A clinical prediction rule to identify patients with tuberculosis at high risk for HIV co-infection

Background & Objective: Many patients presenting with tuberculosis (TB) have underlying human immunodeficiency virus (HIV) co-infection. Routine HIV testing, however, is not a component of the national TB control programme in India. We sought to derive and validate a clinical prediction rule, based on clinical and laboratory parameters, to identify patients at high risk for HIV co-infection among those treated for active TB. Methods: Case records of adult patients with active TB treated between 1997 and 2003 at the All India Institute of Medical Sciences hospital, New Delhi were retrospectively reviewed. The data set was randomly split into a training set and a testing set. First a clinical prediction rule was derived by multivariable logistic regression on the training set and was subsequently validated on the testing set. Results: The study group comprised 1074 patients [training set 711 (66%), HIV co-infected 66 (9%); testing set 363 (34%), HIV co-infected 30 (8%)]. In the training set, male gender [odds ratio (95% CI) 5.31(1.52- 18.61)], axillary lymphadenopathy [9.71 (3.24-29.10)], anaemia [7.56 (2.48-23.05)], hypoalbuminaemia [3.67(1.31-10.26)], and reduced triceps skinfold thickness [2.91(0.95-8.89)] were independently associated with HIV co-infection. In the testing set, presence of any two of these five features was 94 per cent (95% CI 84-100%) sensitive and 54 per cent (49-60%) specific for predicting HIV co-infection; negative predictive value was 99 per cent (98-100%). Area under the receiver-operating characteristic curve was 0.93 (0.86-1.0) in the testing set. Interpretation & Conclusion: A simple clinical prediction rule based on clinical and laboratory parameters could be used to identify a subgroup of patients, among those treated for active TB in a hospital setting, for targeted HIV testing

Urban Cholera transmission hotspots and their implications for Reactive Vaccination: evidence from Bissau city, Guinea Bissau

Use of cholera vaccines in response to epidemics (reactive vaccination) may provide an effective supplement to traditional control measures. In Haiti, reactive vaccination was considered but, until recently, rejected in part due to limited global supply of vaccine. Using Bissau City, Guinea-Bissau as a case study, we explore neighborhood-level transmission dynamics to understand if, with limited vaccine and likely delays, reactive vaccination can significantly change the course of a cholera epidemic

Parameter identifiability analysis and visualization in large-scale kinetic models of biosystems

[Background] Kinetic models of biochemical systems usually consist of ordinary differential equations that have many unknown parameters. Some of these parameters are often practically unidentifiable, that is, their values cannot be uniquely determined from the available data. Possible causes are lack of influence on the measured outputs, interdependence among parameters, and poor data quality. Uncorrelated parameters can be seen as the key tuning knobs of a predictive model. Therefore, before attempting to perform parameter estimation (model calibration) it is important to characterize the subset(s) of identifiable parameters and their interplay. Once this is achieved, it is still necessary to perform parameter estimation, which poses additional challenges.[Methods] We present a methodology that (i) detects high-order relationships among parameters, and (ii) visualizes the results to facilitate further analysis. We use a collinearity index to quantify the correlation between parameters in a group in a computationally efficient way. Then we apply integer optimization to find the largest groups of uncorrelated parameters. We also use the collinearity index to identify small groups of highly correlated parameters. The results files can be visualized using Cytoscape, showing the identifiable and non-identifiable groups of parameters together with the model structure in the same graph.[Results] Our contributions alleviate the difficulties that appear at different stages of the identifiability analysis and parameter estimation process. We show how to combine global optimization and regularization techniques for calibrating medium and large scale biological models with moderate computation times. Then we evaluate the practical identifiability of the estimated parameters using the proposed methodology. The identifiability analysis techniques are implemented as a MATLAB toolbox called VisId, which is freely available as open source from GitHub ( https://github.com/gabora/visid ).[Conclusions] Our approach is geared towards scalability. It enables the practical identifiability analysis of dynamic models of large size, and accelerates their calibration. The visualization tool allows modellers to detect parts that are problematic and need refinement or reformulation, and provides experimentalists with information that can be helpful in the design of new experiments.This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (“CANPATHPRO”), from the EU FP7 project "NICHE" (ITN Grant number 289384), and from the Spanish MINECO project "SYNBIOFACTORY" (grant number DPI2014-55276-C5-2-R).Peer reviewe

