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1.  Metamodels for Transdisciplinary Analysis of Wildlife Population Dynamics 
PLoS ONE  2013;8(12):e84211.
Wildlife population models have been criticized for their narrow disciplinary perspective when analyzing complexity in coupled biological – physical – human systems. We describe a “metamodel” approach to species risk assessment when diverse threats act at different spatiotemporal scales, interact in non-linear ways, and are addressed by distinct disciplines. A metamodel links discrete, individual models that depict components of a complex system, governing the flow of information among models and the sequence of simulated events. Each model simulates processes specific to its disciplinary realm while being informed of changes in other metamodel components by accessing common descriptors of the system, populations, and individuals. Interactions among models are revealed as emergent properties of the system. We introduce a new metamodel platform, both to further explain key elements of the metamodel approach and as an example that we hope will facilitate the development of other platforms for implementing metamodels in population biology, species risk assessments, and conservation planning. We present two examples – one exploring the interactions of dispersal in metapopulations and the spread of infectious disease, the other examining predator-prey dynamics – to illustrate how metamodels can reveal complex processes and unexpected patterns when population dynamics are linked to additional extrinsic factors. Metamodels provide a flexible, extensible method for expanding population viability analyses beyond models of isolated population demographics into more complete representations of the external and intrinsic threats that must be understood and managed for species conservation.
PMCID: PMC3862810  PMID: 24349567
2.  Multi-way metamodelling facilitates insight into the complex input-output maps of nonlinear dynamic models 
BMC Systems Biology  2012;6:88.
Statistical approaches to describing the behaviour, including the complex relationships between input parameters and model outputs, of nonlinear dynamic models (referred to as metamodelling) are gaining more and more acceptance as a means for sensitivity analysis and to reduce computational demand. Understanding such input-output maps is necessary for efficient model construction and validation. Multi-way metamodelling provides the opportunity to retain the block-wise structure of the temporal data typically generated by dynamic models throughout the analysis. Furthermore, a cluster-based approach to regional metamodelling allows description of highly nonlinear input-output relationships, revealing additional patterns of covariation.
By presenting the N-way Hierarchical Cluster-based Partial Least Squares Regression (N-way HC-PLSR) method, we here combine multi-way analysis with regional cluster-based metamodelling, together making a powerful methodology for extensive exploration of the input-output maps of complex dynamic models. We illustrate the potential of the N-way HC-PLSR by applying it both to predict model outputs as functions of the input parameters, and in the inverse direction (predicting input parameters from the model outputs), to analyse the behaviour of a dynamic model of the mammalian circadian clock. Our results display a more complete cartography of how variation in input parameters is reflected in the temporal behaviour of multiple model outputs than has been previously reported.
Our results indicated that the N-way HC-PLSR metamodelling provides a gain in insight into which parameters that are related to a specific model output behaviour, as well as variations in the model sensitivity to certain input parameters across the model output space. Moreover, the N-way approach allows a more transparent and detailed exploration of the temporal dimension of complex dynamic models, compared to alternative 2-way methods.
PMCID: PMC3483253  PMID: 22818032
Parameter-phenotype map; Dynamic models; Metamodelling; N-way Partial Least Squares Regression; Hierarchical analysis; HC-PLSR; Cluster-analysis; Input-output relationships; Circadian clock
3.  Statistical Metamodeling for Revealing Synergistic Antimicrobial Interactions 
PLoS ONE  2010;5(11):e15472.
Many bacterial pathogens are becoming drug resistant faster than we can develop new antimicrobials. To address this threat in public health, a metamodel antimicrobial cocktail optimization (MACO) scheme is demonstrated for rapid screening of potent antibiotic cocktails using uropathogenic clinical isolates as model systems. With the MACO scheme, only 18 parallel trials were required to determine a potent antimicrobial cocktail out of hundreds of possible combinations. In particular, trimethoprim and gentamicin were identified to work synergistically for inhibiting the bacterial growth. Sensitivity analysis indicated gentamicin functions as a synergist for trimethoprim, and reduces its minimum inhibitory concentration for 40-fold. Validation study also confirmed that the trimethoprim-gentamicin synergistic cocktail effectively inhibited the growths of multiple strains of uropathogenic clinical isolates. With its effectiveness and simplicity, the MACO scheme possesses the potential to serve as a generic platform for identifying synergistic antimicrobial cocktails toward management of bacterial infection in the future.
PMCID: PMC2988685  PMID: 21124958
4.  Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR) is an efficient tool for metamodelling of nonlinear dynamic models 
BMC Systems Biology  2011;5:90.
Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs) to variation in features of the trajectories of the state variables (outputs) throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR), where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR) and ordinary least squares (OLS) regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function.
Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback loops.
HC-PLSR is a promising approach for metamodelling in systems biology, especially for highly nonlinear or non-monotone parameter to phenotype maps. The algorithm can be flexibly adjusted to suit the complexity of the dynamic model behaviour, inviting automation in the metamodelling of complex systems.
PMCID: PMC3127793  PMID: 21627852
5.  A UML profile for the OBO relation ontology 
BMC Genomics  2012;13(Suppl 5):S3.
