Inferring fungal growth rates from optical density data

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration.

A Holling Functional Response Model for Mapping QTLs Governing Interspecific Interactions

Genes play an important role in community ecology and evolution, but how to identify the genes that affect community dynamics at the whole genome level is very challenging. Here, we develop a Holling type II functional response model for mapping quantitative trait loci (QTLs) that govern interspecific interactions. The model, integrated with generalized Lotka-Volterra differential dynamic equations, shows a better capacity to reveal the dynamic complexity of inter-species interactions than classic competition models. By applying the new model to a published mapping data from a competition experiment of two microbial species, we identify a set of previously uncharacterized QTLs that are specifically responsible for microbial cooperation and competition. The model can not only characterize how these QTLs affect microbial interactions, but also address how change in ecological interactions activates the genetic effects of the QTLs. This model provides a quantitative means of predicting the genetic architecture that shapes the dynamic behavior of ecological communities.

Bayesian modeling of two- and three-species bacterial competition in milk

Listeria monocytogenes is a well-known food-borne pathogen and is among the bacteria best adapted to grow at low temperatures. Psychrotrophic spoilage microorganisms present in milk and milk products are primarily in the genus Pseudomonas, and their numbers increase during cold storage leading to deterioration and/or spoilage. The nature of the competition in two- or three-species bacterial systems with L. monocytogenes, L. innocua, and P. fluorescens in skimmed milk at 7 or 14°C was studied. The Baranyi growth model was used to estimate the growth rate and the maximum population density of the three microorganisms for each strain in single cultures or in two- or three-strains co-cultures. The highest Listeria populations were achieved by pure cultures, decreasing in co-culture with P. fluorescens at both temperatures. A modified deterministic logistic model was applied which includes inhibition functions for single cultures, and two- or three-species cultures. A subsequent Bayesian approach was applied for modelling the bacterial interactions. There was not a direct correlation between the growth rate of P. fluorescens and its inhibitory effect on Listeria species. The use of some species from the natural food microflora to inhibit pathogen growth may be an important tool to enhance the safety of refrigerated foods such as milk and dairy products.