Identifying density-dependent interactions in collective cell behaviour

Browning, Alexander P; Wang, Jin; Plank, Michael J.; Simpson, Matthew J.

doi:10.1101/811257

Cited by 4 publications

(22 citation statements)

References 54 publications

(89 reference statements)

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“…In fact, the posterior distributions for the CDH5 and CDC42 knockdowns have support on negative values of p – d , which is consistent with the fact that some of the CDH5 and CDC42 knockdown assays show a decrease in the number of cells over the course of the experiment. In line with previous inference performed on this mechanistic model of density-dependent cell movement and proliferation, the parameters controlling density-dependent proliferation, γ p , and the strength of density-dependent interactions, γ b , are not conclusively identified from the data [16].…”

Section: Resultsmentioning

confidence: 52%

“…Changes in cell counts are a simple summary statistic to extract from scratch assay data [16, 19, 20] and provide an indication of the average net proliferation rate in a large population of cells, such as the cell monolayer used for scratch assays. However, computing an average net proliferation rate does not reflect more complex mechanisms, such as contact inhibition, that may influence the change in cell numbers throughout the experiment.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, we use a well-established stochastic, lattice-free IBM developed by Binny et al [17] to model density-dependent cell migration, proliferation, and death. This model has previously been shown by Browning et al [16] to accurately represent collective cell migration in a wound healing assay experiment. Density-dependent behaviour in the model is incorporated through cell-to-cell interactions, so that behaviour depends on local crowding [16].…”

Section: Methods and Modelmentioning

confidence: 98%

“…To capture population size increase, we use the population size, N ( t ), at t = 24 h . To capture spatial structure, we use the pair correlation function, P ( r, t ), which describes the density of pairs of agents separated by a distance r at time t , relative to the expected density of pairs if the population were uniformly distributed [16]. For our discrete data, the pair correlation function describes the relative number of pairs separated by distances in the range ( j – 1)Δ r < r < j Δ r .…”

Section: Methods and Modelmentioning

confidence: 99%

“…By using Bayes' rule, the prior is updated with data, which in this work consists of the repeated measurements of cell centre locations at t = 0h and t = 24h. In this work, we choose independent uniform priors for each of the parameters, and we let the prior ranges be informed by the findings of Browning et al [16] so that m ∼ U(0,…”

Section: Bayesian Statistics: Connecting Models and Datamentioning

confidence: 99%

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Efficient Bayesian inference for mechanistic modelling with high-throughput data

Martina-Perez

Sailem

Baker

2022

Preprint

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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. Moreover, 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 \textit{minibatch} approach to approximate Bayesian computation. Through a case study of a high-throughput imaging scratch assay experiment, we show that reliable inference can be performed at a fraction of the computational cost of a traditional Bayesian inference scheme. By applying a detailed mathematical model of single cell motility, proliferation and death to a data set of 118 gene knockdowns, we characterise functional subgroups of gene knockdowns, each displaying its own typical combination of local cell density-dependent and -independent motility 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 process of wound healing.

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Section: Resultsmentioning

confidence: 52%

Section: Resultsmentioning

confidence: 99%

Section: Methods and Modelmentioning

confidence: 98%

Section: Methods and Modelmentioning

confidence: 99%

Section: Bayesian Statistics: Connecting Models and Datamentioning

confidence: 99%

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Efficient Bayesian inference for mechanistic modelling with high-throughput data

Martina-Perez

Sailem

Baker

2022

Preprint

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Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population

Messenger

Wheeler

Liu

et al. 2022

J. R. Soc. Interface.

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Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it is challenging to infer the interaction rules directly from data. In the field of equation discovery, the weak-form sparse identification of nonlinear dynamics (WSINDy) methodology has been shown to be computationally efficient for identifying the governing equations of complex systems from noisy data. Motivated by the success of IPS models to describe the spatial movement of organisms, we develop WSINDy for the second-order IPS to learn equations for communities of cells. Our approach learns the directional interaction rules for each individual cell that in aggregate govern the dynamics of a heterogeneous population of migrating cells. To sort a cell according to the active classes present in its model, we also develop a novel ad hoc classification scheme (which accounts for the fact that some cells do not have enough evidence to accurately infer a model). Aggregated models are then constructed hierarchically to simultaneously identify different species of cells present in the population and determine best-fit models for each species. We demonstrate the efficiency and proficiency of the method on several test scenarios, motivated by common cell migration experiments.

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Small-scale spatial structure affects predator-prey dynamics and coexistence

Surendran

Plank

Simpson

2019

Preprint

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Short-range neighbour-dependent interactions and small-scale spatial structure are thought to affect community dynamics in many ecological and biological processes, such as predator-prey dynamics and immune responses. Yet, a large class of mathematical models aimed at representing these processes ignores these factors by making a classical mean-field approximation, where interactions between individuals are assumed to occur in proportion to their average density. Such mean-field approximations amount to ignoring spatial structure. In this work, we consider an individual based model of a two-species community that is composed of consumers and resources. The model describes migration, predation, competition and dispersal of offspring, and explicitly gives rise to varying degrees of spatial structure. We compare simulation results from the individual based model with the solution of a classical mean-field approximation, and this comparison provides insight into how spatial structure can drive the system away from mean-field dynamics. Our analysis reveals that mechanisms leading to intraspecific clustering and interspecific segregation, such as short-range predation and short-range dispersal, tend to increase the size of the resource species relative to the mean-field prediction. We show that under certain parameter regimes these mechanisms lead to the extinction of consumers whereas the classical mean-field model predicts the coexistence of both species.Keywords Spatial patterns, individual-based model, prey-predator dynamics, competition, mean-field.

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Identifying density-dependent interactions in collective cell behaviour

Cited by 4 publications

References 54 publications

Efficient Bayesian inference for mechanistic modelling with high-throughput data

Efficient Bayesian inference for mechanistic modelling with high-throughput data

Learning anisotropic interaction rules from individual trajectories in a heterogeneous cellular population

Small-scale spatial structure affects predator-prey dynamics and coexistence

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