Ontologies have increasingly been used in the biomedical domain, which has prompted the emergence of different initiatives to facilitate their development and integration. The Open Biological and Biomedical Ontologies (OBO) Foundry consortium provides a repository of life-science ontologies, which are developed according to a set of shared principles. This consortium has developed an ontology called OBO Relation Ontology aiming at standardizing the different types of biological entity classes and associated relationships. Since ontologies are primarily intended to be used by humans, the use of graphical notations for ontology development facilitates the capture, comprehension and communication of knowledge between its users. However, OBO Foundry ontologies are captured and represented basically using text-based notations. The Unified Modeling Language (UML) provides a standard and widely-used graphical notation for modeling computer systems. UML provides a well-defined set of modeling elements, which can be extended using a built-in extension mechanism named Profile. Thus, this work aims at developing a UML profile for the OBO Relation Ontology to provide a domain-specific set of modeling elements that can be used to create standard UML-based ontologies in the biomedical domain.
We have studied the OBO Relation Ontology, the UML metamodel and the UML profiling mechanism. Based on these studies, we have proposed an extension to the UML metamodel in conformance with the OBO Relation Ontology and we have defined a profile that implements the extended metamodel. Finally, we have applied the proposed UML profile in the development of a number of fragments from different ontologies. Particularly, we have considered the Gene Ontology (GO), the PRotein Ontology (PRO) and the Xenopus Anatomy and Development Ontology (XAO).
The use of an established and well-known graphical language in the development of biomedical ontologies provides a more intuitive form of capturing and representing knowledge than using only text-based notations. The use of the profile requires the domain expert to reason about the underlying semantics of the concepts and relationships being modeled, which helps preventing the introduction of inconsistencies in an ontology under development and facilitates the identification and correction of errors in an already defined ontology.
PMCID: PMC3477006  PMID: 23095840
6.  Insight into model mechanisms through automatic parameter fitting: a new methodological framework for model development 
BMC Systems Biology  2014;8:59.
Striking a balance between the degree of model complexity and parameter identifiability, while still producing biologically feasible simulations using modelling is a major challenge in computational biology. While these two elements of model development are closely coupled, parameter fitting from measured data and analysis of model mechanisms have traditionally been performed separately and sequentially. This process produces potential mismatches between model and data complexities that can compromise the ability of computational frameworks to reveal mechanistic insights or predict new behaviour. In this study we address this issue by presenting a generic framework for combined model parameterisation, comparison of model alternatives and analysis of model mechanisms.
The presented methodology is based on a combination of multivariate metamodelling (statistical approximation of the input–output relationships of deterministic models) and a systematic zooming into biologically feasible regions of the parameter space by iterative generation of new experimental designs and look-up of simulations in the proximity of the measured data. The parameter fitting pipeline includes an implicit sensitivity analysis and analysis of parameter identifiability, making it suitable for testing hypotheses for model reduction. Using this approach, under-constrained model parameters, as well as the coupling between parameters within the model are identified. The methodology is demonstrated by refitting the parameters of a published model of cardiac cellular mechanics using a combination of measured data and synthetic data from an alternative model of the same system. Using this approach, reduced models with simplified expressions for the tropomyosin/crossbridge kinetics were found by identification of model components that can be omitted without affecting the fit to the parameterising data. Our analysis revealed that model parameters could be constrained to a standard deviation of on average 15% of the mean values over the succeeding parameter sets.
Our results indicate that the presented approach is effective for comparing model alternatives and reducing models to the minimum complexity replicating measured data. We therefore believe that this approach has significant potential for reparameterising existing frameworks, for identification of redundant model components of large biophysical models and to increase their predictive capacity.
PMCID: PMC4078362  PMID: 24886522
Parameter estimation; Multivariate metamodelling; Parameter space exploration; Zooming into feasible parameter space regions; Experimental design; Model reduction; Computational Biology; Cardiac contraction
7.  Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach 
PLoS ONE  2015;10(12):e0143626.
Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information.
PMCID: PMC4668087  PMID: 26629901
8.  Computational Intelligence and Wavelet Transform Based Metamodel for Efficient Generation of Not-Yet Simulated Waveforms 
PLoS ONE  2016;11(1):e0146602.
The design and verification of complex electronic systems, especially the analog and mixed-signal ones, prove to be extremely time consuming tasks, if only circuit-level simulations are involved. A significant amount of time can be saved if a cost effective solution is used for the extensive analysis of the system, under all conceivable conditions. This paper proposes a data-driven method to build fast to evaluate, but also accurate metamodels capable of generating not-yet simulated waveforms as a function of different combinations of the parameters of the system. The necessary data are obtained by early-stage simulation of an electronic control system from the automotive industry. The metamodel development is based on three key elements: a wavelet transform for waveform characterization, a genetic algorithm optimization to detect the optimal wavelet transform and to identify the most relevant decomposition coefficients, and an artificial neuronal network to derive the relevant coefficients of the wavelet transform for any new parameters combination. The resulted metamodels for three different waveform families are fully reliable. They satisfy the required key points: high accuracy (a maximum mean squared error of 7.1x10-5 for the unity-based normalized waveforms), efficiency (fully affordable computational effort for metamodel build-up: maximum 18 minutes on a general purpose computer), and simplicity (less than 1 second for running the metamodel, the user only provides the parameters combination). The metamodels can be used for very efficient generation of new waveforms, for any possible combination of dependent parameters, offering the possibility to explore the entire design space. A wide range of possibilities becomes achievable for the user, such as: all design corners can be analyzed, possible worst-case situations can be investigated, extreme values of waveforms can be discovered, sensitivity analyses can be performed (the influence of each parameter on the output waveform).
