Investigating biocomplexity through the agent-based paradigm.

Capturing the dynamism that pervades biological systems requires a computational approach that can accommodate both the continuous features of the system environment as well as the flexible and heterogeneous nature of component interactions. This presents a serious challenge for the more traditional mathematical approaches that assume component homogeneity to relate system observables using mathematical equations. While the homogeneity condition does not lead to loss of accuracy while simulating various continua, it fails to offer detailed solutions when applied to systems with dynamically interacting heterogeneous components. As the functionality and architecture of most biological systems is a product of multi-faceted individual interactions at the sub-system level, continuum models rarely offer much beyond qualitative similarity. Agent-based modelling is a class of algorithmic computational approaches that rely on interactions between Turing-complete finite-state machines--or agents--to simulate, from the bottom-up, macroscopic properties of a system. In recognizing the heterogeneity condition, they offer suitable ontologies to the system components being modelled, thereby succeeding where their continuum counterparts tend to struggle. Furthermore, being inherently hierarchical, they are quite amenable to coupling with other computational paradigms. The integration of any agent-based framework with continuum models is arguably the most elegant and precise way of representing biological systems. Although in its nascence, agent-based modelling has been utilized to model biological complexity across a broad range of biological scales (from cells to societies). In this article, we explore the reasons that make agent-based modelling the most precise approach to model biological systems that tend to be non-linear and complex

Ensemble methods for meningitis aetiology diagnosis

In this work, we explore data-driven techniques for the fast and early diagnosis concerning the etiological origin of meningitis, more specifically with regard to differentiating between viral and bacterial meningitis. We study how machine learning can be used to predict meningitis aetiology once a patient has been diagnosed with this disease. We have a dataset of 26,228 patients described by 19 attributes, mainly about the patient's observable symptoms and the early results of the cerebrospinal fluid analysis. Using this dataset, we have explored several techniques of dataset sampling, feature selection and classification models based both on ensemble methods and on simple techniques (mainly, decision trees). Experiments with 27 classification models (19 of them involving ensemble methods) have been conducted for this paper. Our main finding is that the combination of ensemble methods with decision trees leads to the best meningitis aetiology classifiers. The best performance indicator values (precision, recall and f-measure of 89% and an AUC value of 95%) have been achieved by the synergy between bagging and NBTrees. Nonetheless, our results also suggest that the combination of ensemble methods with certain decision tree clearly improves the performance of diagnosis in comparison with those obtained with only the corresponding decision tree.This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. We would like to thank the Health Department of the Brazilian Government for providing the dataset and for authorizing its use in this study. We would also like to express our gratitude to the reviewers for their thoughtful comments and efforts towards improving our manuscript. Funding for open access charge: Universidad de Málaga / CBUA

Host-Pathogen O-Methyltransferase Similarity and Its Specific Presence in Highly Virulent Strains of Francisella tularensis Suggests Molecular Mimicry

Whole genome comparative studies of many bacterial pathogens have shown an overall high similarity of gene content (>95%) between phylogenetically distinct subspecies. In highly clonal species that share the bulk of their genomes subtle changes in gene content and small-scale polymorphisms, especially those that may alter gene expression and protein-protein interactions, are more likely to have a significant effect on the pathogen's biology. In order to better understand molecular attributes that may mediate the adaptation of virulence in infectious bacteria, a comparative study was done to further analyze the evolution of a gene encoding an o-methyltransferase that was previously identified as a candidate virulence factor due to its conservation specifically in highly pathogenic Francisella tularensis subsp. tularensis strains. The o-methyltransferase gene is located in the genomic neighborhood of a known pathogenicity island and predicted site of rearrangement. Distinct o-methyltransferase subtypes are present in different Francisella tularensis subspecies. Related protein families were identified in several host species as well as species of pathogenic bacteria that are otherwise very distant phylogenetically from Francisella, including species of Mycobacterium. A conserved sequence motif profile is present in the mammalian host and pathogen protein sequences, and sites of non-synonymous variation conserved in Francisella subspecies specific o-methyltransferases map proximally to the predicted active site of the orthologous human protein structure. Altogether, evidence suggests a role of the F. t. subsp. tularensis protein in a mechanism of molecular mimicry, similar perhaps to Legionella and Coxiella. These findings therefore provide insights into the evolution of niche-restriction and virulence in Francisella, and have broader implications regarding the molecular mechanisms that mediate host-pathogen relationships

Hidden Markov Models for Gene Sequence Classification: Classifying the VSG genes in the Trypanosoma brucei Genome

The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host's immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.Comment: Accepted article in July, 2015 in Pattern Analysis and Applications, Springer. The article contains 23 pages, 4 figures, 8 tables and 51 reference

Promoting Appropriate Use of Drugs in Children

Promotion of appropriate and safe drugs in children is the need of the hour globally. Pediatric population by itself is a spectrum of different physiologies with significant variation in pharmacodynamics and pharmacokinetics. Unfortunately, 50–90% of drugs used in children today have never been actually studied in this population, and the results of drug studies done in adults are often extrapolated for use in children. Many medicines in pediatrics are off label or unlicensed. There is a spurt in drug resistance due to the overzealous prescription of antimicrobials not indicated, such as, using inadequate dosage or duration of drug regime leading to partially treated infections, using the wrong antimicrobial due to ignorance of causative organism, and finally using indigenous, irrational combinations. Availability of properly labeled and safe pediatric formulations, regular audit by pharmacists, judicious prescriptions, proper counseling about drug administration, surveillance of adverse effects, and pediatric drug trials can be the best possible interventions to offer appropriate medicines to children and thereby save millions of lives

