Dataset Information

RMut: R package for a Boolean sensitivity analysis against various types of mutations.

ABSTRACT: There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel user-defined mutations. In addition, RMut can specify the mutation area and the duration time for more precise analysis. RMut can be used to analyze large-scale networks because it is implemented in a parallel algorithm using the OpenCL library. In the first case study, we observed that the real biological networks were most sensitive to overexpression/state-flip and edge-addition/-reverse mutations among node-based and edgetic mutations, respectively. In the second case study, we showed that edgetic mutations can predict drug-targets better than node-based mutations. Finally, we examined the network sensitivity to double edge-removal mutations and found an interesting synergistic effect. Taken together, these findings indicate that RMut is a flexible R package to efficiently analyze network sensitivity to various types of mutations. RMut is available at https://github.com/csclab/RMut.

SUBMITTER: Trinh HC

PROVIDER: S-EPMC6424452 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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RMut: R package for a Boolean sensitivity analysis against various types of mutations.

Trinh Hung-Cuong HC Kwon Yung-Keun YK

PloS one 20190319 3

There have been many in silico studies based on a Boolean network model to investigate network sensitivity against gene or interaction mutations. However, there are no proper tools to examine the network sensitivity against many different types of mutations, including user-defined ones. To address this issue, we developed RMut, which is an R package to analyze the Boolean network-based sensitivity by efficiently employing not only many well-known node-based and edgetic mutations but also novel u ...[more]

PMID: 30889216

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Project description:BackgroundSolutions to stochastic Boolean models are usually estimated by Monte Carlo simulations, but as the state space of these models can be enormous, there is an inherent uncertainty about the accuracy of Monte Carlo estimates and whether simulations have reached all attractors. Moreover, these models have timescale parameters (transition rates) that the probability values of stationary solutions depend on in complex ways, raising the necessity of parameter sensitivity analysis. We address these two issues by an exact calculation method for this class of models.ResultsWe show that the stationary probability values of the attractors of stochastic (asynchronous) continuous time Boolean models can be exactly calculated. The calculation does not require Monte Carlo simulations, instead it uses graph theoretical and matrix calculation methods previously applied in the context of chemical kinetics. In this version of the asynchronous updating framework the states of a logical model define a continuous time Markov chain and for a given initial condition the stationary solution is fully defined by the right and left nullspace of the master equation's kinetic matrix. We use topological sorting of the state transition graph and the dependencies between the nullspaces and the kinetic matrix to derive the stationary solution without simulations. We apply this calculation to several published Boolean models to analyze the under-explored question of the effect of transition rates on the stationary solutions and show they can be sensitive to parameter changes. The analysis distinguishes processes robust or, alternatively, sensitive to parameter values, providing both methodological and biological insights.ConclusionUp to an intermediate size (the biggest model analyzed is 23 nodes) stochastic Boolean models can be efficiently solved by an exact matrix method, without using Monte Carlo simulations. Sensitivity analysis with respect to the model's timescale parameters often reveals a small subset of all parameters that primarily determine the stationary probability of attractor states.

Project description:The development of antibiotic resistance is a major public health issue, as infections are increasingly unresponsive to antibiotics. Emerging antimicrobial resistance has raised researchers' interest in the development of alternative strategies using natural compounds with antibacterial activity, like honey, which has emerged as an agent to treat several infections and wound injuries. Nevertheless, the antibacterial effect of honey was mostly evaluated against Gram-positive bacteria. Hence, the objective of our study was to evaluate the antibacterial activity, as well as the physicochemical parameters, of genuine Greek honeys against multidrug-resistant Gram-negative pathogenic bacteria. In this vein, we aimed to study the in vitro antibacterial potential of rare Greek honeys against Verona integron-encoded metallo-β-lactamase (VIM)- or Klebsiella pneumoniae carbapenemase-producing multidrug-resistant Gram-negative pathogens. Physicochemical parameters such as pH, hydrogen peroxide, free acidity, lactonic acid, total phenols total flavonoids, free radical scavenging activities, tyrosinase enzyme inhibitory activity and kojic acid were examined. Moreover, the antimicrobial activity of 10 different honey types was evaluated in five consecutive dilutions (75%, 50%, 25%, 12.5% and 6.25%) against the clinical isolates by the well diffusion method, as well as by the determination of the minimum inhibition concentration after the addition of catalase and protease. Almost all the physicochemical parameters varied significantly among the different honeys. Fir and manuka honey showed the highest values in pH and H2O2, while the free acidity and lactonic acid levels were higher in chestnut honey. Total phenols, total flavonoids and free radical scavenging activities were found higher in cotton, arbutus and manuka honey, and finally, manuka and oregano honeys showed higher tyrosinase inhibition activity and kojic acid levels. The antimicrobial susceptibility depended on the type of honey, on its dilution, on the treatment methodology and on the microorganism. Arbutus honey was the most potent against VIM-producing Enterobacter cloacae subsp. dissolvens in 75% concentration, while fir honey was more lethal for the same microorganism in the 25% concentration. Many honeys outperformed manuka honey in their antibacterial potency. It is of interest that, for any given concentration in the well diffusion method and for any given type of honey, significant differences were not detected among the four multidrug-resistant pathogens, which explains that the damaging effect to the bacterial cells was the same regardless of the bacterial species or strain. Although the antimicrobial potency of different honey varieties dependents on their geographical origin and on their compositional differences, the exact underlying mechanism remains yet unclear.

Project description:BackgroundThe importance of crescent formation in glomerulonephritis has increased. However, detailed analysis of crescentic glomerulonephritis in Asia is scarce. In addition, advances in serological diagnostic techniques (antineutrophil cytoplasmic and antiglomerular basement membrane autoantibodies) and early diagnosis have reduced the number of cases meeting the strict definition of crescentic glomerulonephritis (>50% of glomeruli are crescentic). Therefore, we analyzed the clinicopathological features and renal prognosis of glomerulonephritis cases that exhibited at least one crescentic lesion.MethodsWe retrospectively evaluated 265 adult patients diagnosed with glomerulonephritis with at least one crescent formation based on the results of renal biopsy. We divided the patients into two groups based on the four types of glomerulonephritis, namely, the immune-complex (type II: IgA nephropathy, IgA vasculitis with nephritis, and lupus nephritis) and pauci-immune (type III: microscopic polyangiitis) groups. Factors affecting renal prognosis (end-stage renal failure requiring renal replacement therapy) were examined in a multivariate analysis using the Cox proportional hazards model. Kaplan-Meier curves and log-rank test were used to analyze and compare time from entry to renal death.ResultsRenal prognosis differed significantly between the immune-complex and pauci-immune groups. Among the four types of glomerulonephritis, IgA nephropathy was the most prevalent. Multivariate analysis showed that renal function at renal biopsy and the ratio of global sclerosis independently predicted renal prognosis, but the type of glomerulonephritis was not a factor.ConclusionsRenal dysfunction at renal biopsy and the ratio of global sclerosis predicted renal prognosis, because it reflects the degree of irreversible renal damage. We also suspect that the formation of at least one crescentic lesion led to the development of these predictive factors, regardless of the type of glomerular disease and degree of crescent formation.

Dataset Information

RMut: R package for a Boolean sensitivity analysis against various types of mutations.

Publications

RMut: R package for a Boolean sensitivity analysis against various types of mutations.

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