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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

P-system model for AR evolution in complex ecosystems. Venn diagram representation showing of the framework of membranes and vocabulary of objects, on which our P-system model is based; membranes are illustrated as nested diagrams labeled at bottom according to the model´s code of symbols we use for referring membranes; objects are also represented using symbols summarized below the figure; and rules assigned to each membrane area are, for simplicity´s sake, indicated as text indications colored green
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Fig1: P-system model for AR evolution in complex ecosystems. Venn diagram representation showing of the framework of membranes and vocabulary of objects, on which our P-system model is based; membranes are illustrated as nested diagrams labeled at bottom according to the model´s code of symbols we use for referring membranes; objects are also represented using symbols summarized below the figure; and rules assigned to each membrane area are, for simplicity´s sake, indicated as text indications colored green

Mentions: In summary, our P-system consists of a membrane structure composed of five types of EBs and a working alphabet V of objects whose interactive feedback is determined by the set of rules assigned at every EB. This framework can be graphically represented as a Venn diagram (Fig. 1). As shown in the figure, the container diagram (ECO) represents the skin EB. Within ECO, the two next diagrams designated as – Pi and Pj – represent two P-like environmental EBs (the user can however design as many P-like EBs as required). P-like EBs are allowed to contain RS-like, H-like and B-like EBs (represented as diagrams of smaller size). RS-like EBs (designated as i, j, k) represent food, water and sewage reservoirs and are allowed to contain B-like membranes (but not H-like membranes). H-like EBs can be distinguished in subtypes (social classes, species, etc.) using subscript assignations. For example, in the figure we contemplate 3 populations (i, j, k) that may be respectively composed of a number of individuals (for instance 100, 50 and 150, etc.). Each H-like EB is allowed to contain a number of internal B-like EBs (but not RS-like EBs) defining its intrinsic microbiota. B-like EBs can be placed not only within RS-like and H-like EBs but also in P-like EBs and can be differentiated in lineages to which gram and GEC status can be assigned using sub- and superscripts. The status of Gram positive or Gram-negative organisms is assigned using a superscript with two states (minus and plus). In the figure we observe four subtypes (i_, j, k, l) according to the left subscript. Those labeled with the left subscript j belong the GEC-j and those labeled with the subscript k belong to GEC-k. Those having the superscript plus are considered to be gram-positive cells, and those assigned the superscript minus are gram-negative. Logically, population size can also be assigned to each lineage (for example 109 cells per bacterial lineage). The working alphabet is composed of four types of objects (also differentiated in subtypes) summarized below the Venn diagram. In particular the figure shows two AR-like objects (i, j) defining two different AR genes; four A-like objects (i, j, k, l) defining four distinct substances (for instance two antibiotics and two insecticides with different properties); eight PL-like objects representing two plasmids (i, j) each one with four possible states (without AR genes, carrying an ARi gene, an ARj gene or carrying both AR genes); and four G-like objects (i, j, k, l) representing management clocks. AR-like and PL-like objects are restricted to B-like EBs but they can move from a B-like EB into another (to emulate horizontal transfer events). Note, however, that AR objects can be only transferred when carried by a plasmid object. A-like and G-like objects are allowed in all EBs (excepting ECO which is the ultimate container) as they either define substances expected to spread across all environments or periodical actions (in the case of G-like management clocks) stated by the user. Finally, every EB is assigned a set of specific rules (R) designed and tuned with the aim to govern the dynamic of interactions and evolutionary events within each EB according to given priorities (ρ), parameters and conditions indicated by the user.Fig. 1


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)

P-system model for AR evolution in complex ecosystems. Venn diagram representation showing of the framework of membranes and vocabulary of objects, on which our P-system model is based; membranes are illustrated as nested diagrams labeled at bottom according to the model´s code of symbols we use for referring membranes; objects are also represented using symbols summarized below the figure; and rules assigned to each membrane area are, for simplicity´s sake, indicated as text indications colored green
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig1: P-system model for AR evolution in complex ecosystems. Venn diagram representation showing of the framework of membranes and vocabulary of objects, on which our P-system model is based; membranes are illustrated as nested diagrams labeled at bottom according to the model´s code of symbols we use for referring membranes; objects are also represented using symbols summarized below the figure; and rules assigned to each membrane area are, for simplicity´s sake, indicated as text indications colored green
Mentions: In summary, our P-system consists of a membrane structure composed of five types of EBs and a working alphabet V of objects whose interactive feedback is determined by the set of rules assigned at every EB. This framework can be graphically represented as a Venn diagram (Fig. 1). As shown in the figure, the container diagram (ECO) represents the skin EB. Within ECO, the two next diagrams designated as – Pi and Pj – represent two P-like environmental EBs (the user can however design as many P-like EBs as required). P-like EBs are allowed to contain RS-like, H-like and B-like EBs (represented as diagrams of smaller size). RS-like EBs (designated as i, j, k) represent food, water and sewage reservoirs and are allowed to contain B-like membranes (but not H-like membranes). H-like EBs can be distinguished in subtypes (social classes, species, etc.) using subscript assignations. For example, in the figure we contemplate 3 populations (i, j, k) that may be respectively composed of a number of individuals (for instance 100, 50 and 150, etc.). Each H-like EB is allowed to contain a number of internal B-like EBs (but not RS-like EBs) defining its intrinsic microbiota. B-like EBs can be placed not only within RS-like and H-like EBs but also in P-like EBs and can be differentiated in lineages to which gram and GEC status can be assigned using sub- and superscripts. The status of Gram positive or Gram-negative organisms is assigned using a superscript with two states (minus and plus). In the figure we observe four subtypes (i_, j, k, l) according to the left subscript. Those labeled with the left subscript j belong the GEC-j and those labeled with the subscript k belong to GEC-k. Those having the superscript plus are considered to be gram-positive cells, and those assigned the superscript minus are gram-negative. Logically, population size can also be assigned to each lineage (for example 109 cells per bacterial lineage). The working alphabet is composed of four types of objects (also differentiated in subtypes) summarized below the Venn diagram. In particular the figure shows two AR-like objects (i, j) defining two different AR genes; four A-like objects (i, j, k, l) defining four distinct substances (for instance two antibiotics and two insecticides with different properties); eight PL-like objects representing two plasmids (i, j) each one with four possible states (without AR genes, carrying an ARi gene, an ARj gene or carrying both AR genes); and four G-like objects (i, j, k, l) representing management clocks. AR-like and PL-like objects are restricted to B-like EBs but they can move from a B-like EB into another (to emulate horizontal transfer events). Note, however, that AR objects can be only transferred when carried by a plasmid object. A-like and G-like objects are allowed in all EBs (excepting ECO which is the ultimate container) as they either define substances expected to spread across all environments or periodical actions (in the case of G-like management clocks) stated by the user. Finally, every EB is assigned a set of specific rules (R) designed and tuned with the aim to govern the dynamic of interactions and evolutionary events within each EB according to given priorities (ρ), parameters and conditions indicated by the user.Fig. 1

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