A multilayered graph-based framework to explore behavioural phenomena in social media conversations.

OBJECTIVE: Social media is part of current health communications. This research aims to delve into the effects of social contagion, biased assimilation, and homophily in building and changing health opinions on social media. MATERIALS AND METHODS: Conversations about COVID-19 vaccination on English and Spanish Twitter are the case studies. A new multilayered graph-based framework supports the integrated analysis of content similarity within and across posts, users, and conversations to interpret contrasting and confluent user stances. Deep learning models are applied to infer stance. Graph centrality and homophily scores support the interpretation of information reproduction. RESULTS: The results show that semantically related English posts tend to present a similar stance about COVID-19 vaccination (rstance = 0.51) whereas Spanish posts are more heterophilic (rstance = 0.38). Neither case showed evidence of homophily regarding user influence or vaccine hashtags. Graph filters for Pfizer and Astrazeneca with a similarity threshold of 0.85 show stance homophily in English scenarios (i.e. rstance = 0.45 and rstance = 0.58, respectively) and small homophily in Spanish scenarios (i.e. r = 0.12 and r = 0.3, respectively). Highly connected users are a minority and are not socially influential. Spanish conversations showed stance homophily, i.e. most of the connected conversations promote vaccination (rstance = 0.42), whereas English conversations are more likely to offer contrasting stances. CONCLUSION: The methodology proposed for quantifying the impact of natural and intentional social behaviours in health information reproduction can be applied to any of the main social platforms and any given topic of conversation. Its effectiveness was demonstrated by two case studies describing English and Spanish demographic and sociocultural scenarios.

Targeted depletion of pks+ bacteria from a fecal microbiota using specific antibodies.

The pks island is one of the most prevalent pathogenicity islands among the Escherichia coli strains that colonize the colon of colorectal carcinoma (CRC) patients. This pathogenic island encodes the production of a nonribosomal polyketide-peptide named colibactin, which induces double-strand breaks in DNA molecules. Detection or even depletion of this pks-producing bacteria could help to understand the role of these strains in the context of CRC. In this work, we performed a large-scale in silico screening of the pks cluster in more than 6,000 isolates of E. coli. The results obtained reveal that not all the pks-detected strains could produce a functional genotoxin and, using antibodies against pks-specific peptides from surface cell proteins, a methodology for detection and depletion of pks+ bacteria in gut microbiotas was proposed. With our method, we were able to deplete a human gut microbiota of this pks+ strains, opening the door to strain-directed microbiota modification and intervention studies that allow us to understand the relation between these genotoxic strains and some gastrointestinal diseases. IMPORTANCE The human gut microbiome has also been hypothesized to play a crucial role in the development and progression of colorectal carcinoma (CRC). Between the microorganisms of this community, the Escherichia coli strains carrying the pks genomic island were shown to be capable of promoting colon tumorigenesis in a colorectal cancer mouse model, and their presence seems to be directly related to a distinct mutational signature in patients suffering CRC. This work proposes a novel method for the detection and depletion of pks-carrying bacteria in human gut microbiotas. In contrast to methods based on probes, this methodology allows the depletion of low-abundance bacterial strains maintaining the viability of both targeted and non-targeted fractions of the microbiota, allowing the study of the contribution of these pks-carrying strains to different diseases, such as CRC, and their role in other physiological, metabolic or immune processes.

Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations.

This paper proposes a new deep learning approach to better understand how optimistic and pessimistic feelings are conveyed in Twitter conversations about COVID-19. A pre-trained transformer embedding is used to extract the semantic features and several network architectures are compared. Model performance is evaluated on two new, publicly available Twitter corpora of crisis-related posts. The best performing pessimism and optimism detection models are based on bidirectional long- and short-term memory networks. Experimental results on four periods of the COVID-19 pandemic show how the proposed approach can model optimism and pessimism in the context of a health crisis. There is a total of 150,503 tweets and 51,319 unique users. Conversations are characterised in terms of emotional signals and shifts to unravel empathy and support mechanisms. Conversations with stronger pessimistic signals denoted little emotional shift (i.e. 62.21% of these conversations experienced almost no change in emotion). In turn, only 10.42% of the conversations laying more on the optimistic side maintained the mood. User emotional volatility is further linked with social influence.

A framework to extract biomedical knowledge from gluten-related tweets: The case of dietary concerns in digital era.