PREMER: Parallel reverse engineering of biological networks with information theory

A common approach for reverse engineering biological networks from data is to deduce the existence of interactions among nodes from information theoretic measures. Estimating these quantities in a multidimensional space is computationally demanding for large datasets. This hampers the application of elaborate algorithms which are crucial for discarding spurious interactions and determining causal relationships  to large-scale network inference problems. To alleviate this issue we have developed PREMER, a software tool which can automatically run in parallel and sequential environments, thanks to its implementation of OpenMP directives. It recovers network topology and estimates the strength and causality of interactions using information theoretic criteria, and allowing the incorporation of prior knowledge. A preprocessing module takes care of imputing missing data and correcting outliers if needed. PREMER (https://sites.google.com/site/premertoolbox/) runs on Windows, Linux and OSX, it is implemented in Matlab/Octave and Fortran 90, and it does not require any commercial software.AFV acknowledges funding from the Galician government (Xunta de Galiza) through the I2C fellowship ED481B2014/133-0. KB was supported by the German Federal Ministry of Research and Education (BMBF, OncoPath consortium). JRB acknowledges funding from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the project “SYNBIOFACTORY” (grant number DPI2014-55276-C5-2-R). This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 686282 (CanPathPro). We thank David R. Penas and David Henriques for assistance with the implementation

Inference of complex biological networks: distinguishability issues and optimization-based solutions

<p>Abstract</p> <p>Background</p> <p>The inference of biological networks from high-throughput data has received huge attention during the last decade and can be considered an important problem class in systems biology. However, it has been recognized that reliable network inference remains an unsolved problem. Most authors have identified lack of data and deficiencies in the inference algorithms as the main reasons for this situation.</p> <p>Results</p> <p>We claim that another major difficulty for solving these inference problems is the frequent lack of uniqueness of many of these networks, especially when prior assumptions have not been taken properly into account. Our contributions aid the distinguishability analysis of chemical reaction network (CRN) models with mass action dynamics. The novel methods are based on linear programming (LP), therefore they allow the efficient analysis of CRNs containing several hundred complexes and reactions. Using these new tools and also previously published ones to obtain the network structure of biological systems from the literature, we find that, often, a unique topology cannot be determined, even if the structure of the corresponding mathematical model is assumed to be known and all dynamical variables are measurable. In other words, certain mechanisms may remain undetected (or they are falsely detected) while the inferred model is fully consistent with the measured data. It is also shown that sparsity enforcing approaches for determining 'true' reaction structures are generally not enough without additional prior information.</p> <p>Conclusions</p> <p>The inference of biological networks can be an extremely challenging problem even in the utopian case of perfect experimental information. Unfortunately, the practical situation is often more complex than that, since the measurements are typically incomplete, noisy and sometimes dynamically not rich enough, introducing further obstacles to the structure/parameter estimation process. In this paper, we show how the structural uniqueness and identifiability of the models can be guaranteed by carefully adding extra constraints, and that these important properties can be checked through appropriate computation methods.</p

On the Verification and Correction of Large-Scale Kinetic Models in Systems Biology

14 páginasIn this paper we consider the problem of verification of large dynamic models of biological systems. We present syntactical criteria based on biochemical kinetics to ensure the plausibility of a model and the positivity of its solution. These criteria include the positivity of the rate functions, their kinetic type dependence on the reactant species concentrations, and the absence of the negative cross-effects that together guarantee the nonnegativity of the dynamics. Further, the stoichiometric matrix of the truncated reaction system is checked against conservation using its algebraic properties. Algorithmic procedures are then proposed for checking these criteria with emphasis on good scaling up properties. In addition to these verification procedures, we also provide, for certain typical errors, model correcting methods. The capabilities and usefulness of these procedures are illustrated on biochemical models taken from the Biomodels database. In particular, a set of 11 kinetic models related with E. coli are checked, finding two with deficiencies. Correcting actions for these models are proposed.Peer reviewe

Novel metaheuristic for parameter estimation in nonlinear dynamic biological systems

