire population ecology of antibiotic resistance in the hospital setting.F. Baquero, M. Campos, and T. M. Coque were supported by EU Joint Programming Initiative JPIAMR2016-AC16/00043 (JPIonAMR-Third call on Transmission, ST131TS project), the Health Institute Carlos III of Spain (grants PI15-00818 and PI18-01942 and CIBER [CIBER in Epidemiology and Public Health, CIBERESP; CB06/02/0053]), and the Regional Government of Madrid (InGEMICS-C; S2017/BMD-3691), all of them cofinanced by the European Development Regional Fund (ERDF) "A Way to Achieve Europe." A. San Millan was supported by the European Research Council under the European Union's Horizon 2020 Research and Innovation Program (ERC grant agreement number 757440-PLASREVOLUTION)Campos Frances, M.; San Millan, A.; Sempere Luna, JM.; Lanza, VF.; Coque, TM.; Llorens, C.; Baquero, F. (2020). Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. Antimicrobial Agents and Chemotherapy. 64(8):1-19. https://doi.org/10.1128/AAC.00593-20S119648De Gelder, L., Ponciano, J. M., Joyce, P., & Top, E. M. (2007). Stability of a promiscuous plasmid in different hosts: no guarantee for a long-term relationship. Microbiology, 153(2), 452-463. doi:10.1099/mic.0.2006/001784-0Norman, A., Hansen, L. H., & Sørensen, S. J. (2009). Conjugative plasmids: vessels of the communal gene pool. Philosophical Transactions of the Royal Society B: Biological Sciences, 364(1527), 2275-2289. doi:10.1098/rstb.2009.0037Andam, C. P., Fournier, G. P., & Gogarten, J. P. (2011). Multilevel populations and the evolution of antibiotic resistance through horizontal gene transfer. FEMS Microbiology Reviews, 35(5), 756-767. doi:10.1111/j.1574-6976.2011.00274.xBaquero, F., Tedim, A. P., & Coque, T. M. (2013). Antibiotic resistance shaping multi-level population biology of bacteria. Frontiers in Microbiology, 4. doi:10.3389/fmicb.2013.00015Wein, T., Hülter, N. F., Mizrahi, I., & Dagan, T. (2019). Emergence of plasmid stability under non-selective conditions maintains antibiotic resistance. Nature Communications, 10(1). doi:10.1038/s41467-019-10600-7Yano, H., Shintani, M., Tomita, M., Suzuki, H., & Oshima, T. (2019). Reconsidering plasmid maintenance factors for computational plasmid design. Computational and Structural Biotechnology Journal, 17, 70-81. doi:10.1016/j.csbj.2018.12.001Gumpert, H., Kubicek-Sutherland, J. Z., Porse, A., Karami, N., Munck, C., Linkevicius, M., … Sommer, M. O. A. (2017). Transfer and Persistence of a Multi-Drug Resistance Plasmid in situ of the Infant Gut Microbiota in the Absence of Antibiotic Treatment. Frontiers in Microbiology, 8. doi:10.3389/fmicb.2017.01852Durão, P., Balbontín, R., & Gordo, I. (2018). Evolutionary Mechanisms Shaping the Maintenance of Antibiotic Resistance. Trends in Microbiology, 26(8), 677-691. doi:10.1016/j.tim.2018.01.005Campos, M., Llorens, C., Sempere, J. M., Futami, R., Rodriguez, I., Carrasco, P., … Baquero, F. (2015). A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biology Direct, 10(1). doi:10.1186/s13062-015-0070-9Campos, M., Capilla, R., Naya, F., Futami, R., Coque, T., Moya, A., … Baquero, F. (2019). Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. mBio, 10(1), e02460-18. doi:10.1128/mbio.02460-1813. Baquero F, Campos M, Llorens C, Sempere JM. 2018. A model of antibiotic resistance evolution dynamics through P systems with active membranes and communication rules, p 33–44. In Graciani C, Agustín Riscos-Núñez A, Păun Gh, Rozenberg G, Salomaa A (ed), Enjoying natural computing. Springer, Cham, Switzerland.Leclerc, Q. J., Lindsay, J. A., & Knight, G. M. (2019). Mathematical modelling to study the horizontal transfer of antimicrobial resistance genes in bacteria: current state of the field and recommendations. Journal of The Royal Society Interface, 16(157), 20190260. doi:10.1098/rsif.2019.0260Blanquart, F. (2019). Evolutionary epidemiology models to predict the dynamics of antibiotic resistance. Evolutionary Applications, 12(3), 365-383. doi:10.1111/eva.1275316. Rozenberg G, Salomaa A, Păun G (ed). 