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Zhang L, Xu F, Neri F. An Asynchronous Spiking Neural Membrane System for Edge Detection. Int J Neural Syst 2024; 34:2450023. [PMID: 38490956 DOI: 10.1142/s0129065724500230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
Spiking neural membrane systems (SN P systems) are a class of bio-inspired models inspired by the activities and connectivity of neurons. Extensive studies have been made on SN P systems with synchronization-based communication, while further efforts are needed for the systems with rhythm-based communication. In this work, we design an asynchronous SN P system with resonant connections where all the enabled neurons in the same group connected by resonant connections should instantly produce spikes with the same rhythm. In the designed system, each of the three modules implements one type of the three operations associated with the edge detection of digital images, and they collaborate each other through the resonant connections. An algorithm called EDSNP for edge detection is proposed to simulate the working of the designed asynchronous SN P system. A quantitative analysis of EDSNP and the related methods for edge detection had been conducted to evaluate the performance of EDSNP. The performance of the EDSNP in processing the testing images is superior to the compared methods, based on the quantitative metrics of accuracy, error rate, mean square error, peak signal-to-noise ratio and true positive rate. The results indicate the potential of the temporal firing and the proper neuronal connections in the SN P system to achieve good performance in edge detection.
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Affiliation(s)
- Luping Zhang
- Jiangxi Engineering Technology Research Center of Nuclear, Geoscience Data Science and System, Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data Technology, School of Information Engineering, East China University of Technology, Nanchang 330013, Jiangxi, P. R. China
| | - Fei Xu
- Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, P. R. China
| | - Ferrante Neri
- NICE Research Group, School of Computer Science and Electronic Engineering, University of Surrey, Guildford, Surrey GU2 7XH, UK
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Dong J, Zhang G, Hu Y, Wu Y, Rong H. An Optimization Numerical Spiking Neural Membrane System with Adaptive Multi-Mutation Operators for Brain Tumor Segmentation. Int J Neural Syst 2024:2450036. [PMID: 38686911 DOI: 10.1142/s0129065724500369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
Magnetic Resonance Imaging (MRI) is an important diagnostic technique for brain tumors due to its ability to generate images without tissue damage or skull artifacts. Therefore, MRI images are widely used to achieve the segmentation of brain tumors. This paper is the first attempt to discuss the use of optimization spiking neural P systems to improve the threshold segmentation of brain tumor images. To be specific, a threshold segmentation approach based on optimization numerical spiking neural P systems with adaptive multi-mutation operators (ONSNPSamos) is proposed to segment brain tumor images. More specifically, an ONSNPSamo with a multi-mutation strategy is introduced to balance exploration and exploitation abilities. At the same time, an approach combining the ONSNPSamo and connectivity algorithms is proposed to address the brain tumor segmentation problem. Our experimental results from CEC 2017 benchmarks (basic, shifted and rotated, hybrid, and composition function optimization problems) demonstrate that the ONSNPSamo is better than or close to 12 optimization algorithms. Furthermore, case studies from BraTS 2019 show that the approach combining the ONSNPSamo and connectivity algorithms can more effectively segment brain tumor images than most algorithms involved.
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Affiliation(s)
- Jianping Dong
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Gexiang Zhang
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yangheng Hu
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Yijin Wu
- School of Automation, Chengdu University of Information Technology, Chengdu 610225, China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, China
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Liu Q, Huang Y, Yang Q, Peng H, Wang J. An Attention-Aware Long Short-Term Memory-Like Spiking Neural Model for Sentiment Analysis. Int J Neural Syst 2023; 33:2350037. [PMID: 37303084 DOI: 10.1142/s0129065723500375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
LSTM-SNP model is a recently developed long short-term memory (LSTM) network, which is inspired from the mechanisms of spiking neural P (SNP) systems. In this paper, LSTM-SNP is utilized to propose a novel model for aspect-level sentiment analysis, termed as ALS model. The LSTM-SNP model has three gates: reset gate, consumption gate and generation gate. Moreover, attention mechanism is integrated with LSTM-SNP model. The ALS model can better capture the sentiment features in the text to compute the correlation between context and aspect words. To validate the effectiveness of the ALS model for aspect-level sentiment analysis, comparison experiments with 17 baseline models are conducted on three real-life data sets. The experimental results demonstrate that the ALS model has a simpler structure and can achieve better performance compared to these baseline models.