PMCID: PMC4706416  PMID: 26745370
9.  A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions 
The Scientific World Journal  2014;2014:192862.
Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples.
PMCID: PMC4124212  PMID: 25133206
10.  Discerning the Complexity of Community Interactions Using a Drosophila Model of Polymicrobial Infections 
PLoS Pathogens  2008;4(10):e1000184.
A number of human infections are characterized by the presence of more than one bacterial species and are defined as polymicrobial diseases. Methods for the analysis of the complex biological interactions in mixed infections with a large number of microorganisms are limited and do not effectively determine the contribution of each bacterial species to the pathogenesis of the polymicrobial community. We have developed a novel Drosophila melanogaster infection model to study microbe–microbe interactions and polymicrobe–host interactions. Using this infection model, we examined the interaction of 40 oropharyngeal isolates with Pseudomonas aeruginosa. We observe three classes of microorganisms, one of which acts synergistically with the principal pathogen, while being avirulent or even beneficial on its own. This synergy involves microbe–microbe interactions that result in the modulation of P. aeruginosa virulence factor gene expression within infected Drosophila. The host innate immune response to these natural-route polymicrobial infections is complex and characterized by additive, suppressive, and synergistic transcriptional activation of antimicrobial peptide genes. The polymicrobial infection model was used to differentiate the bacterial flora in cystic fibrosis (CF) sputum, revealing that a large proportion of the organisms in CF airways has the ability to influence the outcome of an infection when in combination with the principal CF pathogen P. aeruginosa.
Author Summary
Bacterial infections often involve more than one species. The lung disease of cystic fibrosis (CF) patients provides examples of polymicrobial infections whereby diverse and dynamic microbial communities are a characteristic of CF airways. The significance of microbe–microbe interactions and the interplay of the communities with the host have not been thoroughly investigated. We describe a novel Drosophila model to discern the biological interactions between microbes within microbial communities, as well as the interactions between the communities and the innate immune system. Using fly survival as a readout of relevant interactions, we show that mixed infections may additively or synergistically enhance the pathogenicity of a microbial community. The polymicrobial infection model was used to differentiate the bacterial flora in CF sputum, revealing that a large proportion of the organisms in CF airways has the ability to influence the outcome of an infection when in combination with the principal CF pathogen Pseudomonas aeruginosa. We show that during the synergistic-type mixed infections, P. aeruginosa virulence gene expression is altered within live Drosophila compared to mono-species infections. The immune response to microbial communities takes many forms and can include synergistic activation of antimicrobial peptide gene expression. We postulate that the biological interactions exposed using this model may contribute to the transition from chronic stable infections to acute pulmonary exacerbation infections in CF.
PMCID: PMC2566602  PMID: 18949036
11.  Influence of the variation of geometrical and topological traits on light interception efficiency of apple trees: sensitivity analysis and metamodelling for ideotype definition 
Annals of Botany  2014;114(4):739-752.
Background and Aims
The impact of a fruit tree's architecture on its performance is still under debate, especially with regard to the definition of varietal ideotypes and the selection of architectural traits in breeding programmes. This study aimed at providing proof that a modelling approach can contribute to this debate, by using in silico exploration of different combinations of traits and their consequences on light interception, here considered as one of the key parameters to optimize fruit tree production.
The variability of organ geometrical traits, previously described in a bi-parental population, was used to simulate 1- to 5-year-old apple trees (Malus × domestica). Branching sequences along trunks observed during the first year of growth of the same hybrid trees were used to initiate the simulations, and hidden semi-Markov chains previously parameterized were used in subsequent years. Tree total leaf area (TLA) and silhouette to total area ratio (STAR) values were estimated, and a sensitivity analysis was performed, based on a metamodelling approach and a generalized additive model (GAM), to analyse the relative impact of organ geometry and lateral shoot types on STAR.
Key Results
A larger increase over years in TLA mean and variance was generated by varying branching along trunks than by varying organ geometry, whereas the inverse was observed for STAR, where mean values stabilized from year 3 to year 5. The internode length and leaf area had the highest impact on STAR, whereas long sylleptic shoots had a more significant effect than proleptic shoots. Although the GAM did not account for interactions, the additive effects of the geometrical factors explained >90% of STAR variation, but much less in the case of branching factors.
This study demonstrates that the proposed modelling approach could contribute to screening architectural traits and their relative impact on tree performance, here viewed through light interception. Even though trait combinations and antagonism will need further investigation, the approach opens up new perspectives for breeding and genetic selection to be assisted by varietal ideotype definition.
PMCID: PMC4156120  PMID: 24723446
Silhouette to total area ratio; STAR; functional–structural growth modelling; leaf area; branching; sensitivity analysis; apple; ideotype; Malus × domestica
12.  Dynamic Response and Optimal Design of Curved Metallic Sandwich Panels under Blast Loading 
The Scientific World Journal  2014;2014:853681.
It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a “soft” outer face and a “hard” inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances.
PMCID: PMC4121257  PMID: 25126606
13.  Fitness benefits in fluoroquinolone-resistant Salmonella Typhi in the absence of antimicrobial pressure 
eLife  2013;2:e01229.