Big data importance and potential are becoming more and more relevant nowadays, enhanced by the explosive growth of information volume that is being generated on the Internet in the last years. In this sense, many experts agree that social media networks are one of the internet areas with higher growth in recent years and one of the fields that are expected to have a more significant increment in the coming years. Similarly, social media sites are quickly becoming one of the most popular platforms to discuss health issues and exchange social support with others. In this context, this work presents a new methodology to process, classify, visualise and analyse the big data knowledge produced by the sociome on social media platforms. This work proposes a methodology that combines natural language processing techniques, ontology-based named entity recognition methods, machine learning algorithms and graph mining techniques to: (i) reduce the irrelevant messages by identifying and focusing the analysis only on individuals and patient experiences from the public discussion; (ii) reduce the lexical noise produced by the different ways in how users express themselves through the use of domain ontologies; (iii) infer the demographic data of the individuals through the combined analysis of textual, geographical and visual profile information; (iv) perform a community detection and evaluate the health topic study combining the semantic processing of the public discourse with knowledge graph representation techniques; and (v) gain information about the shared resources combining the social media statistics with the semantical analysis of the web contents. The practical relevance of the proposed methodology has been proven in the study of 1.1 million unique messages from >400,000 distinct users related to one of the most popular dietary fads that evolve into a multibillion-dollar industry, i.e., gluten-free food. Besides, this work analysed one of the least research fields studied on Twitter concerning public health (i.e., the allergies or immunology diseases as celiac disease), discovering a wide range of health-related conclusions.

Computational Resources and Strategies to Construct Single-Molecule Models of FISH.

Currently, the interactions occurring between oligonucleotides and the cellular envelope of bacteria are not fully resolved at the molecular level. Understanding these interactions is essential to gain insights on how to improve the internalization of the tagged oligonucleotides during fluorescence in situ hybridization (FISH). Agent-based modeling (ABM) is a promising in silico tool to dynamically simulate FISH and bring forward new knowledge on this process. Notably, it is important to simulate the whole bacterial cell, including the different layers of the cell envelope, given that the oligonucleotide must cross the envelope to reach its target in the cytosol. In addition, it is also important to characterize other molecules in the cell to best emulate the cell and represent molecular crowding. Here, we review the main information that should be compiled to construct an ABM on FISH and provide a practical example of an oligonucleotide targeting the 23S rRNA of Escherichia coli .

Computational Approach to the Systematic Prediction of Glycolytic Abilities: Looking Into Human Microbiota.

Glycoside hydrolases are responsible for the enzymatic deconstruction of complex carbohydrates. Most of the families are known to conserve the catalytic machinery and molecular mechanisms. This work introduces a new method to predict glycolytic abilities in sequenced genomes and thus, gain a better understanding of how to target specific carbohydrates and identify potentially interesting sources of specialised enzymes. Genome sequences are aligned to those of organisms with expertly curated glycolytic abilities. Clustering of homology scores helps identify organisms that share common abilities and the most promising organisms regarding specific glycolytic abilities. The method has been applied to members of the bacterial families Ruminococcaceae (39 genera), Eubacteriaceae (11 genera) and Lachnospiraceae (59 genera), which hold major representatives of the human gut microbiota. The method predicted the potential presence of glycoside hydrolases in 1701 species of these genera. Here, the validity and practical usefulness of the method is discussed based on the predictions obtained for members of the genus Ruminococcus. Results were consistent with existing literature and offer useful, complementary insights to comparative genomics and physiological testing. The implementation of the Gleukos web portal (http://sing-group.org/gleukos) offers a public service to those interested in targeting microbial carbohydrate metabolism for biotechnological and health applications.

Computational resources and strategies to assess single-molecule dynamics of the translation process in S. cerevisiae.

This work provides a systematic and comprehensive overview of available resources for the molecular-scale modelling of the translation process through agent-based modelling. The case study is the translation in Saccharomyces cerevisiae, one of the most studied yeasts. The data curation workflow encompassed structural information about the yeast (i.e. the simulation environment), and the proteins, ribonucleic acids and other types of molecules involved in the process (i.e. the agents). Moreover, it covers the main process events, such as diffusion (i.e. motion of molecules in the environment) and collision efficiency (i.e. interaction between molecules). Data previously determined by wet-lab techniques were preferred, resorting to computational predictions/extrapolations only when strictly necessary. The computational modelling of the translation processes is of added industrial interest, since it may bring forward knowledge on how to control such phenomena and enhance the production of proteins of interest in a faster and more efficient manner.