2010. The Oxford handbook of membrane computing. Oxford University Press, Oxford, England.17. Păun G. 2002. Membrane computing. An introduction. Springer-Verlag, Heidelberg, Germany.Novais, A., Cantón, R., Moreira, R., Peixe, L., Baquero, F., & Coque, T. M. (2006). Emergence and Dissemination of Enterobacteriaceae Isolates Producing CTX-M-1-Like Enzymes in Spain Are Associated with IncFII (CTX-M-15) and Broad-Host-Range (CTX-M-1, -3, and -32) Plasmids. Antimicrobial Agents and Chemotherapy, 51(2), 796-799. doi:10.1128/aac.01070-06Mathers, A. J., Peirano, G., & Pitout, J. D. D. (2015). The Role of Epidemic Resistance Plasmids and International High-Risk Clones in the Spread of Multidrug-Resistant Enterobacteriaceae. Clinical Microbiology Reviews, 28(3), 565-591. doi:10.1128/cmr.00116-1420. Poirel L, Madec JY, Lupo A, Schink AK, Kieffer N, Nordmann P, Schwarz S. 2018. Antimicrobial resistance in Escherichia coli, p 289–316. In Schwarz S, Cavaco LM, Shen J (ed), Antimicrobial resistance in bacteria from livestock and companion animals. ASM Press, Washington, DC.Livermore, D. M., & Hawkey, P. M. (2005). CTX-M: changing the face of ESBLs in the UK. Journal of Antimicrobial Chemotherapy, 56(3), 451-454. doi:10.1093/jac/dki23923. European Centre for Disease Prevention and Control. 2015. Antimicrobial resistance surveillance in Europe 2015. Annual report of the European Antimicrobial Resistance Surveillance Network (EARS-Net). European Centre for Disease Prevention and Control, Stockholm, Sweden.Bush, K., & Fisher, J. F. (2011). Epidemiological Expansion, Structural Studies, and Clinical Challenges of New β-Lactamases from Gram-Negative Bacteria. Annual Review of Microbiology, 65(1), 455-478. doi:10.1146/annurev-micro-090110-102911Bush, K. (2018). Past and Present Perspectives on β-Lactamases. Antimicrobial Agents and Chemotherapy, 62(10). doi:10.1128/aac.01076-18Hawser, S. P., Bouchillon, S. K., Hoban, D. J., Badal, R. E., Cantón, R., & Baquero, F. (2010). Incidence and Antimicrobial Susceptibility of Escherichia coli and Klebsiella pneumoniae with Extended-Spectrum β-Lactamases in Community- and Hospital-Associated Intra-Abdominal Infections in Europe: Results of the 2008 Study for Monitoring Antimicrobial Resistance Trends (SMART). Antimicrobial Agents and Chemotherapy, 54(7), 3043-3046. doi:10.1128/aac.00265-10Simonsen, L., Gordon, D. M., Stewart, F. M., & Levin, B. R. (1990). Estimating the rate of plasmid transfer: an end-point method. Journal of General Microbiology, 136(11), 2319-2325. doi:10.1099/00221287-136-11-2319Levin, B. R., Stewart, F. M., & Rice, V. A. (1979). The kinetics of conjugative plasmid transmission: Fit of a simple mass action model. Plasmid, 2(2), 247-260. doi:10.1016/0147-619x(79)90043-xTurner, P. E., Williams, E. S. C. P., Okeke, C., Cooper, V. S., Duffy, S., & Wertz, J. E. (2014). Antibiotic resistance correlates with transmission in plasmid evolution. Evolution, 68(12), 3368-3380. doi:10.1111/evo.12537Porse, A., Schønning, K., Munck, C., & Sommer, M. O. A. (2016). Survival and Evolution of a Large Multidrug Resistance Plasmid in New Clinical Bacterial Hosts. Molecular Biology and Evolution, 33(11), 2860-2873. doi:10.1093/molbev/msw163Smillie, C., Garcillán-Barcia, M. P., Francia, M. V., Rocha, E. P. C., & de la Cruz, F. (2010). Mobility of Plasmids. Microbiology and Molecular Biology Reviews, 74(3), 434-452. doi:10.1128/mmbr.00020-1038. Taylor DE, Gibreel A, Tracz DM, Lawley TD. 2004. Antibiotic resistance plasmids, p 473–492. In Funnell BE, Phillips GJ (ed), Plasmid biology. American Society of Microbiology, Washington, DC.Million-Weaver, S., & Camps, M. (2014). Mechanisms of plasmid segregation: Have multicopy plasmids been overlooked? Plasmid, 75, 27-36. doi:10.1016/j.plasmid.2014.07.002Lau, B. T. C., Malkus, P., & Paulsson, J. (2013). New quantitative methods for measuring plasmid loss rates reveal