Fluoroquinolones (FQ) are the recommended antimicrobial treatment for typhoid, a severe systemic infection caused by the bacterium Salmonella enterica serovar Typhi. FQ-resistance mutations in S. Typhi have become common, hindering treatment and control efforts. Using in vitro competition experiments, we assayed the fitness of eleven isogenic S. Typhi strains with resistance mutations in the FQ target genes, gyrA and parC. In the absence of antimicrobial pressure, 6 out of 11 mutants carried a selective advantage over the antimicrobial-sensitive parent strain, indicating that FQ resistance in S. Typhi is not typically associated with fitness costs. Double-mutants exhibited higher than expected fitness as a result of synergistic epistasis, signifying that epistasis may be a critical factor in the evolution and molecular epidemiology of S. Typhi. Our findings have important implications for the management of drug-resistant S. Typhi, suggesting that FQ-resistant strains would be naturally maintained even if fluoroquinolone use were reduced.
eLife digest
The fluoroquinolones are a group of antimicrobials that are used to treat a variety of life-threatening bacterial infections, including typhoid fever. Before the introduction of antimicrobials, the mortality rate from typhoid fever was 10–20%. Prompt treatment with fluoroquinolones has reduced this to less than 1%, and has also decreased the severity of symptoms suffered by people with the disease.
Now, however, the usefulness of many antimicrobials, including the fluoroquinolones, is threatened by the evolution of antimicrobial resistance within the bacterial populations being treated. Drug resistance in bacteria typically arises through specific mutations, or following the acquisition of antimicrobial resistance genes from other bacteria. It is thought that the frequent use of antimicrobials in human and animal health puts selective pressure on bacterial populations, allowing bacterial strains with mutations or genes that confer antimicrobial resistance to survive, while bacterial strains that are sensitive to the antimicrobials die out.
At first it was thought that specific mutations conferring antimicrobial resistance came at a fitness cost, which would mean that such mutations would be rare in the absence of antimicrobials. Now, based on research into typhoid fever, Baker et al. describe a system in which the majority of evolutionary routes to drug resistance are marked by significant fitness benefits, even in the absence of antimicrobial exposure.
Typhoid is caused by a bacterial pathogen known as Salmonella Typhi, and mutations in two genes—gyrA and parC—result in resistance to fluoroquinolones. Baker et al. show that mutations in these genes confer a measurable fitness advantage over strains without these mutations, even in the absence of exposure to fluoroquinolones. Moreover, strains with two mutations in one of these genes exhibited a higher than predicted fitness, suggesting that there is a synergistic interaction between the two mutations. This work challenges the dogma that antimicrobial resistant organisms have a fitness disadvantage in the absence of antimicrobials, and suggests that increasing resistance to the fluoroquinolones is not solely driven by excessive use of this important group of drugs.
PMCID: PMC3857714  PMID: 24327559
Salmonella; typhoid; fitness cost; epistasis; fluoroquinolone; Other
14.  Cross-species discovery of syncretic drug combinations that potentiate the antifungal fluconazole 
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
The authors screen for compounds that show synergistic antifungal activity when combined with the widely-used fungistatic drug fluconazole. Chemogenomic profiling explains the mode of action of synergistic drugs and allows the prediction of additional drug synergies.
Chemical screens with a library enriched for known drugs identified a diverse set of 148 compounds that potentiated the action of the antifungal drug fluconazole against the fungal pathogens Cryptococcus neoformans, Cryptococcus gattii and Candida albicans, and the model yeast Saccharomyces cerevisiae, often in a species-specific manner.Chemogenomic profiles of six confirmed hits in S. cerevisiae revealed different modes of action and enabled the prediction of additional synergistic combinations; three-way synergistic interactions exhibited even stronger synergies at low doses of fluconazole.The synergistic combination of fluconazole and the antidepressant sertraline was active against fluconazole-resistant clinical fungal isolates and in an in vivo model of Cryptococcal infection.
Rising fungal infection rates, especially among immune-suppressed individuals, represent a serious clinical challenge (Gullo, 2009). Cancer, organ transplant and HIV patients, for example, often succumb to opportunistic fungal pathogens. The limited repertoire of approved antifungal agents and emerging drug resistance in the clinic further complicate the effective treatment of systemic fungal infections. At the molecular level, the paucity of fungal-specific essential targets arises from the conserved nature of cellular functions from yeast to humans, as well as from the fact that many essential yeast genes can confer viability at a fraction of wild-type dosage (Yan et al, 2009). Although only ∼1100 of the ∼6000 genes in yeast are essential, almost all genes become essential in specific genetic backgrounds in which another non-essential gene has been deleted or otherwise attenuated, an effect termed synthetic lethality (Tong et al, 2001). Genome-scale surveys suggest that over 200 000 binary synthetic lethal gene combinations dominate the yeast genetic landscape (Costanzo et al, 2010). The genetic buffering phenomenon is also manifest as a plethora of differential chemical–genetic interactions in the presence of sublethal doses of bioactive compounds (Hillenmeyer et al, 2008). These observations frame the difficulty of interdicting network functions in eukaryotic pathogens with single agent therapeutics. At the same time, however, this genetic network organization suggests that judicious combinations of small molecule inhibitors of both essential and non-essential targets may elicit additive or synergistic effects on cell growth (Sharom et al, 2004; Lehar et al, 2008). Unbiased screens for drugs that synergistically enhance a specific bioactive effect, but which are not themselves individually active—termed a syncretic combination—are one means to substantially elaborate chemical space (Keith et al, 2005). Indeed, compounds that enhance the activity of known agents in model yeast and cancer cell line systems have been identified both by focused small molecule library screens and by computational methods (Borisy et al, 2003; Lehar et al, 2007; Nelander et al, 2008; Jansen et al, 2009; Zinner et al, 2009).
