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

Robert Ross, Ruth E. Baker, Andrew Parker, Matthew Ford, Richard Mort, Christian Yates

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.
LanguageEnglish
Article number9
Journalnpj Systems Biology and Applications
Volume3
Early online date10 Jan 2017
DOIs
StatusPublished - 1 Mar 2017

Fingerprint

adhesion
cells
locomotion
wound healing
statistics
interactions
costs

Keywords

  • Approximate bayesian computation
  • cell migration
  • scratch assay

Cite this

Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process. / Ross, Robert; Baker, Ruth E.; Parker, Andrew ; Ford, Matthew; Mort, Richard; Yates, Christian.

In: npj Systems Biology and Applications, Vol. 3, 9, 01.03.2017.

Research output: Contribution to journalArticle

@article{5960ca743097492187945a5729fb676f,
title = "Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process",
abstract = "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.",
keywords = "Approximate bayesian computation, cell migration, scratch assay",
author = "Robert Ross and Baker, {Ruth E.} and Andrew Parker and Matthew Ford and Richard Mort and Christian Yates",
year = "2017",
month = "3",
day = "1",
doi = "10.1038/s41540-017-0010-7",
language = "English",
volume = "3",
journal = "npj Systems Biology and Applications",
issn = "2056-7189",
publisher = "Nature Publishing Group",

}

TY - JOUR

T1 - Using approximate Bayesian computation to quantify cell-cell adhesion parameters in a cell migratory process

AU - Ross, Robert

AU - Baker, Ruth E.

AU - Parker, Andrew

AU - Ford, Matthew

AU - Mort, Richard

AU - Yates, Christian

PY - 2017/3/1

Y1 - 2017/3/1

N2 - 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.

AB - 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.

KW - Approximate bayesian computation

KW - cell migration

KW - scratch assay

U2 - 10.1038/s41540-017-0010-7

DO - 10.1038/s41540-017-0010-7

M3 - Article

VL - 3

JO - npj Systems Biology and Applications

T2 - npj Systems Biology and Applications

JF - npj Systems Biology and Applications

SN - 2056-7189

M1 - 9

ER -