Revisiting the Metabolic Capabilities of Bifidobacterium longum susbp. longum and Bifidobacterium longum subsp. infantis from a Glycoside Hydrolase Perspective.

Bifidobacteria are among the most abundant microorganisms inhabiting the intestine of humans and many animals. Within the genus Bifidobacterium, several beneficial effects have been attributed to strains belonging to the subspecies Bifidobacterium longum subsp. longum and Bifidobacterium longum subsp. infantis, which are often found in infants and adults. The increasing numbers of sequenced genomes belonging to these two subspecies, and the availability of novel computational tools focused on predicting glycolytic abilities, with the aim of understanding the capabilities of degrading specific carbohydrates, allowed us to depict the potential glycoside hydrolases (GH) of these bacteria, with a focus on those GH profiles that differ in the two subspecies. We performed an in silico examination of 188 sequenced B. longum genomes and depicted the commonly present and strain-specific GHs and GH families among representatives of this species. Additionally, GH profiling, genome-based and 16S rRNA-based clustering analyses showed that the subspecies assignment of some strains does not properly match with their genetic background. Furthermore, the analysis of the potential GH component allowed the distinction of clear GH patterns. Some of the GH activities, and their link with the two subspecies under study, are further discussed. Overall, our in silico analysis poses some questions about the suitability of considering the GH activities of B. longum subsp. longum and B. longum subsp. infantis to gain insight into the characterization and classification of these two subspecies with probiotic interest.

Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics.

BACKGROUND: Nowadays, the use of social media is part of daily life, with more and more people, including governments and health organizations, using at least one platform regularly. Social media enables users to interact among large groups of people that share the same interests and suffer the same afflictions. Notably, these channels promote the ability to find and share information about health and medical conditions. OBJECTIVE: This study aimed to characterize the bowel disease (BD) community on Twitter, in particular how patients understand, discuss, feel, and react to the condition. The main questions were as follows: Which are the main communities and most influential users?; Where are the main content providers from?; What are the key biomedical and scientific topics under discussion? How are topics interrelated in patient communications?; How do external events influence user activity?; What kind of external sources of information are being promoted? METHODS: To answer these questions, a dataset of tweets containing terms related to BD conditions was collected from February to August 2018, accounting for a total of 24,634 tweets from 13,295 different users. Tweet preprocessing entailed the extraction of textual contents, hyperlinks, hashtags, time, location, and user information. Missing and incomplete information about the user profiles was completed using different analysis techniques. Semantic tweet topic analysis was supported by a lexicon-based entity recognizer. Furthermore, sentiment analysis enabled a closer look into the opinions expressed in the tweets, namely, gaining a deeper understanding of patients' feelings and experiences. RESULTS: Health organizations received most of the communication, whereas BD patients and experts in bowel conditions and nutrition were among those tweeting the most. In general, the BD community was mainly discussing symptoms, BD-related diseases, and diet-based treatments. Diarrhea and constipation were the most commonly mentioned symptoms, and cancer, anxiety disorder, depression, and chronic inflammations were frequently part of BD-related tweets. Most patient tweets discussed the bad side of BD conditions and other related conditions, namely, depression, diarrhea, and fibromyalgia. In turn, gluten-free diets and probiotic supplements were often mentioned in patient tweets expressing positive emotions. However, for the most part, tweets containing mentions to foods and diets showed a similar distribution of negative and positive sentiments because the effects of certain food components (eg, fiber, iron, and magnesium) were perceived differently, depending on the state of the disease and other personal conditions of the patients. The benefits of medical cannabis for the treatment of different chronic diseases were also highlighted. CONCLUSIONS: This study evidences that Twitter is becoming an influential space for conversation about bowel conditions, namely, patient opinions about associated symptoms and treatments. So, further qualitative and quantitative content analyses hold the potential to support decision making among health-related stakeholders, including the planning of awareness campaigns.

Next generation community assessment of biomedical entity recognition web servers: metrics, performance, interoperability aspects of BeCalm.