To extend the stratagem of chemical synthetic lethality to clinically relevant fungal pathogens, we screened a bioactive library of known drugs for synergistic enhancers of the widely used fungistatic drug fluconazole against the clinically relevant pathogens C. albicans, C. neoformans and C. gattii, as well as the genetically tractable budding yeast S. cerevisiae. Fluconazole is an azole drug that inhibits lanosterol 14α-demethylase, the gene product of ERG11, an essential cytochrome P450 enzyme in the ergosterol biosynthetic pathway (Groll et al, 1998). We identified 148 drugs that potentiate the antifungal action of fluconazole against the four species. These syncretic compounds had not been previously recognized in the clinic as antifungal agents, and many acted in a species-specific manner, often in a potent fungicidal manner.
To understand the mechanisms of synergism, we interrogated six syncretic drugs—trifluoperazine, tamoxifen, clomiphene, sertraline, suloctidil and L-cycloserine—in genome-wide chemogenomic profiles of the S. cerevisiae deletion strain collection (Giaever et al, 1999). These profiles revealed that membrane, vesicle trafficking and lipid biosynthesis pathways are targeted by five of the synergizers, whereas the sphingolipid biosynthesis pathway is targeted by L-cycloserine. Cell biological assays confirmed the predicted membrane disruption effects of the former group of compounds, which may perturb ergosterol metabolism, impair fluconazole export by drug efflux pumps and/or affect active import of fluconazole (Kuo et al, 2010; Mansfield et al, 2010). Based on the integration of chemical–genetic and genetic interaction space, a signature set of deletion strains that are sensitive to the membrane active synergizers correctly predicted additional drug synergies with fluconazole. Similarly, the L-cycloserine chemogenomic profile correctly predicted a synergistic interaction between fluconazole and myriocin, another inhibitor of sphingolipid biosynthesis. The structure of genetic networks suggests that it should be possible to devise higher order drug combinations with even greater selectivity and potency (Sharom et al, 2004). In an initial test of this concept, we found that the combination of a non-synergistic pair drawn from the membrane active and sphingolipid target classes exhibited potent three-way synergism with a low dose of fluconazole. Finally, the combination of sertraline and fluconazole was active in a G. mellonella model of Cryptococcal infection, and was also efficacious against fluconazole-resistant clinical isolates of C. albicans and C. glabrata.
Collectively, these results demonstrate that the combinatorial redeployment of known drugs defines a powerful antifungal strategy and establish a number of potential lead combinations for future clinical assessment.
Resistance to widely used fungistatic drugs, particularly to the ergosterol biosynthesis inhibitor fluconazole, threatens millions of immunocompromised patients susceptible to invasive fungal infections. The dense network structure of synthetic lethal genetic interactions in yeast suggests that combinatorial network inhibition may afford increased drug efficacy and specificity. We carried out systematic screens with a bioactive library enriched for off-patent drugs to identify compounds that potentiate fluconazole action in pathogenic Candida and Cryptococcus strains and the model yeast Saccharomyces. Many compounds exhibited species- or genus-specific synergism, and often improved fluconazole from fungistatic to fungicidal activity. Mode of action studies revealed two classes of synergistic compound, which either perturbed membrane permeability or inhibited sphingolipid biosynthesis. Synergistic drug interactions were rationalized by global genetic interaction networks and, notably, higher order drug combinations further potentiated the activity of fluconazole. Synergistic combinations were active against fluconazole-resistant clinical isolates and an in vivo model of Cryptococcus infection. The systematic repurposing of approved drugs against a spectrum of pathogens thus identifies network vulnerabilities that may be exploited to increase the activity and repertoire of antifungal agents.
PMCID: PMC3159983  PMID: 21694716
antifungal; combination; pathogen; resistance; synergism
15.  Automatic generation of a metamodel from an existing knowledge base to assist the development of a new analogous knowledge base. 
Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels.
PMCID: PMC2244244  PMID: 12463788
16.  A Lack of Synergy Between Membrane-permeabilizing Cationic Antimicrobial Peptides and Conventional Antibiotics 
Biochimica et biophysica acta  2014;1848(1 0 0):8-15.
The rapid rise in morbidity and mortality from drug-resistant pathogenic bacteria has generated elevated interest in combination therapy using antimicrobial agents. Antimicrobial peptides (AMPs) are a candidate drug class to advance the development of combination therapies. Although the literature is ambiguous, the generic membrane disrupting activity of AMPs could enable them to synergize with conventional small molecule antibiotics by increasing access to the cell and by triggering membrane damage mediators. We used a novel assay to measure interactions, expressed as fractional inhibitory concentration (FIC), between four conventional antibiotics in combination with four well-characterized, membrane permeabilizing AMPs, against three species of Gram negative and Gram positive bacteria, giving 40 total pair-wise measurements of FIC with statistical uncertainties. We chose a set of AMPs that are known to dramatically disrupt the membranes of both Gram negative and Gram positive bacteria. Yet none of the membrane permeabilizing antimicrobial peptides interacted synergistically with any of the conventional antibiotic drugs in any organism. Large-scale membrane disruption and permeabilization by AMPs is not sufficient to drive them to act synergistically with chemical antibiotics in either Gram negative or Gram positive microbes.
