Quantifying the effect of experimental design choices for in vitro scratch assays

An Approximate Bayesian Computation Approach for Embryonic Astrocyte Migration Model Reduction

During embryonic development of the retina of the eye, astrocytes, a type of glial cell, migrate over the retinal surface and form a dynamic mesh. This mesh then serves as scaffolding for blood vessels to form the retinal vasculature network that supplies oxygen and nutrients to the inner portion of the retina. Astrocyte spreading proceeds in a radially symmetric manner over the retinal surface. Additionally, astrocytes mature from astrocyte precursor cells (APCs) to immature perinatal astrocytes (IPAs) during this embryonic stage. We extend a previously-developed continuum model that describes tension-driven migration and oxygen and growth factor influenced proliferation and differentiation. Comparing numerical simulations to experimental data, we identify model equation components that can be removed via model reduction using approximate Bayesian computation (ABC). Our results verify experimental studies indicating that the choroid oxygen supply plays a negligible role in promoting differentiation of APCs into IPAs and in promoting IPA proliferation, and the hyaloid artery oxygen supply and APC apoptosis play negligible roles in astrocyte spreading and differentiation.

Modelling count data with partial differential equation models in biology

Partial differential equation (PDE) models are often used to study biological phenomena involving movement-birth-death processes, including ecological population dynamics and the invasion of populations of biological cells. Count data, by definition, is non-negative, and count data relating to biological populations is often bounded above by some carrying capacity that arises through biological competition for space or nutrients. Parameter estimation, parameter identifiability, and making model predictions usually involves working with a measurement error model that explicitly relating experimental measurements with the solution of a mathematical model. In many biological applications, a typical approach is to assume the data are normally distributed about the solution of the mathematical model. Despite the widespread use of the standard additive Gaussian measurement error model, the assumptions inherent in this approach are rarely explicitly considered or compared with other options. Here, we interpret scratch assay data, involving migration, proliferation and delays in a population of cancer cells using a reaction-diffusion PDE model. We consider relating experimental measurements to the PDE solution using a standard additive Gaussian measurement error model alongside a comparison to a more biologically realistic binomial measurement error model. While estimates of model parameters are relatively insensitive to the choice of measurement error model, model predictions for data realisations are very sensitive. The standard additive Gaussian measurement error model leads to biologically inconsistent predictions, such as negative counts and counts that exceed the carrying capacity across a relatively large spatial region within the experiment. Furthermore, the standard additive Gaussian measurement error model requires estimating an additional parameter compared to the binomial measurement error model. In contrast, the binomial measurement error model leads to biologically plausible predictions and is simpler to implement. We provide open source Julia software on GitHub to replicate all calculations in this work, and we explain how to generalise our approach to deal with coupled PDE models with several dependent variables through a multinomial measurement error model, as well as pointing out other potential generalisations by linking our work with established practices in the field of generalised linear models.

Isolation of phenolic compounds from eco-friendly white bee propolis: Antioxidant, wound-healing, and anti-Alzheimer effects

This study presents the first findings regarding extraction, isolation, enzyme inhibition, and antioxidant activity. The oral mucosal wound-healing process was investigated using propolis water extract (PWE) incubation with gingival fibroblast cells and concluded that propolis was effective on the oral mucosal wound-healing pattern compared to untreated controls. Additionally, phenolic compounds (fraxetin, apigenin, galangin, pinobanksin, chrysin, etc.) were isolated from propolis, and their chemical structures were elucidated using comprehensive spectroscopic methods. The antioxidant and anti-Alzheimer potential activities of PWE and some isolated compounds were screened and revealing their inhibitory effects on acetylcholinesterase (AChE) with IC50 values ranging from 0.45 ± 0.01 to 1.15 ± 0.03 mM, as well as remarkable free-radical scavenging and metal reduction capacities. The results suggest that these compounds and PWE can be used as therapeutic agents due to their antioxidant properties and inhibitory potential on AChE. It can also be used for therapeutic purposes since its wound-healing effect is promising.

