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

2018 ◽  
Author(s):  
Marcelino Campos ◽  
Rafael Capilla ◽  
Fernando Naya ◽  
Ricardo Futami ◽  
Teresa Coque ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Evolutionary Biology ◽  
Membrane Computing ◽  
Patient Flow ◽  
P System ◽  
Biological Organization ◽  
Computing Model ◽  
Computing Paradigm ◽  
Cross Transmission ◽  
Multi Level

AbstractMembrane Computing is a bio-inspired computing paradigm, whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested “membrane-surrounded entities” able to divide, propagate and die, be transferred into other membranes, exchange informative material according to flexible rules, mutate and being selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multi-level evolutionary biology of antibiotic resistance. We examine a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and viceversa, cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, antibiotics and dosing in opening spaces in the microbiota where resistant phenotypes multiply. We can also evaluate the selective strength of some drugs and the influence of the time-0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multi-level analysis of complex microbial landscapes.

scholarly journals Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

mBio ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. e02460-18 ◽  
Author(s):  
Marcelino Campos ◽  
Rafael Capilla ◽  
Fernando Naya ◽  
Ricardo Futami ◽  
Teresa Coque ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Evolutionary Biology ◽  
Membrane Computing ◽  
Patient Flow ◽  
Local Level ◽  
Explanatory Power ◽  
P System ◽  
Biological Organization ◽  
Computing Model ◽  
Computing Paradigm

ABSTRACT Membrane computing is a bio-inspired computing paradigm whose devices are the so-called membrane systems or P systems. The P system designed in this work reproduces complex biological landscapes in the computer world. It uses nested “membrane-surrounded entities” able to divide, propagate, and die; to be transferred into other membranes; to exchange informative material according to flexible rules; and to mutate and be selected by external agents. This allows the exploration of hierarchical interactive dynamics resulting from the probabilistic interaction of genes (phenotypes), clones, species, hosts, environments, and antibiotic challenges. Our model facilitates analysis of several aspects of the rules that govern the multilevel evolutionary biology of antibiotic resistance. We examined a number of selected landscapes where we predict the effects of different rates of patient flow from hospital to the community and vice versa, the cross-transmission rates between patients with bacterial propagules of different sizes, the proportion of patients treated with antibiotics, and the antibiotics and dosing found in the opening spaces in the microbiota where resistant phenotypes multiply. We also evaluated the selective strengths of some drugs and the influence of the time 0 resistance composition of the species and bacterial clones in the evolution of resistance phenotypes. In summary, we provide case studies analyzing the hierarchical dynamics of antibiotic resistance using a novel computing model with reciprocity within and between levels of biological organization, a type of approach that may be expanded in the multilevel analysis of complex microbial landscapes. IMPORTANCE The work that we present here represents the culmination of many years of investigation in looking for a suitable methodology to simulate the multihierarchical processes involved in antibiotic resistance. Everything started with our early appreciation of the different independent but embedded biological units that shape the biology, ecology, and evolution of antibiotic-resistant microorganisms. Genes, plasmids carrying these genes, cells hosting plasmids, populations of cells, microbial communities, and host's populations constitute a complex system where changes in one component might influence the other ones. How would it be possible to simulate such a complexity of antibiotic resistance as it occurs in the real world? Can the process be predicted, at least at the local level? A few years ago, and because of their structural resemblance to biological systems, we realized that membrane computing procedures could provide a suitable frame to approach these questions. Our manuscript describes the first application of this modeling methodology to the field of antibiotic resistance and offers a bunch of examples—just a limited number of them in comparison with the possible ones to illustrate its unprecedented explanatory power.

A Novel Membrane Clustering Algorithm Based on Tissue-like P System

2016 ◽  
Vol 2 (2) ◽  
pp. 409
Author(s):  
Yan Huaning ◽  
Xiang Laisheng ◽  
Liu Xiyu ◽  
Xue Jie
Keyword(s):  
Clustering Algorithm ◽  
Membrane Computing ◽  
P System ◽  
Data Sets ◽  
Turing Machines ◽  
Computing Systems ◽  
Swarm Optimization ◽  
Computing Model ◽  
Clustering Quality ◽  
Data Points