unexpected stability. Plasmid, 70(3), 353-361. doi:10.1016/j.plasmid.2013.07.007Vogwill, T., & MacLean, R. C. (2014). The genetic basis of the fitness costs of antimicrobial resistance: a meta-analysis approach. Evolutionary Applications, 8(3), 284-295. doi:10.1111/eva.12202Andersson, D. I., & Levin, B. R. (1999). The biological cost of antibiotic resistance. Current Opinion in Microbiology, 2(5), 489-493. doi:10.1016/s1369-5274(99)00005-3Andersson, D. I., & Hughes, D. (2010). Antibiotic resistance and its cost: is it possible to reverse resistance? Nature Reviews Microbiology, 8(4), 260-271. doi:10.1038/nrmicro2319Loftie-Eaton, W., Bashford, K., Quinn, H., Dong, K., Millstein, J., Hunter, S., … Top, E. M. (2017). Compensatory mutations improve general permissiveness to antibiotic resistance plasmids. Nature Ecology & Evolution, 1(9), 1354-1363. doi:10.1038/s41559-017-0243-2Zwanzig, M., Harrison, E., Brockhurst, M. A., Hall, J. P. J., Berendonk, T. U., & Berger, U. (2019). Mobile Compensatory Mutations Promote Plasmid Survival. mSystems, 4(1). doi:10.1128/msystems.00186-18Yang, Q. E., MacLean, C., Papkou, A., Pritchard, M., Powell, L., Thomas, D., … Walsh, T. R. (2020). Compensatory mutations modulate the competitiveness and dynamics of plasmid-mediated colistin resistance in Escherichia coli clones. The ISME Journal, 14(3), 861-865. doi:10.1038/s41396-019-0578-6Gama, J. A., Zilhão, R., & Dionisio, F. (2018). Impact of plasmid interactions with the chromosome and other plasmids on the spread of antibiotic resistance. Plasmid, 99, 82-88. doi:10.1016/j.plasmid.2018.09.009Harrison, E., Dytham, C., Hall, J. P. J., Guymer, D., Spiers, A. J., Paterson, S., & Brockhurst, M. A. (2016). Rapid compensatory evolution promotes the survival of conjugative plasmids. Mobile Genetic Elements, 6(3), e1179074. doi:10.1080/2159256x.2016.1179074Hall, J. P. J., Brockhurst, M. A., Dytham, C., & Harrison, E. (2017). The evolution of plasmid stability: Are infectious transmission and compensatory evolution competing evolutionary trajectories? Plasmid, 91, 90-95. doi:10.1016/j.plasmid.2017.04.00354. Shintani M, Suzuki H. 2019. Plasmids and their hosts, p 109–133. In Nishida H, Oshima T (ed), DNA traffic in the environment. Springer, Singapore.Komp Lindgren, P., Karlsson, A., & Hughes, D. (2003). Mutation Rate and Evolution of Fluoroquinolone Resistance in Escherichia coli Isolates from Patients with Urinary Tract Infections. Antimicrobial Agents and Chemotherapy, 47(10), 3222-3232. doi:10.1128/aac.47.10.3222-3232.2003Krone, S. M., Lu, R., Fox, R., Suzuki, H., & Top, E. M. (2007). Modelling the spatial dynamics of plasmid transfer and persistence. Microbiology, 153(8), 2803-2816. doi:10.1099/mic.0.2006/004531-0Baquero, F., Coque, T. M., & de la Cruz, F. (2011). Ecology and Evolution as Targets: the Need for Novel Eco-Evo Drugs and Strategies To Fight Antibiotic Resistance. Antimicrobial Agents and Chemotherapy, 55(8), 3649-3660. doi:10.1128/aac.00013-11Buckner, M. M. C., Ciusa, M. L., & Piddock, L. J. V. (2018). Strategies to combat antimicrobial resistance: anti-plasmid and plasmid curing. FEMS Microbiology Reviews, 42(6), 781-804. doi:10.1093/femsre/fuy031Bush, K. (2008). Extended-spectrum β-lactamases in North America, 1987–2006. Clinical Microbiology and Infection, 14, 134-143. doi:10.1111/j.1469-0691.2007.01848.xJacoby, G. A., & Han, P. (1996). Detection of extended-spectrum beta-lactamases in clinical isolates of Klebsiella pneumoniae and Escherichia coli. Journal of clinical microbiology, 34(4), 908-911. doi:10.1128/jcm.34.4.908-911.1996Valverde, A., Coque, T. M., Sanchez-Moreno, M. P., Rollan, A., Baquero, F., & Canton, R. (2004). Dramatic Increase in Prevalence of Fecal Carriage of Extended-Spectrum  -Lactamase-Producing Enterobacteriaceae during Nonoutbreak Situations in Spain. Journal of Clinical Microbiology, 42(10), 4769-4775. doi:10.1128/jcm.42.10.4769-4775.2004Hernández, J. R., Martínez-Martínez, L., Cantón, R., Coque, T. M., & Pascual, A. (2005). Nationwide