PMCID: PMC4259837  PMID: 25268681
17.  Antibacterial Activity of Novel Cationic Peptides against Clinical Isolates of Multi-Drug Resistant Staphylococcus pseudintermedius from Infected Dogs 
PLoS ONE  2014;9(12):e116259.
Staphylococcus pseudintermedius is a major cause of skin and soft tissue infections in companion animals and has zoonotic potential. Additionally, methicillin-resistant S. pseudintermedius (MRSP) has emerged with resistance to virtually all classes of antimicrobials. Thus, novel treatment options with new modes of action are required. Here, we investigated the antimicrobial activity of six synthetic short peptides against clinical isolates of methicillin-susceptible and MRSP isolated from infected dogs. All six peptides demonstrated potent anti-staphylococcal activity regardless of existing resistance phenotype. The most effective peptides were RRIKA (with modified C terminus to increase amphipathicity and hydrophobicity) and WR-12 (α-helical peptide consisting exclusively of arginine and tryptophan) with minimum inhibitory concentration50 (MIC50) of 1 µM and MIC90 of 2 µM. RR (short anti-inflammatory peptide) and IK8 “D isoform” demonstrated good antimicrobial activity with MIC50 of 4 µM and MIC90 of 8 µM. Penetratin and (KFF)3K (two cell penetrating peptides) were the least effective with MIC50 of 8 µM and MIC90 of 16 µM. Killing kinetics revealed a major advantage of peptides over conventional antibiotics, demonstrating potent bactericidal activity within minutes. Studies with propidium iodide and transmission electron microscopy revealed that peptides damaged the bacterial membrane leading to leakage of cytoplasmic contents and consequently, cell death. A potent synergistic increase in the antibacterial effect of the cell penetrating peptide (KFF)3K was noticed when combined with other peptides and with antibiotics. In addition, all peptides displayed synergistic interactions when combined together. Furthermore, peptides demonstrated good therapeutic indices with minimal toxicity toward mammalian cells. Resistance to peptides did not evolve after 10 passages of S. pseudintermedius at sub-inhibitory concentration. However, the MICs of amikacin and ciprofloxacin increased 32 and 8 fold, respectively; under similar conditions. Taken together, these results support designing of peptide-based therapeutics for combating MRSP infections, particularly for topical application.
PMCID: PMC4281220  PMID: 25551573
18.  Synergistic Interaction between Phenothiazines and Antimicrobial Agents against Burkholderia pseudomallei▿  
The gram-negative soil bacillus Burkholderia pseudomallei is the causative agent of melioidosis, a severe and potentially fatal septicemic disease that is endemic to Southeast Asia and northern Australia. Its intrinsic resistance to many antibiotics is attributed mainly to the presence of several drug efflux pumps, and therefore, inhibitors of such pumps are expected to restore the activities of many clinically important antimicrobial agents that are the substrates of these pumps. The phenothiazine antipsychotic and antihistaminic drugs prochlorperazine, chlorpromazine, and promazine have a synergistic interaction with a wide spectrum of antimicrobial agents, thereby enhancing their antimicrobial potency against B. pseudomallei. Antimicrobial agents that interacted synergistically with the phenothiazines include streptomycin, erythromycin, oleandomycin, spectinomycin, levofloxacin, azithromycin, and amoxicillin-clavulanic acid. The MICs of these antibiotics were reduced as much as 8,000-fold in the presence of the phenothiazines. Antimicrobial agents which did not interact synergistically with the phenothiazines include gentamicin, amoxicillin, and ampicillin. Omeprazole, a proton pump inhibitor, provided an augmentation of antimicrobial activities similar to that of the phenothiazines, suggesting that the phenothiazines might have interfered with the proton gradient at the inner membrane. B. pseudomallei cells accumulated more erythromycin in the presence of the phenothiazines, an effect similar to that of carbonyl cyanide m-chlorophenylhydrazone, a proton gradient uncoupler. In the presence of the phenothiazines, a much reduced concentration of erythromycin (0.06× MIC) also protected human lung epithelial cells and macrophage cells from B. pseudomallei infection and attenuated its cytotoxicity.
PMCID: PMC1797753  PMID: 17145801
19.  In Vitro Synergistic Effect of Psidium guineense (Swartz) in Combination with Antimicrobial Agents against Methicillin-Resistant Staphylococcus aureus Strains 
The Scientific World Journal  2012;2012:158237.