<span lang="EN-US">Clustering is a process of partitioning data points into different clusters due to their similarity, as a powerful technique of data mining, clustering is widely used in many fields. Membrane computing is a computing model abstracting from the biological area, </span><span lang="EN-US">these computing systems are proved to be so powerful that they are equivalent with Turing machines. In this paper, a modified inversion particle swarm optimization was proposed, this method and the mutational mechanism of genetics algorithm were used to combine with the tissue-like P system, through these evolutionary algorithms and the P system, the idea of a novel membrane clustering algorithm could come true. Experiments were tested on six data sets, by comparing the clustering quality with the GA-K-means, PSO-K-means and K-means proved the superiority of our method.</span>

scholarly journals An Adaptive Optimization Spiking Neural P System for Binary Problems

2020 ◽  
Vol 31 (01) ◽  
pp. 2050054 ◽  
Author(s):  
Ming Zhu ◽  
Qiang Yang ◽  
Jianping Dong ◽  
Gexiang Zhang ◽  
Xiantai Gou ◽  
...  
Keyword(s):  
Knapsack Problem ◽  
Membrane Computing ◽  
Combinatorial Problems ◽  
P Systems ◽  
P System ◽  
Adaptive Optimization ◽  
Spiking Neural P System ◽  
Multiple Faults ◽  
Computing Model ◽  
Bus System

Optimization Spiking Neural P System (OSNPS) is the first membrane computing model to directly derive an approximate solution of combinatorial problems with a specific reference to the 0/1 knapsack problem. OSNPS is composed of a family of parallel Spiking Neural P Systems (SNPS) that generate candidate solutions of the binary combinatorial problem and a Guider algorithm that adjusts the spiking probabilities of the neurons of the P systems. Although OSNPS is a pioneering structure in membrane computing optimization, its performance is competitive with that of modern and sophisticated metaheuristics for the knapsack problem only in low dimensional cases. In order to overcome the limitations of OSNPS, this paper proposes a novel Dynamic Guider algorithm which employs an adaptive learning and a diversity-based adaptation to control its moving operators. The resulting novel membrane computing model for optimization is here named Adaptive Optimization Spiking Neural P System (AOSNPS). Numerical result shows that the proposed approach is effective to solve the 0/1 knapsack problems and outperforms multiple various algorithms proposed in the literature to solve the same class of problems even for a large number of items (high dimensionality). Furthermore, case studies show that a AOSNPS is effective in fault sections estimation of power systems in different types of fault cases: including a single fault, multiple faults and multiple faults with incomplete and uncertain information in the IEEE 39 bus system and IEEE 118 bus system.

Arithmetic Expression Evaluation in Membrane Computing with Priority

2011 ◽  
Vol 225-226 ◽  
pp. 1115-1119 ◽  
Author(s):  
Ping Guo ◽  
Sheng Jiao Liu
Keyword(s):  
Membrane Structure ◽  
Membrane Computing ◽  
Arithmetic Operation ◽  
P System ◽  
Arithmetic Expression ◽  
Computing Model ◽  
Model Based ◽  
Operation Rules ◽  
Expression Evaluation

Arithmetic operation and arithmetic expression evaluation are basic operations of a computing model. Based on the rules with priority, this paper discusses arithmetic operation and arithmetic expression evaluation in transition P system. We present the rules of arithmetic operation and the arithmetic of constructing arithmetic expression evaluation’s membrane structure based on the arithmetic operation rules. In the arithmetic operation rules, we use some specifically symbols to make rules applied in a maximally parallel manner, and also assure synchronization need during the rules application.

Signed Integer Arithmetic on Spiking Neural P System

2010 ◽  
Vol 20-23 ◽  
pp. 779-784 ◽  
Author(s):  
Xi Gao ◽  
Hai Zhu Chen
Keyword(s):  
Membrane Computing ◽  
P System ◽  
Spiking Neural P System ◽  
Spiking Neural P Systems ◽  
Integer Arithmetic ◽  
Computing Model ◽  
Computational Capability ◽  
Computer Chips ◽  
Mathematical Problems ◽  
Hard Problems

Spiking neural P systems are a new computing model incorporating the ideas of spiking neurons into membrane computing. It has been shown that they have powerful computational capability and potential capability in solving computationally hard problems. The paper describes the implementation of signed integer arithmetic operations on spiking neural P system with the inputs and outputs encoded as appropriate sequences of spikes. The present work may be considered as the basis for more complex applications, maybe for constructing computer chips or for solving general mathematical problems.

