Inference of cell–cell interactions from population density characteristics and cell trajectories on static and growing domains

Ross, Robert; Yates, Christian A.; Baker, Ruth E.

doi:10.1016/j.mbs.2015.04.002

Cited by 16 publications

(16 citation statements)

References 44 publications

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“…It has previously been shown that different cell–cell interactions have different effects on the average displacement of agents in an ABM. 36 As may be expected, repulsive (adhesive) interactions between agents tend to increase (decrease) the average displacement of agents, and so the average displacement of agents may be a useful summary statistic for distinguishing between repulsive and adhesive cell–cell interactions in the ABM.…”

Section: Methodsmentioning

confidence: 94%

“…We employ two models to simulate cell–cell interactions in the ABM, one of which has been published before. 13, 36 We define T ( v ′| v ) as the transition probability that an agent situated at site v , having been selected to move, attempts to move to site v ′, where v ′ indicates one of the nearest neighbour sites of v . Therefore, T ( v ′| v ) is only non-zero if v and v ′ are nearest neighbours.…”

Section: Methodsmentioning

confidence: 99%

“…The transition probability in the second model, which we refer to as model B, 13, 36 is defined asand must satisfy …”

Section: Methodsmentioning

confidence: 99%

“…9, 36, 37 Our aim is to ascertain which summary statistic (or combination of summary statistics) is most effective for the identification of agent motility and adhesion parameters in the ABM.…”

Section: Methodsmentioning

confidence: 99%

“…We have shown previously that different cell–cell interactions have different effects on the agent density profile. 36 For instance, repulsive interactions between agents can create a concave agent density profile, whereas adhesive interactions between agents can create a convex agent density profile. Therefore, the agent density profile may be an effective summary statistic for distinguishing between repulsive and adhesive cell–cell interactions in the ABM.…”

Section: Methodsmentioning

confidence: 99%

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Using approximate Bayesian computation to quantify cell–cell adhesion parameters in a cell migratory process

et al. 2017

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In this work, we implement approximate Bayesian computational methods to improve the design of a wound-healing assay used to quantify cell–cell interactions. This is important as cell–cell interactions, such as adhesion and repulsion, have been shown to play a role in cell migration. Initially, we demonstrate with a model of an unrealistic experiment that we are able to identify model parameters that describe agent motility and adhesion, given we choose appropriate summary statistics for our model data. Following this, we replace our model of an unrealistic experiment with a model representative of a practically realisable experiment. We demonstrate that, given the current (and commonly used) experimental set-up, our model parameters cannot be accurately identified using approximate Bayesian computation methods. We compare new experimental designs through simulation, and show more accurate identification of model parameters is possible by expanding the size of the domain upon which the experiment is performed, as opposed to increasing the number of experimental replicates. The results presented in this work, therefore, describe time and cost-saving alterations for a commonly performed experiment for identifying cell motility parameters. Moreover, this work will be of interest to those concerned with performing experiments that allow for the accurate identification of parameters governing cell migratory processes, especially cell migratory processes in which cell–cell adhesion or repulsion are known to play a significant role.

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Section: Methodsmentioning

confidence: 94%

Section: Methodsmentioning

confidence: 99%

“…The transition probability in the second model, which we refer to as model B, 13, 36 is defined asand must satisfy …”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Using approximate Bayesian computation to quantify cell–cell adhesion parameters in a cell migratory process

et al. 2017

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The impact of exclusion processes on angiogenesis models

2018

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Angiogenesis is the process by which new blood vessels form from existing vessels. During angiogenesis, tip cells migrate via diffusion and chemotaxis, new tip cells are introduced through branching, loops form via tip-to-tip and tip-to-sprout anastomosis, and a vessel network forms as endothelial cells, known as stalk cells, follow the paths of tip cells (a process known as the snail-trail). Using a mean-field approximation, we systematically derive one-dimensional non-linear continuum models from a lattice-based cellular automaton model of angiogenesis in the corneal assay, explicitly accounting for cell volume. We compare our continuum models and a well-known phenomenological snail-trail model that is linear in the diffusive, chemotactic and branching terms, with averaged cellular automaton simulation results to distinguish macroscale volume exclusion effects and determine whether linear models can capture them. We conclude that, in general, both linear and non-linear models can be used at low cell densities when single or multi-species exclusion effects are negligible at the macroscale. When cell densities increase, our non-linear model should be used to capture non-linear tip cell behavior that occurs when single-species exclusion effects are pronounced, and alternative models should be derived for non-negligible multi-species exclusion effects.

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Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation

Wang,

Jenner,

Salomone

et al. 2024

J. Math. Biol.

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Agent-based models (ABMs) are readily used to capture the stochasticity in tumour evolution; however, these models are often challenging to validate with experimental measurements due to model complexity. The Voronoi cell-based model (VCBM) is an off-lattice agent-based model that captures individual cell shapes using a Voronoi tessellation and mimics the evolution of cancer cell proliferation and movement. Evidence suggests tumours can exhibit biphasic growth in vivo. To account for this phenomena, we extend the VCBM to capture the existence of two distinct growth phases. Prior work primarily focused on point estimation for the parameters without consideration of estimating uncertainty. In this paper, approximate Bayesian computation is employed to calibrate the model to in vivo measurements of breast, ovarian and pancreatic cancer. Our approach involves estimating the distribution of parameters that govern cancer cell proliferation and recovering outputs that match the experimental data. Our results show that the VCBM, and its biphasic extension, provides insight into tumour growth and quantifies uncertainty in the switching time between the two phases of the biphasic growth model. We find this approach enables precise estimates for the time taken for a daughter cell to become a mature cell. This allows us to propose future refinements to the model to improve accuracy, whilst also making conclusions about the differences in cancer cell characteristics.

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Inference of cell–cell interactions from population density characteristics and cell trajectories on static and growing domains

Cited by 16 publications

References 44 publications

Using approximate Bayesian computation to quantify cell–cell adhesion parameters in a cell migratory process

Using approximate Bayesian computation to quantify cell–cell adhesion parameters in a cell migratory process

The impact of exclusion processes on angiogenesis models

Calibration of agent based models for monophasic and biphasic tumour growth using approximate Bayesian computation

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