The aim of this study was to evaluate the antimicrobial activity of aqueous extract of Psidium guineense Swartz (Araçá-do-campo) and five antimicrobials (ampicillin, amoxicillin/clavulanic acid, cefoxitin, ciprofloxacin, and meropenem) against twelve strains of Staphylococcus aureus with a resistant phenotype previously determined by the disk diffusion method. Four S. aureus strains showed resistance to all antimicrobial agents tested and were selected for the study of the interaction between aqueous extract of P. guineense and antimicrobial agents, by the checkerboard method. The criteria used to evaluate the synergistic activity were defined by the fractional inhibitory concentration index (FICI). All S. aureus strains were susceptible to P. guineense as determined by the microdilution method. The combination of the P. guineense extract with the antimicrobial agents resulted in an eight-fold reduction in the MIC of these agents, which showed a FICI ranging from 0.125 to 0.5, suggesting a synergistic interaction against methicillin-resistant Staphylococcus aureus (MRSA) strains. The combination of the aqueous extract of P. guineense with cefoxitin showed the lowest FICI values. This study demonstrated that the aqueous extract of P. guineense combined with beta lactamics antimicrobials, fluoroquinolones, and carbapenems, acts synergistically by inhibiting MRSA strains.
PMCID: PMC3349319  PMID: 22619603
20.  In Vitro Interactions between Aspirin and Amphotericin B against Planktonic Cells and Biofilm Cells of Candida albicans and C. parapsilosis 
The increase in drug resistance and invasion caused by biofilm formation brings enormous challenges to the management of Candida infection. Aspirin's antibiofilm activity in vitro was discovered recently. The spectrophotometric method and the XTT {2,3-bis(2-methoxy-4-nitro-5-sulfophenyl)-5-[(phenylamino)carbonyl]-2H-tetrazolium hydroxide} reduction assay used for data generation make it possible to evaluate fungal biofilm growth accurately. The combined use of the most commonly used methods, the fractional inhibitory concentration index (FICI) and a newly developed method, the ΔE model, which uses the concentration-effect relationship over the whole concentration range instead of using the MIC index alone, makes the interpretation of results more reliable. As an attractive tool for studying the pharmacodynamics of antimicrobial agents, time-kill curves can provide detailed information about antimicrobial efficacy as a function of both time and concentration. In the present study, in vitro interactions between aspirin (acetylsalicylic acid [ASA]) and amphotericin B (AMB) against planktonic cells and biofilm cells of Candida albicans and C. parapsilosis were evaluated by the checkerboard microdilution method and the time-kill test. Synergistic and indifferent effects were found for the combination of ASA and AMB against planktonic cells, while strong synergy was found against biofilm cells analyzed by FICI. The ΔE model gave more consistent results with FICI. The positive interactions in concentration were also confirmed by the time-kill test. Moreover, this approach also revealed the pharmacodynamics changes of ASA and synergistic action on time. Our findings suggest a potential clinical use for combination therapy with ASA and AMB to augment activity against biofilm-associated infections.
PMCID: PMC3370722  PMID: 22391539
21.  Evaluation of Synergistic Antibacterial and Antioxidant Efficacy of Essential Oils of Spices and Herbs in Combination 
PLoS ONE  2015;10(7):e0131321.
The present study was carried out to evaluate the possible synergistic interactions on antibacterial and antioxidant efficacy of essential oils of some selected spices and herbs [bay leaf, black pepper, coriander (seed and leaf), cumin, garlic, ginger, mustard, onion and turmeric] in combination. Antibacterial combination effect was evaluated against six important food-borne bacteria (Bacillus cereus, Listeria monocytogenes, Micrococcus luteus, Staphylococcus aureus, Escherichia coli and Salmonella typhimurium) using microbroth dilution, checkerboard titration and time-kill methods. Antioxidant combination effect was assessed by DPPH free radical scavenging method. Total phenolic content was measured by Folin-Ciocalteu method. Bioactivity –guided fractionation of active essential oils for isolation of bioactive compounds was done using TLC-bioautography assay and chemical characterization (qualitative and quantitative) of bioactive compounds was performed using DART-MS and HPLC analyses. Cytotoxic potential was evaluated by brine shrimp lethality assay as well as MTT assay using human normal colon cell line. Results showed that among the possible combinations tested only coriander/cumin seed oil combination showed synergistic interactions both in antibacterial (FICI : 0.25-0.50) and antioxidant (CI : 0.79) activities. A high positive correlation between total phenolic content and antibacterial activity against most of the studied bacteria (R2 = 0.688 – 0.917) as well as antioxidant capacity (R2 = 0.828) was also observed. TLC-bioautography-guided screening and subsequent combination studies revealed that two compounds corresponding to Rf values 0.35 from coriander seed oil and 0.53 from cumin seed oil exhibited both synergistic antibacterial and antioxidant activities. The bioactive compound corresponding to Rf 0.35 from coriander seed oil was identified as linalool (68.69%) and the bioactive compound corresponding to Rf 0.53 from cumin seed oil was identified as p-coumaric acid (7.14%) by DART-MS and HPLC analyses. The coriander/cumin seed oil combination did not show any cytotoxic effect both in brine shrimp lethality as well as human normal colon cell line assays. The LC50 in brine shrimp lethality assay was found to be 4945.30 μg/ml and IC50 in human normal colon cell line was > 1000 μg/ml. The results provide evidence that coriander/cumin seed oil combination might indeed be used as a potential source of safe and effective natural antimicrobial and antioxidant agents in pharmaceutical and food industries.