scholarly journals A Survey of Nature-Inspired Computing

2021 ◽  
Vol 54 (1) ◽  
pp. 1-31
Author(s):  
Bosheng Song ◽  
Kenli Li ◽  
David Orellana-Martín ◽  
Mario J. Pérez-Jiménez ◽  
Ignacio PéRez-Hurtado
Keyword(s):  
Membrane Computing ◽  
P Systems ◽  
Natural Computing ◽  
P System ◽  
Open Problems ◽  
Membrane Function ◽  
Natural Systems ◽  
Computing Paradigm ◽  
Complete Problems ◽  
Nature Inspired Computing

Nature-inspired computing is a type of human-designed computing motivated by nature, which is based on the employ of paradigms, mechanisms, and principles underlying natural systems. In this article, a versatile and vigorous bio-inspired branch of natural computing, named membrane computing is discussed. This computing paradigm is aroused by the internal membrane function and the structure of biological cells. We first introduce some basic concepts and formalisms of membrane computing, and then some basic types or variants of P systems (also named membrane systems ) are presented. The state-of-the-art computability theory and a pioneering computational complexity theory are presented with P system frameworks and numerous solutions to hard computational problems (especially NP -complete problems) via P systems with membrane division are reported. Finally, a number of applications and open problems of P systems are briefly described.

scholarly journals Cohorting KPC+ Klebsiella pneumoniae (KPC-Kp)–positive patients: A genomic exposé of cross-colonization hazards in a long-term acute-care hospital (LTACH)

2020 ◽  
Vol 41 (10) ◽  
pp. 1162-1168
Author(s):  
Shawn E. Hawken ◽  
Mary K. Hayden ◽  
Karen Lolans ◽  
Rachel D. Yelin ◽  
Robert A. Weinstein ◽  
...  
Keyword(s):  
Antibiotic Resistance ◽  
Klebsiella Pneumoniae ◽  
Acute Care ◽  
Resistance Genes ◽  
Acute Care Hospital ◽  
Antibiotic Resistance Genes ◽  
Care Hospital ◽  
Cross Transmission ◽  
The Impact

AbstractObjective:Cohorting patients who are colonized or infected with multidrug-resistant organisms (MDROs) protects uncolonized patients from acquiring MDROs in healthcare settings. The potential for cross transmission within the cohort and the possibility of colonized patients acquiring secondary isolates with additional antibiotic resistance traits is often neglected. We searched for evidence of cross transmission of KPC+ Klebsiella pneumoniae (KPC-Kp) colonization among cohorted patients in a long-term acute-care hospital (LTACH), and we evaluated the impact of secondary acquisitions on resistance potential.Design:Genomic epidemiological investigation.Setting:A high-prevalence LTACH during a bundled intervention that included cohorting KPC-Kp–positive patients.Methods:Whole-genome sequencing (WGS) and location data were analyzed to identify potential cases of cross transmission between cohorted patients.Results:Secondary KPC-Kp isolates from 19 of 28 admission-positive patients were more closely related to another patient’s isolate than to their own admission isolate. Of these 19 cases, 14 showed strong genomic evidence for cross transmission (<10 single nucleotide variants or SNVs), and most of these patients occupied shared cohort floors (12 patients) or rooms (4 patients) at the same time. Of the 14 patients with strong genomic evidence of acquisition, 12 acquired antibiotic resistance genes not found in their primary isolates.Conclusions:Acquisition of secondary KPC-Kp isolates carrying distinct antibiotic resistance genes was detected in nearly half of cohorted patients. These results highlight the importance of healthcare provider adherence to infection prevention protocols within cohort locations, and they indicate the need for future studies to assess whether multiple-strain acquisition increases risk of adverse patient outcomes.

Membrane Computing Model for Software Requirement Engineering

2010 ◽  
Author(s):  
Velayutham Pavanasam ◽  
Chandrasekaran Subramaniam ◽  
Thulukkanam Srinivasan ◽  
Jitendra Kumar Jain D
Keyword(s):  
Membrane Computing ◽  
Requirement Engineering ◽  
Computing Model ◽  
Software Requirement

A New P System Based Genetic Algorithm

2016 ◽  
Vol 4 (1) ◽  
pp. 165
Author(s):  
Caiping Hou ◽  
Xiyu Liu
Keyword(s):  
Genetic Algorithm ◽  
Genetic Drift ◽  
Parallel Mechanism ◽  
Membrane Computing ◽  
P System

<p>For the “early convergence” or the “genetic drift” of the genetic algorithm, this paper proposes a new genetic algorithm based on P system. Based on the parallel mechanism of P system in membrane computing, we put forward the new P system based genetic algorithm (PBGA). So that we can improve the performance of GA.</p>

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