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Affiliation(s)
- Qian Liu
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Yanping Huang
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Qian Yang
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Hong Peng
- School of Computer and Software Engineering, Xihua University, P. R. China
| | - Jun Wang
- School of Electrical Engineering and Electronic Information, Xihua University, P. R. China
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Campos M, Sempere JM, Galán JC, Moya A, Cantón R, Llorens C, Baquero F. Simulating the efficacy of vaccines on the epidemiological dynamics of SARS-CoV-2 in a membrane computing model. Microlife 2022; 3:uqac018. [PMID: 37223355 PMCID: PMC10117710 DOI: 10.1093/femsml/uqac018] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 09/07/2022] [Indexed: 05/25/2023]
Abstract
Membrane computing is a natural computing procedure inspired in the compartmental structure of living cells. This approach allows mimicking the complex structure of biological processes, and, when applied to transmissible diseases, can simulate a virtual 'epidemic' based on interactions between elements within the computational model according to established conditions. General and focused vaccination strategies for controlling SARS-Cov-2 epidemics have been simulated for 2.3 years from the emergence of the epidemic in a hypothetical town of 10320 inhabitants in a country with mean European demographics where COVID-19 is imported. The age and immunological-response groups of the hosts and their lifestyles were minutely examined. The duration of natural, acquired immunity influenced the results; the shorter the duration, the more endemic the process, resulting in higher mortality, particularly among elderly individuals. During epidemic valleys between waves, the proportion of infected patients belonging to symptomatic groups (mostly elderly) increased in the total population, a population that largely benefits from standard double vaccination, particularly with boosters. There was no clear difference when comparing booster shots provided at 4 or 6 months after standard double-dose vaccination. Vaccines even of moderate efficacy (short-term protection) were effective in decreasing the number of symptomatic cases. Generalized vaccination of the entire population (all ages) added little benefit to overall mortality rates, and this situation also applied for generalized lockdowns. Elderly-only vaccination and lockdowns, even without general interventions directed to reduce population transmission, is sufficient for dramatically reducing mortality.
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Affiliation(s)
- Marcelino Campos
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), M-607, km 9,1 28034 Madrid, Spain
- Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politécnica de Valencia, Camí de Vera s/n, 46022 Valencia, Spain
| | - José M Sempere
- Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politécnica de Valencia, Camí de Vera s/n, 46022 Valencia, Spain
| | - Juan C Galán
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), M-607, km 9,1 28034 Madrid, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), M-607, km 9,1. 28034 Madrid, Spain
| | - Andrés Moya
- Foundation for the Promotion of Sanitary and Biomedical Research of the Valencian Community (FISABIO), Av. Cataluña 21, 46020 Valencia, Spain
- Integrative Systems Biology Institute, University of Valencia and Spanish Research Council (CSIC), Av. Cataluña 31, 46020, Valencia, Spain
| | - Rafael Cantón
- Department of Microbiology, Ramón y Cajal University Hospital, Ramón y Cajal Institute for Health Research (IRYCIS), M-607, km 9,1 28034 Madrid, Spain
- Centro de Investigación Biomédica en Red Enfermedades Infecciosas (CIBERINFECT), M-607, km 9,1. 28034 Madrid, Spain
| | - Carlos Llorens
- Biotechvana, Valencia, CEEI Building, Valencia Technological Park., C. Agustín Escardino 9, 46980, Paterna, Spain
| | - Fernando Baquero
- Corresponding author: Dpt. Microbiology, Ramón y Cajal University Hospital, Centro de Investigación en Red de Epidemiología y Salud Pública (CIBERESP), M-607, km 9,1. 28034 Madrid, Spain. E-mail:
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Campos M, Sempere JM, Galán JC, Moya A, Llorens C, de-Los-Angeles C, Baquero-Artigao F, Cantón R, Baquero F. Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model. ACTA ACUST UNITED AC 2021; 2:uqab011. [PMID: 34642663 PMCID: PMC8499911 DOI: 10.1093/femsml/uqab011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Accepted: 09/03/2021] [Indexed: 01/08/2023]
Abstract
Epidemics caused by microbial organisms are part of the natural phenomena of increasing biological complexity. The heterogeneity and constant variability of hosts, in terms of age, immunological status, family structure, lifestyle, work activities, social and leisure habits, daily division of time and other demographic characteristics make it extremely difficult to predict the evolution of epidemics. Such prediction is, however, critical for implementing intervention measures in due time and with appropriate intensity. General conclusions should be precluded, given that local parameters dominate the flow of local epidemics. Membrane computing models allows us to reproduce the objects (viruses and hosts) and their interactions (stochastic but also with defined probabilities) with an unprecedented level of detail. Our LOIMOS model helps reproduce the demographics and social aspects of a hypothetical town of 10 320 inhabitants in an average European country where COVID-19 is imported from the outside. The above-mentioned characteristics of hosts and their lifestyle are minutely considered. For the data in the Hospital and the ICU we took advantage of the observations at the Nursery Intensive Care Unit of the Consortium University General Hospital, Valencia, Spain (included as author). The dynamics of the epidemics are reproduced and include the effects on viral transmission of innate and acquired immunity at various ages. The model predicts the consequences of delaying the adoption of non-pharmaceutical interventions (between 15 and 45 days after the first reported cases) and the effect of those interventions on infection and mortality rates (reducing transmission by 20, 50 and 80%) in immunological response groups. The lockdown for the elderly population as a single intervention appears to be effective. This modeling exercise exemplifies the application of membrane computing for designing appropriate multilateral interventions in epidemic situations.