PMCID: PMC4488432  PMID: 26132146
22.  Synergistic effects of Miconazole and Polymyxin B on microbial pathogens 
The therapeutic value of antibiotics depends on the susceptibility of the infecting microorganism and the pharmacological profile of the drugs. To assess the value of an antibiotic combination of polymyxin B and miconazole this study examined the in vitro synergistic potential of the two drugs on Gram-negative and Gram-positive bacteria and yeast. Antifungal and antibacterial activity was tested by minimum inhibitory concentration (MIC) of broth macrodilution and urea broth microdilution, by fluorescence microscopy and flow cytometry. Synergism was calculated using the fractional inhibitory concentration index (FICi). With Staphylococcus intermedius as target we found up to an eightfold reduction of the individual MICs when both drugs were combined. However, the FICi was 0.63 suggesting no real interaction between the two drugs. With Escherichia coli, Pseudomonas aeruginosa, and Malassezia pachydermatis as targets the antimicrobial drug combination reduced the MICs of polymyxin B and miconazole from fourfold to hundredfold resulting in FICi between 0.06 and 0.5 which defines a synergistic action. Thus, if polymyxin B and miconazole are combined their effect is greater than the sum of the effects observed with polymyxin B and miconazole independently, revealing bactericidal and fungicidal synergism. Our results indicate a strong therapeutic value for the combination of these antimicrobial agents against Gram-negative bacteria and yeast and a weaker value against Gram positive bacteria for clinical situations where these pathogens are involved.
PMCID: PMC2707952  PMID: 19085068
Miconazole; Polymyxin; Antibiotic synergism; Otitis externa
23.  Synergistic interactions of TLR2/6 and TLR9 induce a high level of resistance to lung infection in mice 
Infectious pneumonias exact an unacceptable mortality burden worldwide. Efforts to protect populations from pneumonia have historically focused on antibiotic development and vaccine-enhanced adaptive immunity. However, we have recently reported that the lungs’ innate defenses can be therapeutically induced by inhalation of a bacterial lysate that protects mice against otherwise lethal pneumonia. Here, we tested in mice the hypothesis that Toll-like receptors (TLRs) are required for this antimicrobial phenomenon, and found that resistance could not be induced in the absence of the TLR signaling adaptor protein MyD88. We then attempted to recapitulate the protection afforded by the bacterial lysate by stimulating the lung epithelium with aerosolized synthetic TLR ligands. While most single or combination treatments yielded no protection, simultaneous treatment with ligands for TLR2/6 and TLR9 conferred robust, synergistic protection against virulent Gram-positive and Gram-negative pathogens. Protection was associated with rapid pathogen killing in the lungs, and pathogen killing could be induced from lung epithelial cells in isolation. Taken together, these data demonstrate the requirement for TLRs in inducible resistance against pneumonia, reveal a remarkable, unanticipated synergistic interaction of TLR2/6 and TLR9, reinforce the emerging evidence supporting the antimicrobial capacity of the lung epithelium, and may provide the basis for a novel clinical therapeutic that can protect patients against pneumonia during periods of peak vulnerability.
PMCID: PMC3654378  PMID: 21482737
24.  Configuring a Context-Aware Middleware for Wireless Sensor Networks 
Sensors (Basel, Switzerland)  2012;12(7):8544-8570.
In the Future Internet, applications based on Wireless Sensor Networks will have to support reconfiguration with minimum human intervention, depending on dynamic context changes in their environment. These situations create a need for building these applications as adaptive software and including techniques that allow the context acquisition and decisions about adaptation. However, contexts use to be made up of complex information acquired from heterogeneous devices and user characteristics, making them difficult to manage. So, instead of building context-aware applications from scratch, we propose to use FamiWare, a family of middleware for Ambient Intelligence specifically designed to be aware of contexts in sensor and smartphone devices. It provides both, several monitoring services to acquire contexts from devices and users, and a context-awareness service to analyze and detect context changes. However, the current version of FamiWare does not allow the automatic incorporation related to the management of new contexts into the FamiWare family. To overcome this shortcoming, in this work, we first present how to model the context using a metamodel to define the contexts that must to be taken into account in an instantiation of FamiWare for a certain Ambient Intelligence system. Then, to configure a new context-aware version of FamiWare and to generate code ready-to-install within heterogeneous devices, we define a mapping that automatically transforms metamodel elements defining contexts into elements of the FamiWare family, and we also use the FamiWare configuration process to customize the new context-aware variant. Finally, we evaluate the benefits of our process, and we analyze both that the new version of the middleware works as expected and that it manages the contexts in an efficient way.
PMCID: PMC3444063  PMID: 23012505
context-aware; WSN; middleware; model-driven; configuration; AmI; AAL
25.  A Novel Latin Hypercube Algorithm via Translational Propagation 
The Scientific World Journal  2014;2014:163949.
Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is directly related to the experimental designs used. Optimal Latin hypercube designs are frequently used and have been shown to have good space-filling and projective properties. However, the high cost in constructing them limits their use. In this paper, a methodology for creating novel Latin hypercube designs via translational propagation and successive local enumeration algorithm (TPSLE) is developed without using formal optimization. TPSLE algorithm is based on the inspiration that a near optimal Latin Hypercube design can be constructed by a simple initial block with a few points generated by algorithm SLE as a building block. In fact, TPSLE algorithm offers a balanced trade-off between the efficiency and sampling performance. The proposed algorithm is compared to two existing algorithms and is found to be much more efficient in terms of the computation time and has acceptable space-filling and projective properties.
PMCID: PMC4167653  PMID: 25276844

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