Dataset Information

Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory.

ABSTRACT: Cell migration, an essential process for normal cell development and cancer metastasis, differs from a simple random walk: the mean-square displacement (?(?r)²(t)?) of cells sometimes shows non-Fickian behavior, and the spatiotemporal correlation function (G(r, t)) of cells is often non-Gaussian. We find that this intriguing cell migration should be attributed to heterogeneity in a cell population, even one with a homogeneous genetic background. There are two limiting types of heterogeneity in a cell population: cellular heterogeneity and temporal heterogeneity. Cellular heterogeneity accounts for the cell-to-cell variation in migration capacity, while temporal heterogeneity arises from the temporal noise in the migration capacity of single cells. We illustrate that both cellular and temporal heterogeneity need to be taken into account simultaneously to elucidate cell migration. We investigate the two-dimensional migration of A549 lung cancer cells using time-lapse microscopy and find that the migration of A549 cells is Fickian but has a non-Gaussian spatiotemporal correlation. We find that when a theoretical model considers both cellular and temporal heterogeneity, the model reproduces all of the anomalous behaviors of cancer cell migration.

SUBMITTER: Kwon T

PROVIDER: S-EPMC6841739 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Publications

Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory.

Kwon Taejin T Kwon Ok-Seon OS Cha Hyuk-Jin HJ Sung Bong June BJ

Scientific reports 20191108 1

Cell migration, an essential process for normal cell development and cancer metastasis, differs from a simple random walk: the mean-square displacement (〈(Δr)<sup>2</sup>(t)〉) of cells sometimes shows non-Fickian behavior, and the spatiotemporal correlation function (G(r, t)) of cells is often non-Gaussian. We find that this intriguing cell migration should be attributed to heterogeneity in a cell population, even one with a homogeneous genetic background. There are two limiting types of heterog ...[more]

PMID: 31704971

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Project description:BackgroundInfectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.MethodsOne feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.ResultsWe test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data.ConclusionWe propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.

Dataset Information

Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory.

Publications

Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory.

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