Many cell types form clumps or aggregates when cultured in vitro through a variety of mechanisms including rapid cell proliferation, chemotaxis, or direct cell-to-cell contact. In this paper we develop an agent-based model to explore the formation of aggregates in cultures where cells are initially distributed uniformly, at random, on a two-dimensional substrate. Our model includes unbiased random cell motion, together with two mechanisms which can produce cell aggregates: (i) rapid cell proliferation, and (ii) a biased cell motility mechanism where cells can sense other cells within a finite range, and will tend to move towards areas with higher numbers of cells. We then introduce a pair-correlation function which allows us to quantify aspects of the spatial patterns produced by our agent-based model. In particular, these pair-correlation functions are able to detect differences between domains populated uniformly at random (i.e. at the exclusion complete spatial randomness (ECSR) state) and those where the proliferation and biased motion rules have been employed -even when such differences are not obvious to the naked eye. The pair-correlation function can also detect the emergence of a characteristic inter-aggregate distance which occurs when the biased motion mechanism is dominant, and is not observed when cell proliferation is the main mechanism of aggregate formation. This suggests that applying the pair-correlation function to experimental images of cell aggregates may provide information about the mechanism associated with observed aggregates. As a proof of concept, we perform such analysis for images of cancer cell aggregates, which are known to be associated with rapid proliferation. The results of our analysis are consistent with the predictions of the proliferation-based simulations, which supports the potential usefulness of pair correlation functions for providing insight into the mechanisms of aggregate formation.

Engineered phage with properties optimised for the treatment of bacterial infections hold great promise, but require careful characterisation by a number of approaches. Phage–bacteria infection time courses, where populations of bacteriophage and bacteria are mixed and followed over many infection cycles, can be used to deduce properties of phage infection at the individual cell level. Here, we apply this approach to analysis of infection of Escherichia coli by the temperate bacteriophage 186 and explore which properties of the infection process can be reliably inferred. By applying established modelling methods to such data, we extract the frequency at which phage 186 chooses the lysogenic pathway after infection, and show that lysogenisation increases in a graded manner with increased expression of the lysogenic establishment factor CII. The data also suggest that, like phage λ, the rate of lysogeny of phage 186 increases with multiple infections.