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A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES).

Campos M, Llorens C, Sempere JM, Futami R, Rodriguez I, Carrasco P, Capilla R, Latorre A, Coque TM, Moya A, Baquero F - Biol. Direct (2015)

Bottom Line: Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes.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).ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Carretera de Colmenar Viejo, km. 9,100, 28034, Madrid, Spain. mcampos@dsic.upv.es.

ABSTRACT

Background: Antibiotic resistance is a major biomedical problem upon which public health systems demand solutions to construe the dynamics and epidemiological risk of resistant bacteria in anthropogenically-altered environments. The implementation of computable models with reciprocity within and between levels of biological organization (i.e. essential nesting) is central for studying antibiotic resistances. Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes. Such a complex background motivated us to explore the P-system standards of membrane computing an innovative natural computing formalism that abstracts the notion of movement across membranes to simulate antibiotic resistance evolution processes across nested levels of micro- and macro-environmental organization in a given ecosystem.

Results: 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.

Conclusions: The stochastic nature of the P-system model implemented in ARES explicitly links within and between host dynamics into a simulation, with feedback reciprocity among the different units of selection influenced by antibiotic exposure at various ecological levels. ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached. ARES can be accessed at http://gydb.org/ares.

No MeSH data available.


Related in: MedlinePlus

ARES interface and server organization. a Screenshot of the ARES interface. The interface implements a menu that gives access to the distinct server forms that apply for configuration, storage and simulation of P-system model scenarios. At the bottom of the interface the user can access other support sections for managing ARES of for statistical interrogation of the output generated by the simulator device. b ARES sever scheme and workflow for creation, edition and simulation of P-system model scenarios
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Fig2: ARES interface and server organization. a Screenshot of the ARES interface. The interface implements a menu that gives access to the distinct server forms that apply for configuration, storage and simulation of P-system model scenarios. At the bottom of the interface the user can access other support sections for managing ARES of for statistical interrogation of the output generated by the simulator device. b ARES sever scheme and workflow for creation, edition and simulation of P-system model scenarios

Mentions: Users can simulate dynamics of AR evolution using ARES to design a P-system model scenario adapted to the case study specified by the user and then run simulation of this scenario as many times as necessary correcting parameters until a realistic description of the AR process is approximated or validated if real world observations are available. Use of ARES is free, but its accession is password protected in order to allow the users to open and maintain a user account that will needed to store and run model projects in private session (a simulation may last hours or even days depending on the complexity of a P-system scenario). ARES is managed via an easy-to-use interface that implements a centralized menu (Fig. 2a) for accessing the system of forms the user need to sequentially complete in order to introduce the starting configuration of a P-system scenario, run a simulation, and access the results. All menu-forms accessible with this menu can be navigated back and forth for editing the P-system configuration, change or add EBs, objects and rules where or when necessary. A scheme of the whole ARES infrastructure and the workflow for configuration and simulation of P-system scenarios is depicted on Fig. 2b. The usual procedure can be synthetized in the following steps. The form designated as “ECO” must be first accessed (via menu) and completed to create the P basal skin EB; then “ENVIRONMENTS” has to be accessed to configure as many P-like (environmental) EBs as needed within ECO; next, “RESERVOIRS” and “HOSTS” must be filled to configure RS-like (reservoirs) and H-like (hosts) EBs within the previously created P EBs; after this, “MICROBIOMES” must be used to configure a series of B-like (bacterial) EBs that can be either placed within the previously created P-, RS- and H-like EBs; then “OBJECTS” must be used to create as many as PL- (plasmids), AR- (AR genes), A-like (antibiotics and/or other substances), and G-like (clocks) objects as required within the previously created EBs (except for ECO, since it is the skin membrane); finally, the forms “SPECIFICATIONS” and “RULES” must be completed to state the rules assigned to each EB by selecting them from a list of pre-designed rules provided in an understandable and generalized way allowing the user to choose and tune rules with the values and the parameters needed to approximate the frequencies, behaviors, conditions and priorities that govern the dynamic of interactions among the different membranes and objects of the P-system model to be simulated.Fig. 2


A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES).

