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

Simulating the impact of non-pharmaceutical interventions limiting transmission in COVID-19 epidemics using a membrane computing model

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.

Antibiotic resistance: Time of synthesis in a post-genomic age

Antibiotic resistance has been highlighted by international organizations, including World Health Organization, World Bank and United Nations, as one of the most relevant global health problems. Classical approaches to study this problem have focused in infected humans, mainly at hospitals. Nevertheless, antibiotic resistance can expand through different ecosystems and geographical allocations, hence constituting a One-Health, Global-Health problem, requiring specific integrative analytic tools. Antibiotic resistance evolution and transmission are multilayer, hierarchically organized processes with several elements (from genes to the whole microbiome) involved. However, their study has been traditionally gene-centric, each element independently studied. The development of robust-economically affordable whole genome sequencing approaches, as well as other -omic techniques as transcriptomics and proteomics, is changing this panorama. These technologies allow the description of a system, either a cell or a microbiome as a whole, overcoming the problems associated with gene-centric approaches. We are currently at the time of combining the information derived from -omic studies to have a more holistic view of the evolution and spread of antibiotic resistance. This synthesis process requires the accurate integration of -omic information into computational models that serve to analyse the causes and the consequences of acquiring AR, fed by curated databases capable of identifying the elements involved in the acquisition of resistance. In this review, we analyse the capacities and drawbacks of the tools that are currently in use for the global analysis of AR, aiming to identify the more useful targets for effective corrective interventions.

P systems in the time of COVID-19

In this paper, we present LOIMOS, which is an epidemiological scenario simulator developed in the context of the fight against the pandemic caused by coronavirus SARS-CoV-2 on a global scale. LOIMOS has been fully developed under the paradigm of membrane computing using transition P systems with communication rules, active membranes and a stochastic simulator engine. In this paper we detail the main components of the system and we report some examples of epidemiological scenarios evaluated with LOIMOS.

The ecology of plasmid-coded antibiotic resistance: a basic framework for experimental research and modeling

Many antibiotic resistance genes are associated with plasmids. The ecological success of these mobile genetic elements within microbial communities depends on varying mechanisms to secure their own propagation, not only on environmental selection. Among the most important are the cost of plasmids and their ability to be transferred to new hosts through mechanisms such as conjugation. These are regulated by dynamic control systems of the conjugation machinery and genetic adaptations that plasmid-host pairs can acquire in coevolution. However, in complex communities, these processes and mechanisms are subject to a variety of interactions with other bacterial species and other plasmid types. This article summarizes basic plasmid properties and ecological principles particularly important for understanding the persistence of plasmid-coded antibiotic resistance in aquatic environments. Through selected examples, it further introduces to the features of different types of simulation models such as systems of ordinary differential equations and individual-based models, which are considered to be important tools to understand these complex systems. This ecological perspective aims to improve the way we study and understand the dynamics, diversity and persistence of plasmids and associated antibiotic resistance genes.

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

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.

Networks Consolidate the Core Concepts of Evolution by Natural Selection

Microbiology has unraveled rich evidence of ongoing reticulate evolutionary processes and complex interactions both within and between cells. These phenomena feature real biological networks, which can logically be analyzed using network-based tools. It is thus not surprising that network sciences, a field independent from evolutionary biology and microbiology, have recently pervasively infused their methods into both fields. Importantly, network tools bring forward observations enhancing the understanding of three core evolutionary concepts: variation, fitness, and heredity. Consequently, our work shows how network sciences can enhance evolutionary theory by explaining the evolution by natural selection of a broad diversity of units of selection, while updating the popular figure of Darwin's tree of life with a comprehensive sketch of the networks of evolution.

Causality in Biological Transmission: Forces and Energies

Transmission is a basic process in biology that can be analyzed in accordance with information theory. A sender or transmitter located in a particular patch of space is the source of the transmitted object, the message. A receiver patch interacts to receive the message. The "messages" that are transmitted between patches (eventually located in different hierarchical biological levels) are "meaningful" biological entities (biosemiotics). cis-acting transmission occurs when unenclosed patches acting as emitter and receiver entities of the same hierarchical level are linked (frequently by a vehicle) across an unfit space; trans-acting transmission occurs between biological individuals of different hierarchical levels, embedded within a close external common limit. To understand the causal frame of transmission events, we analyze the ultimate, but most importantly also the proximate, causes of transmission. These include the repelling, centrifugal "forces" influencing the transmission (emigration) and the attractive, centripetal "energies" involved in the reception (immigration). As transmission is a key process in evolution, creating both genetic-embedded complexity-diversity (trans-acting transmission, as introgression), and exposure to novel and alternative patches-environments (cis-acting transmission, as migration), the causal frame of transmission shows the cis-evolutionary and trans-evolutionary dimensions of evolution.