Estimating parameters of a stochastic cell invasion model with fluorescent cell cycle labelling using approximate Bayesian computation

We develop a parameter estimation method based on approximate Bayesian computation (ABC) for a stochastic cell invasion model using fluorescent cell cycle labelling with proliferation, migration and crowding effects. Previously, inference has been performed on a deterministic version of the model fitted to cell density data, and not all parameters were identifiable. Considering the stochastic model allows us to harness more features of experimental data, including cell trajectories and cell count data, which we show overcomes the parameter identifiability problem. We demonstrate that, while difficult to collect, cell trajectory data can provide more information about the parameters of the cell invasion model. To handle the intractability of the likelihood function of the stochastic model, we use an efficient ABC algorithm based on sequential Monte Carlo. Rcpp and MATLAB implementations of the simulation model and ABC algorithm used in this study are available at https://github.com/michaelcarr-stats/FUCCI.

Model-based data analysis of tissue growth in thin 3D printed scaffolds

Tissue growth in three-dimensional (3D) printed scaffolds enables exploration and control of cell behaviour in more biologically realistic geometries than that allowed by traditional 2D cell culture. Cell proliferation and migration in these experiments have yet to be explicitly characterised, limiting the ability of experimentalists to determine the effects of various experimental conditions, such as scaffold geometry, on cell behaviour. We consider tissue growth by osteoblastic cells in melt electro-written scaffolds that comprise thin square pores with sizes that were deliberately increased between experiments. We collect highly detailed temporal measurements of the average cell density, tissue coverage, and tissue geometry. To quantify tissue growth in terms of the underlying cell proliferation and migration processes, we introduce and calibrate a mechanistic mathematical model based on the Porous-Fisher reaction-diffusion equation. Parameter estimates and uncertainty quantification through profile likelihood analysis reveal consistency in the rate of cell proliferation and steady-state cell density between pore sizes. This analysis also serves as an important model verification tool: while the use of reaction-diffusion models in biology is widespread, the appropriateness of these models to describe tissue growth in 3D scaffolds has yet to be explored. We find that the Porous-Fisher model is able to capture features relating to the cell density and tissue coverage, but is not able to capture geometric features relating to the circularity of the tissue interface. Our analysis identifies two distinct stages of tissue growth, suggests several areas for model refinement, and provides guidance for future experimental work that explores tissue growth in 3D printed scaffolds.

Invading and Receding Sharp-Fronted Travelling Waves

Biological invasion, whereby populations of motile and proliferative individuals lead to moving fronts that invade vacant regions, is routinely studied using partial differential equation models based upon the classical Fisher-KPP equation. While the Fisher-KPP model and extensions have been successfully used to model a range of invasive phenomena, including ecological and cellular invasion, an often-overlooked limitation of the Fisher-KPP model is that it cannot be used to model biological recession where the spatial extent of the population decreases with time. In this work, we study the Fisher-Stefan model, which is a generalisation of the Fisher-KPP model obtained by reformulating the Fisher-KPP model as a moving boundary problem. The nondimensional Fisher-Stefan model involves just one parameter, [Formula: see text], which relates the shape of the density front at the moving boundary to the speed of the associated travelling wave, c. Using numerical simulation, phase plane and perturbation analysis, we construct approximate solutions of the Fisher-Stefan model for both slowly invading and receding travelling waves, as well as for rapidly receding travelling waves. These approximations allow us to determine the relationship between c and [Formula: see text] so that commonly reported experimental estimates of c can be used to provide estimates of the unknown parameter [Formula: see text]. Interestingly, when we reinterpret the Fisher-KPP model as a moving boundary problem, many overlooked features of the classical Fisher-KPP phase plane take on a new interpretation since travelling waves solutions with [Formula: see text] are normally disregarded. This means that our analysis of the Fisher-Stefan model has both practical value and an inherent mathematical value.

Modified Bacterial Cellulose Dressings to Treat Inflammatory Wounds

Natural products suited for prophylaxis and therapy of inflammatory diseases have gained increasing importance. These compounds could be beneficially integrated into bacterial cellulose (BC), which is a natural hydropolymer applicable as a wound dressing and drug delivery system alike. This study presents experimental outcomes for a natural anti-inflammatory product concept of boswellic acids from frankincense formulated in BC. Using esterification respectively (resp.) oxidation and subsequent coupling with phenylalanine and tryptophan, post-modification of BC was tested to facilitate lipophilic active pharmaceutical ingredient (API) incorporation. Diclofenac sodium and indomethacin were used as anti-inflammatory model drugs before the findings were transferred to boswellic acids. By acetylation of BC fibers, the loading efficiency for the more lipophilic API indomethacin and the release was increased by up to 65.6% and 25%, respectively, while no significant differences in loading could be found for the API diclofenac sodium. Post-modifications could be made while preserving biocompatibility, essential wound dressing properties and anti-inflammatory efficacy. Eventually, in vitro wound closure experiments and evaluations of the effect of secondary dressings completed the study.