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Affiliation(s)
- M Campos
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
| | - J M Sempere
- Valencian Research Institute for Artificial Intelligence (VRAIN), Universitat Politècnica de Valencia, Camí de Vera s/n, 46022 Valencia, Spain
| | - J C Galán
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
| | - A Moya
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública, M-607, km 9,1. 28034 Madrid, Spain
| | - C Llorens
- Biotechvana, Valencia, CEEI Building, Valencia Technological Park., C. agustín Escardino 9, 46980, Paterna, Valencia, Spain
| | - C de-Los-Angeles
- Nursery Unit, Intensive Care Unit and Pain Therapy, Consortium University General Hospital (CHGUV)., Av. Tres Cruces 2, 46014 Valencia, Spain
| | - F Baquero-Artigao
- Department of Infectious Diseases and Tropical Pediatrics, La Paz University Hospital., Av. Monforte de Lemos 2D, 28029 Madrid, Spain
| | - R Cantón
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
| | - F Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, M-607, km 9,1 28034 Madrid, Spain
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Abstract
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.
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Affiliation(s)
- Ming Zhu
- School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China
| | - Qiang Yang
- School of Control Engineering, Chengdu University of Information Technology, Chengdu 610225, P. R. China
| | - Jianping Dong
- College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Gexiang Zhang
- College of Information Science and Technology, Chengdu University of Technology, Chengdu 610059, P. R. China
| | - Xiantai Gou
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Haina Rong
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Prithwineel Paul
- School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, P. R. China
| | - Ferrante Neri
- COL Laboratory, School of Computer Science, University of Nottingham, Nottingham, UK
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Campos M, San Millán Á, Sempere JM, Lanza VF, Coque TM, Llorens C, Baquero F. Simulating the Influence of Conjugative-Plasmid Kinetic Values on the Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. Antimicrob Agents Chemother 2020; 64:e00593-20. [PMID: 32457104 DOI: 10.1128/AAC.00593-20] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Accepted: 05/15/2020] [Indexed: 12/17/2022] Open
Abstract
Bacterial plasmids harboring antibiotic resistance genes are critical in the spread of antibiotic resistance. It is known that plasmids differ in their kinetic values, i.e., conjugation rate, segregation rate by copy number incompatibility with related plasmids, and rate of stochastic loss during replication. They also differ in cost to the cell in terms of reducing fitness and in the frequency of compensatory mutations compensating plasmid cost. However, we do not know how variation in these values influences the success of a plasmid and its resistance genes in complex ecosystems, such as the microbiota. Genes are in plasmids, plasmids are in cells, and cells are in bacterial populations and microbiotas, which are inside hosts, and hosts are in human communities at the hospital or the community under various levels of cross-colonization and antibiotic exposure. Differences in plasmid kinetics might have consequences on the global spread of antibiotic resistance. New membrane computing methods help to predict these consequences. In our simulation, conjugation frequency of at least 10-3 influences the dominance of a strain with a resistance plasmid. Coexistence of different antibiotic resistances occurs if host strains can maintain two copies of similar plasmids. Plasmid loss rates of 10-4 or 10-5 or plasmid fitness costs of ≥0.06 favor plasmids located in the most abundant species. The beneficial effect of compensatory mutations for plasmid fitness cost is proportional to this cost at high mutation frequencies (10-3 to 10-5). The results of this computational model clearly show how changes in plasmid kinetics can modify the entire population ecology of antibiotic resistance in the hospital setting.
