Data–driven modelling makes quantitative predictions regarding bacteria surface motility

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.Author summary: Twitching is a type of bacterial surface motion facilitated by molecular motor-driven micron-scale filaments known as type IV pili (TFP). The resulting motion, which is characterised by erratic behaviour at short timescales, is important for bacterial surface invasion, biofilm formation etc. The link between microscopic mechanisms on the level of single filaments and macroscopic properties of twitching trajectories is generally difficult to make, since direct time-resolved imaging is of TFP in experiments is challenging. We propose a data-driven model for surface motility of the bacteria P. Aeruginosa that bridges these scales: we resolve single-TFP dynamics and connect it to the macroscopic properties of twitching trajectories. We quantitatively compare our simulations to experimental tracking data, predict the previously unresolved distribution of TFP on bacterial membrane, and discuss how it affects the transitions between the “walking” and “crawling” modes of motion. We also extract the speed of TFP retraction corresponding to the specific experimental conditions, which is lower than reported in several other experiments, but in perfect agreement with measurements performed at conditions of our tracking data. Our work thus elucidates microscopic mechanism of twitching, and it represents a systematic data-driven approach to quantitative modelling of complex biological phenomena.