Antibacterial Multi-Layered Nanocellulose-Based Patches Loaded with Dexpanthenol for Wound Healing Applications

Antibacterial multi-layered patches composed of an oxidized bacterial cellulose (OBC) membrane loaded with dexpanthenol (DEX) and coated with several chitosan (CH) and alginate (ALG) layers were fabricated by spin-assisted layer-by-layer (LbL) assembly. Four patches with a distinct number of layers (5, 11, 17, and 21) were prepared. These nanostructured multi-layered patches reveal a thermal stability up to 200 °C, high mechanical performance (Young's modulus ≥ 4 GPa), and good moisture-uptake capacity (240-250%). Moreover, they inhibited the growth of the skin pathogen Staphylococcus aureus (3.2-log CFU mL-1 reduction) and were non-cytotoxic to human keratinocytes (HaCaT cells). The in vitro release profile of DEX was prolonged with the increasing number of layers, and the time-dependent data imply a diffusion/swelling-controlled drug release mechanism. In addition, the in vitro wound healing assay demonstrated a good cell migration capacity, headed to a complete gap closure after 24 h. These results certify the potential of these multi-layered polysaccharides-based patches toward their application in wound healing.

Identifiability analysis for stochastic differential equation models in systems biology

Mathematical models are routinely calibrated to experimental data, with goals ranging from building predictive models to quantifying parameters that cannot be measured. Whether or not reliable parameter estimates are obtainable from the available data can easily be overlooked. Such issues of parameter identifiability have important ramifications for both the predictive power of a model, and the mechanistic insight that can be obtained. Identifiability analysis is well-established for deterministic, ordinary differential equation (ODE) models, but there are no commonly adopted methods for analysing identifiability in stochastic models. We provide an accessible introduction to identifiability analysis and demonstrate how existing ideas for analysis of ODE models can be applied to stochastic differential equation (SDE) models through four practical case studies. To assess structural identifiability, we study ODEs that describe the statistical moments of the stochastic process using open-source software tools. Using practically motivated synthetic data and Markov chain Monte Carlo methods, we assess parameter identifiability in the context of available data. Our analysis shows that SDE models can often extract more information about parameters than deterministic descriptions. All code used to perform the analysis is available on Github.

Population dynamics with spatial structure and an Allee effect

Population dynamics including a strong Allee effect describe the situation where long-term population survival or extinction depends on the initial population density. A simple mathematical model of an Allee effect is one where initial densities below the threshold lead to extinction, whereas initial densities above the threshold lead to survival. Mean-field models of population dynamics neglect spatial structure that can arise through short-range interactions, such as competition and dispersal. The influence of non-mean-field effects has not been studied in the presence of an Allee effect. To address this, we develop an individual-based model that incorporates both short-range interactions and an Allee effect. To explore the role of spatial structure we derive a mathematically tractable continuum approximation of the IBM in terms of the dynamics of spatial moments. In the limit of long-range interactions where the mean-field approximation holds, our modelling framework recovers the mean-field Allee threshold. We show that the Allee threshold is sensitive to spatial structure neglected by mean-field models. For example, there are cases where the mean-field model predicts extinction but the population actually survives. Through simulations we show that our new spatial moment dynamics model accurately captures the modified Allee threshold in the presence of spatial structure.