BACKGROUND: Shared tasks and community challenges represent key instruments to promote research, collaboration and determine the state of the art of biomedical and chemical text mining technologies. Traditionally, such tasks relied on the comparison of automatically generated results against a so-called Gold Standard dataset of manually labelled textual data, regardless of efficiency and robustness of the underlying implementations. Due to the rapid growth of unstructured data collections, including patent databases and particularly the scientific literature, there is a pressing need to generate, assess and expose robust big data text mining solutions to semantically enrich documents in real time. To address this pressing need, a novel track called "Technical interoperability and performance of annotation servers" was launched under the umbrella of the BioCreative text mining evaluation effort. The aim of this track was to enable the continuous assessment of technical aspects of text annotation web servers, specifically of online biomedical named entity recognition systems of interest for medicinal chemistry applications. RESULTS: A total of 15 out of 26 registered teams successfully implemented online annotation servers. They returned predictions during a two-month period in predefined formats and were evaluated through the BeCalm evaluation platform, specifically developed for this track. The track encompassed three levels of evaluation, i.e. data format considerations, technical metrics and functional specifications. Participating annotation servers were implemented in seven different programming languages and covered 12 general entity types. The continuous evaluation of server responses accounted for testing periods of low activity and moderate to high activity, encompassing overall 4,092,502 requests from three different document provider settings. The median response time was below 3.74 s, with a median of 10 annotations/document. Most of the servers showed great reliability and stability, being able to process over 100,000 requests in a 5-day period. CONCLUSIONS: The presented track was a novel experimental task that systematically evaluated the technical performance aspects of online entity recognition systems. It raised the interest of a significant number of participants. Future editions of the competition will address the ability to process documents in bulk as well as to annotate full-text documents.

In silico Approach for Unveiling the Glycoside Hydrolase Activities in Faecalibacterium prausnitzii Through a Systematic and Integrative Large-Scale Analysis.

This work presents a novel in silico approach to the prediction and characterization of the glycolytic capacities of the beneficial intestinal bacterium Faecalibacterium prausnitzii. Available F. prausnitzii genomes were explored taking the glycolytic capacities of F. prausnitzii SL3/3 and F. prausnitzii L2-6 as reference. The comparison of the generated glycolytic profiles offered insights into the particular capabilities of F. prausnitzii SL3/3 and F. prausnitzii L2-6 as well as the potential of the rest of strains. Glycoside hydrolases were mostly detected in the pathways responsible for the starch and sucrose metabolism and the biosynthesis of secondary metabolites, but this analysis also identified some other potentially interesting, but still uncharacterized activities, such as several hexosyltransferases and some hydrolases. Gene neighborhood maps offered additional understanding of the genes coding for relevant glycoside hydrolases. Although information about the carbohydrate preferences of F. prausnitzii is scarce, the in silico metabolic predictions were consistent with previous knowledge about the impact of fermentable sugars on the growth promotion and metabolism of F. prausnitzii. So, while the predictions still need to be validated using culturing methods, the approach holds the potential to be reproduced and scaled to accommodate the analysis of other strains (or even families and genus) as well as other metabolic activities. This will allow the exploration of novel methodologies to design or obtain targeted probiotics for F. prausnitzii and other strains of interest.

Application of agent-based modelling to assess single-molecule transport across the cell envelope of E. coli.

MOTIVATION: Single cells often show stochastic behaviour and variations in the physiological state of individual cells affect the behaviour observed in cell populations. This may be partially explained by variations in the concentration and spatial location of molecules within and in the vicinity of each cell. METHODS: This paper introduces an agent-based model that represents single-molecule transport through the cellular envelope of Escherichia coli at the micrometre scale. This model enables broader observation of molecular transport throughout the different membrane layers and the study of the effect of molecular concentration in cellular noise. Simulations considered various low molecular weight molecules, i.e. ampicillin, bosentan, coumarin, saquinavir, and terbutaline, and a gradient of molecular concentrations. The model ensured stochasticity in the location of the agents, using diffusing spherical particles with physical dimensions. RESULTS: Simulation results were validated against theoretical and experimental data. For example, theoretically, ampicillin molecules take 0.6 s to cross the entire cell envelope, and computational simulations took 0.68 s, 0.68 s, 0.70 s, and 0.69 s, for concentrations of 1.44 µM, 13.21 µM, 26.4 µM and 105.61 µM, respectively. Replicate standard deviation decreased with growing initial concentrations of the molecules. In turn, no clear relationship could be observed between molecular size and variability. CONCLUSIONS: This work presented a novel agent-based model to study the effect of the initial concentration of low molecular weight molecules on cellular noise. Cellular noise during molecule diffusion was found to be concentration-dependent and size-independent. The new model holds considerable potential for future, more complex analyses, when emerging experimental data may enable modelling of membrane transport mechanisms.

