Data–driven modelling makes quantitative predictions regarding bacteria surface motility

• Published in: PLoS computational biology Vol. 20; no. 5; p. e1012063
• Main Authors: Barton, Daniel L., Chang, Yow-Ren, Ducker, William, Dobnikar, Jure
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.05.2024; Public Library of Science (PLoS)
• Subjects: Analysis; Bacteria; Bayes Theorem; Bayesian analysis; Computational Biology; Computer Simulation; Computer-generated environments; Estimates; Fimbriae, Bacterial - physiology; Mathematical models; Microscopy; Models, Biological; Motility; Movement - physiology; Parameter sensitivity; Pseudomonas aeruginosa; Pseudomonas aeruginosa - physiology; Sensitivity analysis; Testing; Tracking; Twitching; United States
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1012063

In this work, we quantitatively compare computer simulations and existing cell tracking data of P. aeruginosa surface motility in order to analyse the underlying motility mechanism. We present a three dimensional twitching motility model, that simulates the extension, retraction and surface association of individual Type IV Pili (TFP), and is informed by recent experimental observations of TFP. Sensitivity analysis is implemented to minimise the number of model parameters, and quantitative estimates for the remaining parameters are inferred from tracking data by approximate Bayesian computation. We argue that the motility mechanism is highly sensitive to experimental conditions. We predict a TFP retraction speed for the tracking data we study that is in a good agreement with experimental results obtained under very similar conditions. Furthermore, we examine whether estimates for biologically important parameters, whose direct experimental determination is challenging, can be inferred directly from tracking data. One example is the width of the distribution of TFP on the bacteria body. We predict that the TFP are broadly distributed over the bacteria pole in both walking and crawling motility types. Moreover, we identified specific configurations of TFP that lead to transitions between walking and crawling states.