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

Abstract

BackgroundAntibiotic 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.ResultsIn 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.ConclusionsThe 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.ReviewersThis article was reviewed by Eugene V. Koonin, and Eric Bapteste.

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

“…Details of the basic model’s design have been presented elsewhere (11, 12, 13). These integrative models are mostly fed with data on plasmid biology obtained through in-vitro experiments and suggest that predictions based only on laboratory data might not necessarily reflect the evolution of resistance in natural clinical landscapes.…”

“…All computational simulations were performed using an updated version of the Antibiotic Resistance Evolution Simulator (ARES), which is a P system software implementation for modeling antibiotic resistance evolution (11, 13). The current version of ARES (2.0) can be freely downloaded at https://sourceforge.net/projects/ares-simulator/.…”

“…However, the effects of these changes on the kinetics of plasmid resistance genes among bacterial populations are necessarily influenced by numerous other factors acting in actual biological ecosystems, such as the intestinal microbiota. Of these factors, our previously published studies on modeling by membrane computing (11-13) considered the following: the ecosystem’s bacterial composition, the density and replication rate of cells in each species, their mutation frequencies, the content in chromosomal resistance genes, the selection intensity of resistant organisms due to differing antibiotic exposures, the elimination of susceptible bacterial populations by antibiotic treatment, the transmission of resistant bacteria among human hosts in hospital settings under differing admission-discharge rates, cross-colonization, and exposure to various antibiotics, as well as the influence of antibiotic resistance in non-hospitalized individuals who eventually pass through the hospital environment.…”

Simulating multi-level dynamics of antimicrobial resistance in a membrane computing model

“…The mobility of entities, objects, across membranes is possible according to pre-established rewriting rules, and the collection of multisets of entities will evolve in a synchronous, parallel, and non-deterministic manner. The objects have assigned rules to pass through membranes (to mimic intracellular or intercellular transmission (8,9), to dissolve (to mimic elimination), and to divide themselves (to mimic replication). In this work, we use a P system to simulate multi-level dynamics of antibiotic resistance, based on our first published prototype (8,9).…”

“…The objects have assigned rules to pass through membranes (to mimic intracellular or intercellular transmission (8,9), to dissolve (to mimic elimination), and to divide themselves (to mimic replication). In this work, we use a P system to simulate multi-level dynamics of antibiotic resistance, based on our first published prototype (8,9). This computational model facilitates an approach that is computationally hard to accomplish or simply impossible to address experimentally.…”