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Efficient Bayesian inference for mechanistic modelling with high-throughput data

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MMVW02 - Collective Behaviour

Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. While high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-throughput scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional approximate Bayesian inference scheme. By applying a detailed mathematical model of cell movement, proliferation, and death to a wide range of gene knockdowns, we characterise the relative contributions of local cell density-dependent and -independent mechanisms of cell movement and proliferation. Within a screen of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and independent movement and proliferation patterns. By comparing these patterns to experimental measurements of cell counts and wound closure, we find that density-dependent interactions play a crucial role in the outcome of the scratch assay.

This talk is part of the Isaac Newton Institute Seminar Series series.

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