BACKGROUND: We consider the problem of parameter estimation (model calibration) in nonlinear dynamic models of biological systems. Due to the frequent ill-conditioning and multi-modality of many of these problems, traditional local methods usually fail (unless initialized with very good guesses of the parameter vector). In order to surmount these difficulties, global optimization (GO) methods have been suggested as robust alternatives. Currently, deterministic GO methods can not solve problems of realistic size within this class in reasonable computation times. In contrast, certain types of stochastic GO methods have shown promising results, although the computational cost remains large. Rodriguez-Fernandez and coworkers have presented hybrid stochastic-deterministic GO methods which could reduce computation time by one order of magnitude while guaranteeing robustness. Our goal here was to further reduce the computational effort without loosing robustness. RESULTS: We have developed a new procedure based on the scatter search methodology for nonlinear optimization of dynamic models of arbitrary (or even unknown) structure (i.e. black-box models). In this contribution, we describe and apply this novel metaheuristic, inspired by recent developments in the field of operations research, to a set of complex identification problems and we make a critical comparison with respect to the previous (above mentioned) successful methods. CONCLUSION: Robust and efficient methods for parameter estimation are of key importance in systems biology and related areas. The new metaheuristic presented in this paper aims to ensure the proper solution of these problems by adopting a global optimization approach, while keeping the computational effort under reasonable values. This new metaheuristic was applied to a set of three challenging parameter estimation problems of nonlinear dynamic biological systems, outperforming very significantly all the methods previously used for these benchmark problems

On the relationship between sloppiness and identifiability

25 páginas, 11 figuras, 2 tablasDynamic models of biochemical networks are often formulated as sets of non-linear ordinary differential equations, whose states are the concentrations or abundances of the network components. They typically have a large number of kinetic parameters, which must be determined by calibrating the model with experimental data. In recent years it has been suggested that dynamic systems biology models are universally sloppy, meaning that the values of some parameters can be perturbed by several orders of magnitude without causing significant changes in the model output. This observation has prompted calls for focusing on model predictions rather than on parameters. In this work we examine the concept of sloppiness, investigating its links with the long-established notions of structural and practical identifiability. By analysing a set of case studies we show that sloppiness is not equivalent to lack of identifiability, and that sloppy models can be identifiable. Thus, using sloppiness to draw conclusions about the possibility of estimating parameter values can be misleading. Instead, structural and practical identifiability analyses are better tools for assessing the confidence in parameter estimates. Furthermore, we show that, when designing new experiments to decrease parametric uncertainty, designs that optimize practical identifiability criteria are more informative than those that minimize sloppinessThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 686282 (“CANPATHPRO”) and from the Spanish government (MINECO) and the European Regional Development Fund (ERDF) through the projects “SYNBIOFACTORY” (grant number DPI2014-55276-C5-2-R), and “IMPROWINE” (grant number AGL2015-67504-C3-2-R)N

Functional Effects of Nanoparticle Exposure on Calu-3 Airway Epithelial Cells

High concentrations of manufactured carbon nanoparticles (CNP) are known to cause oxidative stress, inflammatory responses and granuloma formation in respiratory epithelia. To examine the effects of lower, more physiologically relevant concentrations, the human airway epithelial cell line, Calu-3, was used to evaluate potential alterations in transepithelial permeability and cellular function of airway epithelia after exposure to environmentally realistic concentrations of carbon nanoparticles. Three common carbon nanoparticles, fullerenes, single- and multi-wall carbon nanotubes (SWCNT, MWCNT) were used in these experiments. Electrophysiological measurements were performed to assay transepithelial electrical resistance (TEER) and epinephrine-stimulated chloride (Cl(-)) ion secretion of epithelial cell monolayers that had been exposed to nanoparticles for three different times (1 h, 24 h and 48 h) and over a 7 log unit range of concentrations. Fullerenes did not have any effect on the TEER or stimulated ion transport. However, the carbon nanotubes (CNT) significantly decreased TEER and inhibited epinephrine-stimulated Cl(-) secretion. The changes were time dependent and at more chronic exposures caused functional effects which were evident at concentrations substantially lower than have been previously examined. The functional changes manifested in response to physiologically relevant exposures would inhibit mucociliary clearance mechanisms and compromise the barrier function of airway epithelia