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Yuan J, Guo D, Zhang G, Paul P, Zhu M, Yang Q. A Resolution-Free Parallel Algorithm for Image Edge Detection within the Framework of Enzymatic Numerical P Systems. Molecules 2019; 24:E1235. [PMID: 30934868 DOI: 10.3390/molecules24071235] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2019] [Revised: 03/12/2019] [Accepted: 03/23/2019] [Indexed: 12/01/2022] Open
Abstract
Image edge detection is a fundamental problem in image processing and computer vision, particularly in the area of feature extraction. However, the time complexity increases squarely with the increase of image resolution in conventional serial computing mode. This results in being unbearably time consuming when dealing with a large amount of image data. In this paper, a novel resolution free parallel implementation algorithm for gradient based edge detection, namely EDENP, is proposed. The key point of our method is the introduction of an enzymatic numerical P system (ENPS) to design the parallel computing algorithm for image processing for the first time. The proposed algorithm is based on a cell-like P system with a nested membrane structure containing four membranes. The start and stop of the system is controlled by the variables in the skin membrane. The calculation of edge detection is performed in the inner three membranes in a parallel way. The performance and efficiency of this algorithm are evaluated on the CUDA platform. The main advantage of EDENP is that the time complexity of O(1) can be achieved regardless of image resolution theoretically.
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Campos M, Capilla R, Naya F, Futami R, Coque T, Moya A, Fernandez-Lanza V, Cantón R, Sempere JM, Llorens C, Baquero F. Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model. mBio 2019; 10:mBio.02460-18. [PMID: 30696743 PMCID: PMC6355984 DOI: 10.1128/mbio.02460-18] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
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.
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Affiliation(s)
- Marcelino Campos
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
- Department of Information Systems and Computation (DSIC), Universitat Politècnica de València, Valencia, Spain
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain
| | | | | | | | - Teresa Coque
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
- Antibiotic Resistance and Bacterial Virulence Unit (HRYC-CSIC), Superior Council of Scientific Research (CSIC), Madrid, Spain
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain
| | - Andrés Moya
- Integrative Systems Biology Institute, University of Valencia and Spanish Research Council (CSIC), Paterna, Valencia, Spain
- Foundation for the Promotion of Sanitary and Biomedical Research in the Valencian Community (FISABIO), Valencia, Spain
| | - Val Fernandez-Lanza
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
- Bioinformatics Support Unit, IRYCIS, Madrid, Spain
| | - Rafael Cantón
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
- Antibiotic Resistance and Bacterial Virulence Unit (HRYC-CSIC), Superior Council of Scientific Research (CSIC), Madrid, Spain
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain
| | - José M Sempere
- Department of Information Systems and Computation (DSIC), Universitat Politècnica de València, Valencia, Spain
| | | | - Fernando Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Madrid, Spain
- Antibiotic Resistance and Bacterial Virulence Unit (HRYC-CSIC), Superior Council of Scientific Research (CSIC), Madrid, Spain
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain
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Abstract
Antibiotic resistance is recognized as one of the major challenges in public health. The global spread of antibiotic resistance is the consequence of a constant flow of information across multi-hierarchical interactions, involving cellular (clones), subcellular (resistance genes located in plasmids, transposons, and integrons), and supracellular (clonal complexes, genetic exchange communities, and microbiotic ensembles) levels. In order to study such multilevel complexity, we propose to establish an experimental epidemiology model for the transmission of antibiotic resistance with the cockroach Blatella germanica. This paper reports the results of five types of preliminary experiments with B. germanica populations that allow us to conclude that this animal is an appropriate model for experimental epidemiology: (i) the composition, transmission, and acquisition of gut microbiota and endosymbionts; (ii) the effect of different diets on gut microbiota; (iii) the effect of antibiotics on host fitness; (iv) the evaluation of the presence of antibiotic resistance genes in natural- and lab-reared populations; and (v) the preparation of plasmids harboring specific antibiotic resistance genes. The basic idea is to have populations with higher and lower antibiotic exposure, simulating the hospital and the community, respectively, and with a certain migration rate of insects between populations. In parallel, we present a computational model based on P-membrane computing that will mimic the experimental system of antibiotic resistance transmission. The proposal serves as a proof of concept for the development of more-complex population dynamics of antibiotic resistance transmission that are of interest in public health, which can help us evaluate procedures and design appropriate interventions in epidemiology.
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Affiliation(s)
- Pablo Llop
- Foundation for the Promotion of Sanitary and Biomedical Research in the Valencian Region (FISABIO), València, Spain
| | - Amparo Latorre
- Foundation for the Promotion of Sanitary and Biomedical Research in the Valencian Region (FISABIO), València, Spain
- Integrative Systems Biology Institute, Universitat de València, València, Spain
- Network Research Center for Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Andrés Moya
- Foundation for the Promotion of Sanitary and Biomedical Research in the Valencian Region (FISABIO), València, Spain
- Integrative Systems Biology Institute, Universitat de València, València, Spain
- Network Research Center for Epidemiology and Public Health (CIBERESP), Madrid, Spain
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Campos M, Llorens C, Sempere JM, Futami R, Rodriguez I, Carrasco P, Capilla R, Latorre A, Coque TM, Moya A, Baquero F. A membrane computing simulator of trans-hierarchical antibiotic resistance evolution dynamics in nested ecological compartments (ARES). Biol Direct 2015; 10:41. [PMID: 26243297 PMCID: PMC4526193 DOI: 10.1186/s13062-015-0070-9] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Accepted: 07/31/2015] [Indexed: 02/07/2023] Open
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.