DEWE: A novel tool for executing differential expression RNA-Seq workflows in biomedical research.

BACKGROUND: Transcriptomics profiling aims to identify and quantify all transcripts present within a cell type or tissue at a particular state, and thus provide information on the genes expressed in specific experimental settings, differentiation or disease conditions. RNA-Seq technology is becoming the standard approach for such studies, but available analysis tools are often hard to install, configure and use by users without advanced bioinformatics skills. METHODS: Within reason, DEWE aims to make RNA-Seq analysis as easy for non-proficient users as for experienced bioinformaticians. DEWE supports two well-established and widely used differential expression analysis workflows: using Bowtie2 or HISAT2 for sequence alignment; and, both applying StringTie for quantification, and Ballgown and edgeR for differential expression analysis. Also, it enables the tailored execution of individual tools as well as helps with the management and visualisation of differential expression results. RESULTS: DEWE provides a user-friendly interface designed to reduce the learning curve of less knowledgeable users while enabling analysis customisation and software extension by advanced users. Docker technology helps overcome installation and configuration hurdles. In addition, DEWE produces high quality and publication-ready outputs in the form of tab-delimited files and figures, as well as helps researchers with further analyses, such as pathway enrichment analysis. CONCLUSIONS: The abilities of DEWE are exemplified here by practical application to a comparative analysis of monocytes and monocyte-derived dendritic cells, a study of clinical relevance. DEWE installers and documentation are freely available at https://www.sing-group.org/dewe.

In silico prediction reveals the existence of potential bioactive neuropeptides produced by the human gut microbiota.

This work reports on a large-scale potential neuropeptide activity screening in human gut microbiomes deposited in public databases. In our experimental approach, the sequences of the bioactive peptides collected in the MAHMI database, mainly predicted as immunomodulatory or antitumoral, were crossed with those of the neuroactive/digestive peptides. From 91,325,790 potential bioactive peptides, only 581 returned a match when crossed against the 5949 neuroactive peptides from the NeuroPep database and the 15 digestive hormones. Relevant bacterial taxa, such as Ruminococcus sp., Clostridium sp. were found among the main producers of the matching sequences, and many of the matches corresponded to adiponectin and the hormone produced by adipocites, which is involved in glucose homeostasis. These results show, for the first time, the presence of potentially bioactive peptides produced by gut microbiota members over the nervous cells, most notably, peptides with already predicted immunomodulatory or anti-inflammatory activity. Classical (Lactobacillus sp.) and next-generation (Faecalibacterium sp.) probiotics are shown to produce these peptides, which are proposed as a potential mechanism of action of psychobiotics. Our previous experimental results showed that many of these peptides were active when incubated with immune cells, such as dendritic cells, so their effect over the nervous system innervating the gut mucosa holds significant potential and should be explored.

Computational prediction of the bioactivity potential of proteomes based on expert knowledge.

Advances in the field of genome sequencing have enabled a comprehensive analysis and annotation of the dynamics of the protein inventory of cells. This has been proven particularly rewarding for microbial cells, for which the majority of proteins are already accessible to analysis through automatic metagenome annotation. The large-scale in silico screening of proteomes and metaproteomes is key to uncover bioactivities of translational, clinical and biotechnological interest, and to help assign functions to certain proteins, such as those predicted as hypothetical. This work introduces a new method for the prediction of the bioactivity potential of proteomes/metaproteomes, supporting the discovery of functionally relevant proteins based on prior knowledge. This methodology complements functional annotation enrichment methods by allowing the assignment of functions to proteins annotated as hypothetical/putative/uncharacterised, as well as and enabling the detection of specific bioactivities and the recovery of proteins from defined taxa. This work shows how the new method can be applied to screen proteome and metaproteome sets to obtain predictions of clinical or biotechnological interest based on reference datasets. Notably, with this methodology, the large information files obtained after DNA sequencing or protein identification experiments can be associated for translational purposes that, in cases such as antibiotic-resistance pathogens or foodborne diseases, may represent changes in how these important and global health burdens are approached in the clinical practice. Finally, the Sequence-based Expert-driven pRoteome bioactivity Prediction EnvironmENT, a public Web service implemented in Scala functional programming style, is introduced as means to ensure broad access to the method as well as to discuss main implementation issues, such as modularity, extensibility and interoperability.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota.