Multifunctional nanofibrous patches composed of nanocellulose and lysozyme nanofibers for cutaneous wound healing

Cutaneous wounds frequently require the use of patches to promote healing, nevertheless, most commercial products are fabricated with non-biodegradable synthetic substrates that pose environmental problems upon disposal. Herein, the partnership between two biobased nanofibrous polymers, namely a polysaccharide (nanofibrillated cellulose (NFC)) and a protein (lysozyme nanofibers (LNFs)), is explored to design sustainable fibrous patches with good mechanical performance and biological functionalities for wound healing applications. Two patches with different morphologies were prepared by vacuum filtration of a water-based suspension of both nanofibers and by sequential filtration of the separated suspensions (layered patch). The resultant freestanding patches exhibited high thermal stability (up to 250 °C), mechanical performance (Young's modulus ≥3.7 GPa), and UV-barrier properties. The combination of the bioactive LNFs with the mechanically robust NFC conveyed antioxidant activity (76-79% DPPH scavenging) and antimicrobial activity against Staphylococcus aureus (3.5-log CFU mL^-1 reduction), which is a major benefit to prevent microbial wound infections. Moreover, these patches are biocompatible towards L929 fibroblast cells, and the in vitro wound healing assay evidenced a good migration capacity leading to an almost complete wound occlusion. Therefore, the partnership between the two naturally derived nanofibrous polymers represents a potential blueprint to engineer sustainable multifunctional patches for cutaneous wound healing.

Discrete Manhattan and Chebyshev pair correlation functions in k dimensions

Pair correlation functions provide a summary statistic which quantifies the amount of spatial correlation between objects in a spatial domain. While pair correlation functions are commonly used to quantify continuous-space point processes, the on-lattice discrete case is less studied. Recent work has brought attention to the discrete case, wherein on-lattice pair correlation functions are formed by normalizing empirical pair distances against the probability distribution of random pair distances in a lattice with Manhattan and Chebyshev metrics. These distance distributions are typically derived on an ad hoc basis as required for specific applications. Here we present a generalized approach to deriving the probability distributions of pair distances in a lattice with discrete Manhattan and Chebyshev metrics, extending the Manhattan and Chebyshev pair correlation functions to lattices in k dimensions. We also quantify the variability of the Manhattan and Chebyshev pair correlation functions, which is important to understanding the reliability and confidence of the statistic.

Predicting population extinction in lattice-based birth-death-movement models

The question of whether a population will persist or go extinct is of key interest throughout ecology and biology. Various mathematical techniques allow us to generate knowledge regarding individual behaviour, which can be analysed to obtain predictions about the ultimate survival or extinction of the population. A common model employed to describe population dynamics is the lattice-based random walk model with crowding (exclusion). This model can incorporate behaviour such as birth, death and movement, while including natural phenomena such as finite size effects. Performing sufficiently many realizations of the random walk model to extract representative population behaviour is computationally intensive. Therefore, continuum approximations of random walk models are routinely employed. However, standard continuum approximations are notoriously incapable of making accurate predictions about population extinction. Here, we develop a new continuum approximation, the state-space diffusion approximation, which explicitly accounts for population extinction. Predictions from our approximation faithfully capture the behaviour in the random walk model, and provides additional information compared to standard approximations. We examine the influence of the number of lattice sites and initial number of individuals on the long-term population behaviour, and demonstrate the reduction in computation time between the random walk model and our approximation.

Healing effects of natural latex serum 1% from Hevea brasiliensis in an experimental skin abrasion wound model

Background

Dermabrasion is related with mechanical and surgical traumas on the skin; usually topical antiseptics and/or saline have been used for healing. Natural products for wound healing can also be used for abrasions, such as latex from Hevea brasiliensis.

Objective

This study aimed to evaluate the in vitro viability and migratory/proliferative effects of latex serum from H. brasiliensis and to compare with a commercially available standard antiseptic solution and saline in experimental dermabrasion on rats.

Methods

For in vitro evaluation, MTT and scratch assays were used. In vivo testing was performed in 72 rats submitted to dermabrasion, treated with saline, antiseptic, or latex serum. This study evaluated re-epithelialization, neutrophilic infiltration, and the quantification of crust and epidermis.

Results

Latex showed viability at 1% and 0.1% concentrations and migratory/proliferative activity at 0.01% concentrations. The re-epithelialization was highest in latex group on 7th day. The latex group displayed lower thickness of crusts and greater extent of epidermal layers. The latex and antiseptic groups showed increases of myeloperoxidase levels on the 2nd day and showed important reductions from the 7th day.

Study limitations

Acute superficial wound model in rats and non-use of gel-cream (medium) without latex.

Conclusion

In conclusion, non-toxic latex stimulated migration/proliferation of keratinocytes in vitro and significantly accelerated wound healing in animal excoriation models compared to chlorhexidine or saline.