Simulating Multilevel Dynamics of Antimicrobial Resistance in a Membrane Computing Model

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.

Send more data: a systematic review of mathematical models of antimicrobial resistance

Background

Antimicrobial resistance is a global health problem that demands all possible means to control it. Mathematical modelling is a valuable tool for understanding the mechanisms of AMR development and spread, and can help us to investigate and propose novel control strategies. However, it is of vital importance that mathematical models have a broad utility, which can be assured if good modelling practice is followed.

Objective

The objective of this study was to provide a comprehensive systematic review of published models of AMR development and spread. Furthermore, the study aimed to identify gaps in the knowledge required to develop useful models.

Methods

The review comprised a comprehensive literature search with 38 selected studies. Information was extracted from the selected papers using an adaptation of previously published frameworks, and was evaluated using the TRACE good modelling practice guidelines.

Results

None of the selected papers fulfilled the TRACE guidelines. We recommend that future mathematical models should: a) model the biological processes mechanistically, b) incorporate uncertainty and variability in the system using stochastic modelling, c) include a sensitivity analysis and model external and internal validation.

Conclusion

Many mathematical models of AMR development and spread exist. There is still a lack of knowledge about antimicrobial resistance, which restricts the development of useful mathematical models.

Experimental Epidemiology of Antibiotic Resistance: Looking for an Appropriate Animal Model System

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.

Transmission as a basic process in microbial biology. Lwoff Award Prize Lecture

Transmission is a basic process in biology and evolution, as it communicates different biological entities within and across hierarchical levels (from genes to holobionts) both in time and space. Vertical descent, replication, is transmission of information across generations (in the time dimension), and horizontal descent is transmission of information across compartments (in the space dimension). Transmission is essentially a communication process that can be studied by analogy of the classic information theory, based on 'emitters', 'messages' and 'receivers'. The analogy can be easily extended to the triad 'emigration', 'migration' and 'immigration'. A number of causes (forces) determine the emission, and another set of causes (energies) assures the reception. The message in fact is essentially constituted by 'meaningful' biological entities. A DNA sequence, a cell and a population have a semiotic dimension, are 'signs' that are eventually recognized (decoded) and integrated by receiver biological entities. In cis-acting or unenclosed transmission, the emitters and receivers correspond to separated entities of the same hierarchical level; in trans-acting or embedded transmission, the information flows between different, but frequently nested, hierarchical levels. The result (as in introgressive events) is constantly producing innovation and feeding natural selection, influencing also the evolution of transmission processes. This review is based on the concepts presented at the André Lwoff Award Lecture in the FEMS Microbiology Congress in Maastricht in 2015.

Countrywide dissemination of a DHA-1-type plasmid-mediated AmpC β-lactamase-producing Klebsiella pneumoniae ST11 international high-risk clone in Hungary, 2009-2013

The first plasmid-mediated AmpC β-lactamase-producing Klebsiella pneumoniae (pAmpC KP) isolate was detected in December 2009 in Hungary. Hungarian microbiological laboratories were asked to send all KP strains showing cefoxitin resistance and decreased susceptibility or resistance to any third-generation cephalosporins to the Reference Laboratories at the National Center for Epidemiology. Investigation was conducted in order to outline spatio-temporal distribution and genetic characterization of pAmpC-KP isolates in Hungary. Between December 2009 and December 2013, 312 consecutive KP clinical isolates were confirmed as producing pAmpCs. All isolates showed resistance to third-generation cephalosporins, aminoglycosides and fluoroquinolones, and 77 % were non-susceptible to at least one carbapenem. Analysis of β-lactamase genes showed blaDHA-1 in all and additionally blaCTX-M-15 in 90 % of isolates. PFGE typing revealed 12 pulsotypes; of these, KP053 (262/312) and KP070 (38/312) belonged to sequence type ST11 and comprised 96 % of the isolates. The blaDHA-1 and blaCTX-M-15 co-producing KP053/ST11 clone affected 234 patients and spread to 55 healthcare centres across Hungary during the study period. Three KP053 isolates were also resistant to colistin. In two of these, the mgrB gene was truncated by IS10R, while in the third isolate, insertional inactivation of mgrB by ISKPn14 was identified. Hungary is the first European country showing endemic spread of blaDHA-1 facilitated by the international high-risk clone ST11. The rapid countrywide spread of this multidrug-resistant clone seriously endangers Hungarian healthcare facilities and warrants strengthening of infection control practices and prudent use of carbapenems and colistin.