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

BlasterJS: A novel interactive JavaScript visualisation component for BLAST alignment results.

BACKGROUND: The wide range of potential applications has made the Basic Local Alignment Search Tool (BLAST) a ubiquitous tool in the field of Molecular Biology. Within this context, it is increasingly appealing to embed BLAST services within larger Web applications. RESULTS: This work introduces BlasterJS viewer, a new JavaScript library for the lightweight development of Web-based applications supporting the visualisation of BLAST outputs. BlasterJS detaches from similar data viewers by focusing on the visual and interactive display of sequence similarity results and being completely independent of BLAST services. BlasterJS is compatible with the text outputs generated by the BLAST family of programs, namely BLASTp, BLASTn, BLASTx, tBLASTn, and tBLASTx, and works in all major Web browsers. Furthermore, BlasterJS is available through the EBI's BioJS registry 5, which extends its potential use to a wider scope of bioinformatics applications. CONCLUSIONS: BlasterJS is new Javascript library that enables easy and seamless integration of visual and interactive representations of BLAST outputs in Web-based applications supporting sequence similarity search. BlasterJS is free accessible at http://sing-group.org/blasterjs/.

The Evolving Role of Information Technology in Haemovigilance Systems.

This work provides an overview and appraisal of the general evolution of IS/IT in haemovigilance, from which lessons can be learned for its future strategic management. An electronic survey was conducted among the members of the International Haemovigilance Network to compile information on the mechanisms implemented to gather, process, validate, and store these data, to monitor haemovigilance activity, and to produce analytical reports. Survey responses were analysed by means of descriptive statistics, and comments/observations were considered in the final discussion. The answers received from 23 haemovigilance organizations show a direct relationship between the number of collected notifications (i.e., communication of adverse effects and events) and the technical specifications of the haemovigilance system in use. Notably, IT is used in the notification reception of 17 of these systems, out of which 8 systems are exclusively based on Web solutions. Most assessments of the evolution of IS/IT tend to focus on the scalability and flexibility of data gathering and reporting, considering the ever-changing requirements of haemovigilance. Data validation is poorly implemented, and data reporting has not reached its full potential. Web-based solutions are seen as the most intuitive and flexible for a system-user interaction.

Exploring anti-quorum sensing and anti-virulence based strategies to fight Candida albicans infections: an in silico approach.

The complex virulence attributes of Candida albicans are an attractive target to exploit in the development of new antifungals and anti-virulence strategies to combat C. albicans infections. Particularly, quorum sensing (QS) has been reported as critical for virulence regulation in C. albicans. This work presents two knowledge networks with up-to-date information about QS regulation and experimentally tested anti-QS and anti-virulence agents for C. albicans. A semi-automatic bioinformatics workflow that combines literature mining and expert curation was used to retrieve otherwise scattered information from the scientific literature. The network representation offers an innovative and continuously updatable means for the Candida research community to query QS and virulence data systematically and in a user-friendly way. Notably, the reconstructed networks show the complexity of QS regulation and the impact that some molecules have on the inhibition of virulence mechanisms responsible for infection establishment (e.g. hyphal development) and perseverance (e.g. biofilm formation). In the future, the compiled knowledge may be used to build decision-making models that help infer new knowledge of practical significance. The knowledge networks are publicly available at http://pcquorum.org/. This Web platform enables the exploration of fungal virulence cues as well as reported inhibitors in a user-friendly fashion.

Agent-based model of diffusion of N-acyl homoserine lactones in a multicellular environment of Pseudomonas aeruginosa and Candida albicans.

Experimental incapacity to track microbe-microbe interactions in structures like biofilms, and the complexity inherent to the mathematical modelling of those interactions, raises the need for feasible, alternative modelling approaches. This work proposes an agent-based representation of the diffusion of N-acyl homoserine lactones (AHL) in a multicellular environment formed by Pseudomonas aeruginosa and Candida albicans. Depending on the spatial location, C. albicans cells were variably exposed to AHLs, an observation that might help explain why phenotypic switching of individual cells in biofilms occurred at different time points. The simulation and algebraic results were similar for simpler scenarios, although some statistical differences could be observed (p < 0.05). The model was also successfully applied to a more complex scenario representing a small multicellular environment containing C. albicans and P. aeruginosa cells encased in a 3-D matrix. Further development of this model may help create a predictive tool to depict biofilm heterogeneity at the single-cell level.