Campos M, Llorens C, Sempere JM, Futami R, Rodriguez I, Carrasco P, Capilla R, Latorre A, Coque TM, Moya A, Baquero F - Biol. Direct (2015)

ARES interface and server organization. a Screenshot of the ARES interface. The interface implements a menu that gives access to the distinct server forms that apply for configuration, storage and simulation of P-system model scenarios. At the bottom of the interface the user can access other support sections for managing ARES of for statistical interrogation of the output generated by the simulator device. b ARES sever scheme and workflow for creation, edition and simulation of P-system model scenarios
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4526193&req=5

Fig2: ARES interface and server organization. a Screenshot of the ARES interface. The interface implements a menu that gives access to the distinct server forms that apply for configuration, storage and simulation of P-system model scenarios. At the bottom of the interface the user can access other support sections for managing ARES of for statistical interrogation of the output generated by the simulator device. b ARES sever scheme and workflow for creation, edition and simulation of P-system model scenarios
Mentions: Users can simulate dynamics of AR evolution using ARES to design a P-system model scenario adapted to the case study specified by the user and then run simulation of this scenario as many times as necessary correcting parameters until a realistic description of the AR process is approximated or validated if real world observations are available. Use of ARES is free, but its accession is password protected in order to allow the users to open and maintain a user account that will needed to store and run model projects in private session (a simulation may last hours or even days depending on the complexity of a P-system scenario). ARES is managed via an easy-to-use interface that implements a centralized menu (Fig. 2a) for accessing the system of forms the user need to sequentially complete in order to introduce the starting configuration of a P-system scenario, run a simulation, and access the results. All menu-forms accessible with this menu can be navigated back and forth for editing the P-system configuration, change or add EBs, objects and rules where or when necessary. A scheme of the whole ARES infrastructure and the workflow for configuration and simulation of P-system scenarios is depicted on Fig. 2b. The usual procedure can be synthetized in the following steps. The form designated as “ECO” must be first accessed (via menu) and completed to create the P basal skin EB; then “ENVIRONMENTS” has to be accessed to configure as many P-like (environmental) EBs as needed within ECO; next, “RESERVOIRS” and “HOSTS” must be filled to configure RS-like (reservoirs) and H-like (hosts) EBs within the previously created P EBs; after this, “MICROBIOMES” must be used to configure a series of B-like (bacterial) EBs that can be either placed within the previously created P-, RS- and H-like EBs; then “OBJECTS” must be used to create as many as PL- (plasmids), AR- (AR genes), A-like (antibiotics and/or other substances), and G-like (clocks) objects as required within the previously created EBs (except for ECO, since it is the skin membrane); finally, the forms “SPECIFICATIONS” and “RULES” must be completed to state the rules assigned to each EB by selecting them from a list of pre-designed rules provided in an understandable and generalized way allowing the user to choose and tune rules with the values and the parameters needed to approximate the frequencies, behaviors, conditions and priorities that govern the dynamic of interactions among the different membranes and objects of the P-system model to be simulated.Fig. 2

Bottom Line: Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes.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).ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached.

View Article: PubMed Central - PubMed

Affiliation: Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Carretera de Colmenar Viejo, km. 9,100, 28034, Madrid, Spain. mcampos@dsic.upv.es.

ABSTRACT

Background: Antibiotic resistance is a major biomedical problem upon which public health systems demand solutions to construe the dynamics and epidemiological risk of resistant bacteria in anthropogenically-altered environments. The implementation of computable models with reciprocity within and between levels of biological organization (i.e. essential nesting) is central for studying antibiotic resistances. Antibiotic resistance is not just the result of antibiotic-driven selection but more properly the consequence of a complex hierarchy of processes shaping the ecology and evolution of the distinct subcellular, cellular and supra-cellular vehicles involved in the dissemination of resistance genes. Such a complex background motivated us to explore the P-system standards of membrane computing an innovative natural computing formalism that abstracts the notion of movement across membranes to simulate antibiotic resistance evolution processes across nested levels of micro- and macro-environmental organization in a given ecosystem.

Results: 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.

Conclusions: The stochastic nature of the P-system model implemented in ARES explicitly links within and between host dynamics into a simulation, with feedback reciprocity among the different units of selection influenced by antibiotic exposure at various ecological levels. ARES offers the possibility of modeling predictive multilevel scenarios of antibiotic resistance evolution that can be interrogated, edited and re-simulated if necessary, with different parameters, until a correct model description of the process in the real world is convincingly approached. ARES can be accessed at http://gydb.org/ares.

No MeSH data available.


Related in: MedlinePlus