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Affiliation(s)
- Marcelino Campos
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Carretera de Colmenar Viejo, km. 9,100, 28034, Madrid, Spain.
- Department of Information Systems and Computation (DSIC), Polytechnic University of Valencia, Camino de Vera, 46022, Valencia, Spain.
| | - Carlos Llorens
- Biotechvana, Valencia, CEEI Building, Benjamin Franklin Av. 12, Valencia Technological Park, 46980, Paterna, Spain.
| | - José M Sempere
- Department of Information Systems and Computation (DSIC), Polytechnic University of Valencia, Camino de Vera, 46022, Valencia, Spain.
| | - Ricardo Futami
- Biotechvana, Valencia, CEEI Building, Benjamin Franklin Av. 12, Valencia Technological Park, 46980, Paterna, Spain.
| | - Irene Rodriguez
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Carretera de Colmenar Viejo, km. 9,100, 28034, Madrid, Spain.
- Antibiotic Resistance and Bacterial Virulence Unit (HRYC-CSIC), associated to the Superior Council of Scientific Investigations (CSIC), Madrid, Spain.
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain.
| | - Purificación Carrasco
- Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, C/ Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain.
| | - Rafael Capilla
- Biotechvana, Valencia, CEEI Building, Benjamin Franklin Av. 12, Valencia Technological Park, 46980, Paterna, Spain.
| | - Amparo Latorre
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain.
- Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, C/ Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain.
- Foundation for the Promotion of Health and Biomedical Research in the Valencian Community (FISABIO) - Public Health, Avenida de Cataluña 21, 46020, Valencia, Spain.
| | - Teresa M Coque
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Carretera de Colmenar Viejo, km. 9,100, 28034, Madrid, Spain.
- Antibiotic Resistance and Bacterial Virulence Unit (HRYC-CSIC), associated to the Superior Council of Scientific Investigations (CSIC), Madrid, Spain.
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain.
| | - Andres Moya
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain.
- Cavanilles Institute of Biodiversity and Evolutionary Biology, University of Valencia, C/ Catedrático José Beltrán 2, 46980, Paterna, Valencia, Spain.
- Foundation for the Promotion of Health and Biomedical Research in the Valencian Community (FISABIO) - Public Health, Avenida de Cataluña 21, 46020, Valencia, Spain.
| | - Fernando Baquero
- Department of Microbiology, Ramón y Cajal University Hospital, IRYCIS, Carretera de Colmenar Viejo, km. 9,100, 28034, Madrid, Spain.
- Antibiotic Resistance and Bacterial Virulence Unit (HRYC-CSIC), associated to the Superior Council of Scientific Investigations (CSIC), Madrid, Spain.
- Network Research Center for Epidemiology and Public Health (CIBER-ESP), Madrid, Spain.
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12
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Abstract
We present a formal calculus, termed the chemtainer calculus, able to capture the complexity of compartmentalized reaction systems such as populations of possibly nested vesicular compartments. Compartments contain molecular cargo as well as surface markers in the form of DNA single strands. These markers serve as compartment addresses and allow for their targeted transport and fusion, thereby enabling reactions of previously separated chemicals. The overall system organization allows for the set-up of programmable chemistry in microfluidic or other automated environments. We introduce a simple sequential programming language whose instructions are motivated by state-of-the-art microfluidic technology. Our approach integrates electronic control, chemical computing and material production in a unified formal framework that is able to mimic the integrated computational and constructive capabilities of the subcellular matrix. We provide a non-deterministic semantics of our programming language that enables us to analytically derive the computational and constructive power of our machinery. This semantics is used to derive the sets of all constructable chemicals and supermolecular structures that emerge from different underlying instruction sets. Because our proofs are constructive, they can be used to automatically infer control programs for the construction of target structures from a limited set of resource molecules. Finally, we present an example of our framework from the area of oligosaccharide synthesis.
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Affiliation(s)
- Harold Fellermann
- School of Computing Science, Newcastle University, King's Gate, Newcastle upon Tyne NE1 7RU, UK Center for Fundamental Living Technology, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark
| | - Luca Cardelli
- Microsoft Research Cambridge, 21 Station Road, Cambridge CB1 2FB, UK
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