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FAQ Policy. About this book Arguing for an evolutionary perspective, this book directly challenges the Standard Social Science Model SSSM on which public policy has often been based. Show all. Show next xx. Services for this book Download High-Resolution Cover. Somit S. PAGE 1. Four of these cellular lineages will be simulated as Gram-negative, while the four other will be Gram-positive. All bacterial cells of all lineages are allowed to carry three distinct types of intracellular plasmids PL 1 , PL 2 and PL 3 capable of horizontal transfer.
In the starting configuration, the third plasmid type PL 3 does not carry AR genes but during the course of the simulation it is allowed to recruit any of two types of AR genes simulared. Each P-environment has food and water supplies RS-like membranes to simulate the feeding of H individuals.
The overall aim of this tutorial is to compare the response of the microbial communities in two main scenarios control and case study simulated under three distinct interventions increase of the rate of migration, increase of antibiotic dosage, and fumigation. ARES is a new membrane computing simulator we have launched with the aim to help researchers develop computational models oriented to help elucidation of hidden aspects of the epidemiological and ecological complex patterns of AR that cannot be easily traced in the real world, due to both practical and complexity reasons.
The underlying computational model of ARES is a P-system that differs from other models including other previously published P-systems in that both the framework and set of rules permit the user to simulate stochastic dynamics at different environmental subcellular, cellular and supracellular levels of the simulated ecosystem. This ability is what allows the user to asses the reciprocal feedback between the different carriers involved in the dissemination of AR genes, edit the configuration of the model scenario and then re-run the simulation with changing the parameters until a correct description of the AR process is approximated according to real world observations.
The project is open to all other experts interested in contributing expertise and criticisms.
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I must indicate that I am not an expert in membrane computing. That said, my impression is that ARES is a highly promising, flexible platform for modeling the complex dynamics of antibiotic resistance evolution. The description of the model is quite logical and meticulous. Given the obvious, overarching importance of the study of antibiotic resistance, I expect that this tool will be in high demand in the research community. We hope the device and the formalism to be of interest for researchers working in predictive models for AR evolution or in System Biology.
This work is stimulating to read and remarkable by its ambition: simulating complex systems and following the evolutionary dynamics of a diversity of objects within these systems. This manuscript introduces a rather intuitive formalization for these two tasks, taking advantage of P-systems.
However intuitive these approaches are, I fell that an additional illustration, typically a Venn diagram for a simulation considered worthy of interest by the authors, would greatly help most readers to go beyond some of the rather abstract and potentially discouraging formalism used in the main text, e.
Such a Venn diagram would also probably help to immediately appreciate what hierarchically adjacent regions? This second version of the manuscript includes a Venn diagram designated as Fig. The proposed implemented specifications seem sound and numerous enough to be of use to interested scientists, or to make them feel scared by the many decisions one must make to fully benefit from this approach. Typically, not being an expert on the evolution of such systems, it was difficult for me to appreciate what sets of parameters and what rules were realistic ones.
While I realize that in principle comparing simulations results with the biological reality may help one to a posteriori decipher what parameters were indeed realistic, I am a bit skeptical that in practice this strategy will necessarily work so well for several reasons:. How can one measure the similarity between simulated results and the biological reality? It might be useful to implement such comparative measures in ARES. How can one know when the simulation results are significantly close to the reality to be approximated?
Some statistics would be needed here. Consistently, how can one discriminate between multiple scenarios producing comparable results what scenario is more realistic, if any, especially with so many parameters? Is there a way to compare, say, the complexity of two models with equally likely outputs? I guess these might become tasks for the future, should ARES evolve in a way that helps its users to explore parameters ranges in a statistically meaningful framework.
This motivated us to design ARES as friendly as possible in order to let the users to deal with membrane computing without being an expert in membrane computing.
However, it is also true that to appropriately manage ARES, the user must do an first effort in getting familiar with at least the basic principles of membrane computing and also make another effort in getting some training. These issues, motivated us to create several support sections in ARES see also our response to the minor comments. In this first release we have designed the basal P-system model and have programmed the software implementation of this P-system. At present, we are preparing new implementations. The 3 questions you address are excellent examples of new improvements we take note in order to implement them as soon as possible.
Some generic terms related to P-systems, such as membrane structure? Hope you will find this term more appropiate. It is easy to get lost in the numerous options specifications and such : maybe having examples of what are considered as realistic parameters in some known environment i. Maybe such a pre-implemented P-system is just what the import environments? In addition we have also created a contact section for users support as well as another section for frequently asked questions FAQs.
This new section also includes a form for users to make us recommendations in regards of new rules not yet contemplated that we will also try to program as soon as possible. Finally, let us to make one clarification; at present, there are not pre-implemented P-systems in ARES but the possibility to re-use the complete or partial configuration of a P-system previously introduced and stored in ARES by the user. The option did not work for you because you do not have any P-system configuration in ARES previously stored. We have clarified this in FAQS and where correspond in the system of forms of ARES but we also take note in any case of this interesting suggestion - have a collection of P-system configuration modules i.
For GEC descriptions, please explain what distinct numerical values will mean i. Or does it indicate that a particular set of bacteria can be split into 3 GEC, when 3 is the value chosen? It might be nice to also have a Venn diagram as the output to compare the overall picture before? Bear in mind that one expect to find distinct types and subtypes of EBs and objects in the starting or final configuration of a more or less regular P-system for AR evolution, as well as a variety of rules assigned to each EB subtype. Note that although the population size of some membranes and objects to plot can be defined in single units no more than a ten , the population size of just one bacterial lineage could reach thousands or even millions of EBs.
We are however, working in order to find a satisfactory graphical solution when representing P-system complex scenarios via ARES. Counteracting antibiotic resistance: breaking barriers among antibacterial strategies. Expert Opin Ther Targets. Public health evolutionary biology of antimicrobial resistance: priorities for intervention.
Evol Appl. Ecology and evolution as targets: the need for novel eco-evo drugs and strategies to fight antibiotic resistance. Antimicrob Agents Chemother. Ready for a world without antibiotics? The pensieres antibiotic resistance call to action. Antimicrob Resist Infect Control.
Dynamics of Hierarchical Systems: An Evolutionary Approach by John S. Nicolis - yremebahifoh.cf
Antibiotic resistance-the need for global solutions. Lancet Infect Dis. Levy SB, Marshall B. Antibacterial resistance worldwide: causes, challenges and responses. Nat Med. The role of the natural environment in the emergence of antibiotic resistance in gram-negative bacteria. Food animals and antimicrobials: impacts on human health. Clin Microbiol Rev. Commensals: underappreciated reservoir of antibiotic resistance. The shared antibiotic resistome of soil bacteria and human pathogens.
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