Microrisk Lab: An Online Freeware for Predictive Microbiology

Abstract

Microrisk Lab is an R-based online modeling freeware designed to realize parameter estimation and model simulation in predictive microbiology. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/inactivation under static and nonisothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/inactivation under static and nonisothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate data set and/or parameter setting. In this study, six case studies were reproduced in Microrisk Lab and compared in parallel with DMFit, GInaFiT, IPMP 2013/GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling systems. Microrisk Lab allows for a friendly user experience when modeling microbial behaviors owing to its interactive interfaces, high integration, and interconnectivity. Users can freely access this application at https:

The Influence of Liquid Medium Choice in Determination of Minimum Inhibitory Concentration of Essential Oils against Pathogenic Bacteria

“…Growth curves (OD values) were fitted in Microrisk Lab online predictor v1.2 [ 24 ] using primary growth model of Baranyi as the best fit [ 25 ] in order to compute maximum specific growth rate (μ max ) and lag phase duration (λ).…”

gcplyr: an R package for microbial growth curve data analysis

“…Additionally, gcplyr improves on existing computational tools by providing an expanded array of traits that can be quantified using model-free and non-parametric approaches. Previous tools had fit parametric mathematical models of microbial growth to observed data (2,3,5,7,9,(11)(12)(13)(14)(15)(16)(17)(18)(19), an approach that requires validation of model assumptions and fails when data do not fit the chosen model. Instead, gcplyr and some recent tools quantify traits directly from the data itself (2,4,6,8,10,(13)(14)(15)(17)(18)(19)(20)(21)(22)(23)(24).…”

“…Typically, plate readers measure the optical density of a microbial culture, which corresponds to the density of the population. To convert optical density measures over time into a quantitative microbial trait, many groups have developed software with graphical user interfaces (Table S1, (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)). Graphical user interfaces make the tools easy to use, with little or no programming.…”

“…In addition, many of the most popular computational tools use parametric analyses of microbial growth curve data (Table S1, (2,3,5,7,9,(11)(12)(13)(14)(15)(16)(17)(18)(19)). Parametric analyses fit a mathematical model of microbial population growth to observed data, then extract the fitted parameter values to quantify traits.…”

Challenges and opportunities related to the use of innovative modelling approaches and tools for microbiological food safety management

“…In addition to simulation tools, model fitting applications for estimating kinetic parameters of microbial growth and inactivation were developed as Excel Add-ins, e.g., DMFit, GInaFiT, iPMP. More recently, R-Shiny based applications, accessible on-line with advanced features regarding modelling options, including stochastic modules, statistical indicators and usability have been developed, e.g., Bioinactivation [11], Microrisk Lab [12] or Biogrowth (Table 1). Tools to carry out quantitative microbiological risk assessment for multiple hazards in relation to multiple foods are available (e.g.…”