A practical guide to pseudo-marginal methods for computational inference in systems biology

For many stochastic models of interest in systems biology, such as those describing biochemical reaction networks, exact quantification of parameter uncertainty through statistical inference is intractable. Likelihood-free computational inference techniques enable parameter inference when the likelihood function for the model is intractable but the generation of many sample paths is feasible through stochastic simulation of the forward problem. The most common likelihood-free method in systems biology is approximate Bayesian computation that accepts parameters that result in low discrepancy between stochastic simulations and measured data. However, it can be difficult to assess how the accuracy of the resulting inferences are affected by the choice of acceptance threshold and discrepancy function. The pseudo-marginal approach is an alternative likelihood-free inference method that utilises a Monte Carlo estimate of the likelihood function. This approach has several advantages, particularly in the context of noisy, partially observed, time-course data typical in biochemical reaction network studies. Specifically, the pseudo-marginal approach facilitates exact inference and uncertainty quantification, and may be efficiently combined with particle filters for low variance, high-accuracy likelihood estimation. In this review, we provide a practical introduction to the pseudo-marginal approach using inference for biochemical reaction networks as a series of case studies. Implementations of key algorithms and examples are provided using the Julia programming language; a high performance, open source programming language for scientific computing (https://github.com/davidwarne/Warne2019_GuideToPseudoMarginal).

In vitro cell migration quantification method for scratch assays

The scratch assay is an in vitro technique used to assess the contribution of molecular and cellular mechanisms to cell migration. The assay can also be used to evaluate therapeutic compounds before clinical use. Current quantification methods of scratch assays deal poorly with irregular cell-free areas and crooked leading edges which are features typically present in the experimental data. We introduce a new migration quantification method, called 'monolayer edge velocimetry', that permits analysis of low-quality experimental data and better statistical classification of migration rates than standard quantification methods. The new method relies on quantifying the horizontal component of the cell monolayer velocity across the leading edge. By performing a classification test on in silico data, we show that the method exhibits significantly lower statistical errors than standard methods. When applied to in vitro data, our method outperforms standard methods by detecting differences in the migration rates between different cell groups that the other methods could not detect. Application of this new method will enable quantification of migration rates from in vitro scratch assay data that cannot be analysed using existing methods.

Practical parameter identifiability for spatio-temporal models of cell invasion

We examine the practical identifiability of parameters in a spatio-temporal reaction-diffusion model of a scratch assay. Experimental data involve fluorescent cell cycle labels, providing spatial information about cell position and temporal information about the cell cycle phase. Cell cycle labelling is incorporated into the reaction-diffusion model by treating the total population as two interacting subpopulations. Practical identifiability is examined using a Bayesian Markov chain Monte Carlo (MCMC) framework, confirming that the parameters are identifiable when we assume the diffusivities of the subpopulations are identical, but that the parameters are practically non-identifiable when we allow the diffusivities to be distinct. We also assess practical identifiability using a profile likelihood approach, providing similar results to MCMC with the advantage of being an order of magnitude faster to compute. Therefore, we suggest that the profile likelihood ought to be adopted as a screening tool to assess practical identifiability before MCMC computations are performed.

Corrected pair correlation functions for environments with obstacles

Environments with immobile obstacles or void regions that inhibit and alter the motion of individuals within that environment are ubiquitous. Correlation in the location of individuals within such environments arises as a combination of the mechanisms governing individual behavior and the heterogeneous structure of the environment. Measures of spatial structure and correlation have been successfully implemented to elucidate the roles of the mechanisms underpinning the behavior of individuals. In particular, the pair correlation function has been used across biology, ecology, and physics to obtain quantitative insight into a variety of processes. However, naively applying standard pair correlation functions in the presence of obstacles may fail to detect correlation, or suggest false correlations, due to a reliance on a distance metric that does not account for obstacles. To overcome this problem, here we present an analytic expression for calculating a corrected pair correlation function for lattice-based domains containing obstacles. We demonstrate that this obstacle pair correlation function is necessary for isolating the correlation associated with the behavior of individuals, rather than the structure of the environment. Using simulations that mimic cell migration and proliferation we demonstrate that the obstacle pair correlation function recovers the short-range correlation known to be present in this process, independent of the heterogeneous structure of the environment. Further, we show that the analytic calculation of the obstacle pair correlation function derived here is significantly faster to implement than the corresponding numerical approach.