Study of Escherichia coli and Klebsiella pneumoniae Producing Extended-Spectrum β-Lactamases in Spain. Antimicrobial Agents and Chemotherapy, 49(5), 2122-2125. doi:10.1128/aac.49.5.2122-2125.2005PEREZ, F., ENDIMIANI, A., HUJER, K., & BONOMO, R. (2007). The continuing challenge of ESBLs. Current Opinion in Pharmacology, 7(5), 459-469. doi:10.1016/j.coph.2007.08.003Hernández-García, M., Pérez-Viso, B., Navarro-San Francisco, C., Baquero, F., Morosini, M. I., Ruiz-Garbajosa, P., & Cantón, R. (2019). Intestinal co-colonization with different carbapenemase-producing Enterobacterales isolates is not a rare event in an OXA-48 endemic area. EClinicalMedicine, 15, 72-79. doi:10.1016/j.eclinm.2019.09.005Jensen, R. B., & Gerdes, K. (1995). Programmed cell death in bacteria: proteic plasmid stabilization systems. Molecular Microbiology, 17(2), 205-210. doi:10.1111/j.1365-2958.1995.mmi_17020205.xStalder, T., Cornwell, B., Lacroix, J., Kohler, B., Dixon, S., Yano, H., … Top, E. M. (2020). Evolving Populations in Biofilms Contain More Persistent Plasmids. Molecular Biology and Evolution, 37(6), 1563-1576. doi:10.1093/molbev/msaa024McNally, A., Oren, Y., Kelly, D., Pascoe, B., Dunn, S., Sreecharan, T., … Corander, J. (2016). Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations. PLOS Genetics, 12(9), e1006280. doi:10.1371/journal.pgen.1006280Baquero, M.-R., Galán, J. C., del Carmen Turrientes, M., Cantón, R., Coque, T. M., Martínez, J. L., & Baquero, F. (2005). Increased Mutation Frequencies in Escherichia coli Isolates Harboring Extended-Spectrum β-Lactamases. Antimicrobial Agents and Chemotherapy, 49(11), 4754-4756. doi:10.1128/aac.49.11.4754-4756.2005Baquero, F. (2004). From pieces to patterns: evolutionary engineering in bacterial pathogens. Nature Reviews Microbiology, 2(6), 510-518. doi:10.1038/nrmicro909Andersson, D. I., Balaban, N. Q., Baquero, F., Courvalin, P., Glaser, P., Gophna, U., … Tønjum, T. (2020). Antibiotic resistance: turning evolutionary principles into clinical reality. FEMS Microbiology Reviews, 44(2), 171-188. doi:10.1093/femsre/fuaa001Jernberg, C., Löfmark, S., Edlund, C., & Jansson, J. K. (2010). Long-term impacts of antibiotic exposure on the human intestinal microbiota. Microbiology, 156(11), 3216-3223. doi:10.1099/mic.0.040618-0Sommer, F., Anderson, J. M., Bharti, R., Raes, J., & Rosenstiel, P. (2017). The resilience of the intestinal microbiota influences health and disease. Nature Reviews Microbiology, 15(10), 630-638. doi:10.1038/nrmicro.2017.58Novais, C., Tedim, A. P., Lanza, V. F., Freitas, A. R., Silveira, E., Escada, R., … Coque, T. M. (2016). Co-diversification of Enterococcus faecium Core Genomes and PBP5: Evidences of pbp5 Horizontal Transfer. Frontiers in Microbiology, 7. doi:10.3389/fmicb.2016.0158

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

[EN] Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.MC and FB were sponsored by the Projects COV20 00067 of the Program SARS-COV-2 and COVID-19 infection of the Instituto de Salud Carlos III, Ministerio de Ciencia e Innovacion of Spain, CB06/02/0053 of the Centro de Investigacion Biom edica en Red de Epidemiolog¿a y Salud Publica (CIBERESP), and the Regional Government of Madrid (InGeMICS-B2017/BMD-3691). For JCG, this study was partially founded by the Autonomous Community of Madrid, Spain (COVID-19 Grant, 2020) and the Ramon y Cajal Institute for Health Research (IRYCIS), Madrid, Spain. For AM, this study was supported by grants from the Spanish Ministry of Science and Innovation (PID2019-105969GB-I00), the government of Valencia (project Prometeo/2018/A/133) and cofinanced by the European Regional Development Fund (ERDF).Campos Frances, M.; Sempere Luna, JM.; Galán, JC.; Moya, A.; Llorens, C.; De-Los-Angeles, C.; Baquero-Artigao, F.... (2021). Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model. microLife. 2:1-14. https://doi.org/10.1093/femsml/uqab011S114