Developmental roadmap for antimicrobial susceptibility testing systems

Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability, optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions

Developmental roadmap for antimicrobial susceptibility testing systems

Antimicrobial susceptibility testing (AST) technologies help to accelerate the initiation of targeted antimicrobial therapy for patients with infections and could potentially extend the lifespan of current narrow-spectrum antimicrobials. Although conceptually new and rapid AST technologies have been described, including new phenotyping methods, digital imaging and genomic approaches, there is no single major, or broadly accepted, technological breakthrough that leads the field of rapid AST platform development. This might be owing to several barriers that prevent the timely development and implementation of novel and rapid AST platforms in health-care settings. In this Consensus Statement, we explore such barriers, which include the utility of new methods, the complex process of validating new technology against reference methods beyond the proof-of-concept phase, the legal and regulatory landscapes, costs, the uptake of new tools, reagent stability, optimization of target product profiles, difficulties conducting clinical trials and issues relating to quality and quality control, and present possible solutions

Virus-like particles as a novel platform for delivery of protective Burkholderia antigens

A thesis by Marc Ashley Bayliss entitled ‘Virus-like particles as a novel platform for delivery of protective Burkholderia antigens’ and submitted to the University of Exeter for the degree of Doctor of Philosophy. There is currently no licensed vaccine available for the global tropical pathogen Burkholderia pseudomallei which is the causative agent of melioidosis and a potential bio-threat agent. The capsule polysaccharide (CPS) expressed by B. pseudomallei has been shown to offer some protection against bacterial challenge. Polysaccharide immunogenicity can be enhanced by conjugation to a carrier protein and several licensed vaccines utilise this technology. Virus-like particles (VLPs) are non-infectious, non-replicating, viral proteins that self-assemble into viral structures and are in several licensed vaccines as primary antigens. VLPs are also effective delivery platforms for foreign antigens by genetic insertion or chemical conjugation. iQur, a collaborator on this project, has developed Tandem CoreTM that consists of two genetically linked hepatitis B core proteins that allow insertion of large proteins into each core whilst remaining assembly competent. The aim of this thesis was to assess the protective efficacy of Tandem CoreTM VLPs chemically conjugated to CPS and Tandem CoreTM Burkholderia protein fusion constructs. This involved three objectives; reduce the cost of CPS extraction; identify immunogenic Burkholderia proteins; and test candidate vaccine efficacy in an animal model of acute melioidosis against B. pseudomallei challenge. To reduce the cost of extraction, CPS was purified from B. thailandensis strain E555 and bacterial culture CPS concentration optimised which first required development of a quantitative ELISA. Immunogenic Burkholderia proteins were identified from the literature but Tandem CoreTM fusion constructs containing these proteins were not assembly competent. The Burkholderia proteins were added as co-antigens to the VLP CPS conjugate vaccine but did not improve efficacy. Tandem CoreTM VLPs conjugated to CPS were protective against B. pseudomallei challenge and were compared to CPS conjugated to Crm197: a commercially available carrier protein used in several licensed vaccines. At lower challenge doses, survival was greater in mice vaccinated with the VLP-CPS conjugate although at higher doses, Crm197-CPS efficacy was greater.Defense Threat Reduction Agenc

Genomic sequence analysis and characterization of Sneathia amnii sp. nov

Background Bacteria of the genus Sneathia are emerging as potential pathogens of the female reproductive tract. Species of Sneathia, which were formerly grouped with Leptotrichia, can be part of the normal microbiota of the genitourinary tracts of men and women, but they are also associated with a variety of clinical conditions including bacterial vaginosis, preeclampsia, preterm labor, spontaneous abortion, post-partum bacteremia and other invasive infections. Sneathia species also exhibit a significant correlation with sexually transmitted diseases and cervical cancer. BecauseSneathia species are fastidious and rarely cultured successfully in vitro; and the genomes of members of the genus had until now not been characterized, very little is known about the physiology or the virulence of these organisms. Results Here, we describe a novel species, Sneathia amnii sp. nov, which closely resembles bacteria previously designated Leptotrichia amnionii . As part of the Vaginal Human Microbiome Project at VCU, a vaginal isolate of S. amnii sp. nov. was identified, successfully cultured and bacteriologically cloned. The biochemical characteristics and virulence properties of the organism were examined in vitro, and the genome of the organism was sequenced, annotated and analyzed. The analysis revealed a reduced circular genome of ~1.34 Mbp, containing ~1,282 protein-coding genes. Metabolic reconstruction of the bacterium reflected its biochemical phenotype, and several genes potentially associated with pathogenicity were identified. Conclusions Bacteria with complex growth requirements frequently remain poorly characterized and, as a consequence, their roles in health and disease are unclear. Elucidation of the physiology and identification of genes putatively involved in the metabolism and virulence of S. amnii may lead to a better understanding of the role of this potential pathogen in bacterial vaginosis, preterm birth, and other issues associated with vaginal and reproductive health