Approximate Bayesian computation reveals the importance of repeated measurements for parameterising cell-based models of growing tissues. (14th April 2018) - Kursawe, Jochen; Baker, Ruth E.; Fletcher, Alexander G.
Highlights: Parameters of cell mechanics are inferred for a vertex model of epithelial monolayers. Inference is conducted on in silico data on growth in Drosophila wing imaginal discs. Summary statistics of cellular packing and from laser ablations are compared. Summary statistics that enable accurate inference of model parameters are identified. Parameter uncertainty depends on the amount of data. Abstract: The growth and dynamics of epithelial tissues govern many morphogenetic processes in embryonic development. A recent quantitative transition in data acquisition, facilitated by advances in genetic and live-imaging techniques, is paving the way for new insights to these processes. Computational models can help us understand and interpret observations, and then make predictions for future experiments that can distinguish between hypothesised mechanisms. Increasingly, cell-based modelling approaches such as vertex models are being used to help understand the mechanics underlying epithelial morphogenesis. These models typically seek to reproduce qualitative phenomena, such as cell sorting or tissue buckling. However, it remains unclear to what extent quantitative data can be used to constrain these models so that they can then be used to make quantitative, experimentally testable predictions. To address this issue, we perform an in silico study to investigate whether vertex model parameters can be inferred from imaging data, and explore methods to quantify the uncertainty of such estimates. Our approach requires the use of summary statistics to estimate parameters. Here, we focus on summary statistics of cellular packing and of laser ablation experiments, as are commonly reported from imaging studies. We find that including data from repeated experiments is necessary to generate reliable parameter estimates that can facilitate quantitative model predictions.