Effect of non-adhering dressings on promotion of fibroblast proliferation and wound healing in vitro

Non-adhering dressings are commonly used during granulation, tissue formation, and re-epithelialization. Elucidating cytotoxic effects and influence on proliferation/migration capacity of cells like fibroblasts is of interest. Dressings’ effects were investigated by comprehensive in vitro approach: (1) MTT assay measuring cell viability after direct contact, (2) ATP assay determining effects on cell proliferation, and (3) scratch wound assay featuring an in vitro wound healing model. One cotton-based dressing with vaseline (vas) was included in the study and four polyester dressings containing vas and technology-lipido-colloid matrix (TLC), carboxymethylcellulose (CMC), hydrocolloid (HC), or glycerin (gly) as additives. A polyamide dressing with vas + CMC and three silicone-based dressings (AT, CC, M) were tested. Polyester + vas + CMC did not negatively affect cell viability or proliferation but it was found that fibroblast layers appeared more irregular with decreased F-actin network structure and tubulin density possibly leading to hampered scratch closure. Silicone AT, polyester + gly and polyamide + vas + CMC caused distinct cell damage. The latter two further reduced cell viability, proliferation and scratch healing. From the overall results, it can be concluded that cotton + vas, polyester + TLC, polyester + vas + HC and the silicone dressings CC and M have the potential to prevent damage of newly formed tissue during dressing changes and positively influence wound healing.

INVESTIGATION OF A STRUCTURED FISHER'S EQUATION WITH APPLICATIONS IN BIOCHEMISTRY

Recent biological research has sought to understand how biochemical signaling pathways, such as the mitogen-activated protein kinase (MAPK) family, influence the migration of a population of cells during wound healing. Fisher's Equation has been used extensively to model experimental wound healing assays due to its simple nature and known traveling wave solutions. This partial differential equation with independent variables of time and space cannot account for the effects of biochemical activity on wound healing, however. To this end, we derive a structured Fisher's Equation with independent variables of time, space, and biochemical pathway activity level and prove the existence of a self-similar traveling wave solution to this equation. We exhibit that these methods also apply to a general structured reaction-diffusion equation and a chemotaxis equation. We also consider a more complicated model with different phenotypes based on MAPK activation and numerically investigate how various temporal patterns of biochemical activity can lead to increased and decreased rates of population migration.

Plasma Rich in Growth Factors Induces Cell Proliferation, Migration, Differentiation, and Cell Survival of Adipose-Derived Stem Cells

Adipose-derived stem cells (ASCs) are a promising therapeutic alternative for tissue repair in various clinical applications. However, restrictive cell survival, differential tissue integration, and undirected cell differentiation after transplantation in a hostile microenvironment are complications that require refinement. Plasma rich in growth factors (PRGF) from platelet-rich plasma favors human and canine ASC survival, proliferation, and delaying human ASC senescence and autophagocytosis in comparison with serum-containing cultures. In addition, canine and human-derived ASCs efficiently differentiate into osteocytes, adipocytes, or chondrocytes in the presence of PRGF. PRGF treatment induces phosphorylation of AKT preventing ASC death induced by lethal concentrations of hydrogen peroxide. Indeed, AKT inhibition abolished the PRGF apoptosis prevention in ASC exposed to 100 μM of hydrogen peroxide. Here, we show that canine ASCs respond to PRGF stimulus similarly to the human cells regarding cell survival and differentiation postulating the use of dogs as a suitable translational model. Overall, PRGF would be employed as a serum substitute for mesenchymal stem cell amplification to improve cell differentiation and as a preconditioning agent to prevent oxidative cell death.

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

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.

Cell motility is a central process in wound healing and relies on complex cell-cell interactions. A team of mathematicians led by Ruth Baker and Kit Yates at the University of Oxford utilised computer simulations to re-design wound-healing assays that efficiently identify cell motility parameters. New experimental designs through computer simulation can more accurately identify cell motility parameters by expanding the size of the domain upon which the experiment is performed, as opposed to increasing the number of experimental replicates. The results describe time and cost-saving alterations for an experimental method for evaluate complex cell-cell interactions.