ArticleJournal of mathematical biology1993
A stochastic model for adhesion-mediated cell random motility and haptotaxis.
Article in Journal of mathematical biology, 1993. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 163 citations in OpenAlex.
- Reassessing the Standard Chemotaxis Framework for Understanding Biased Migration inAnnual review of chemical and biomolecular engineering · 2024Review
- CRIPTO-based micro-heterogeneity of mouse muscle satellite cells enables adaptive response to regenerative microenvironment.Developmental cell · 2023Article
- Semi-autonomous wound invasion via matrix-deposited, haptotactic cues.Journal of theoretical biology · 2023Article
- Assessing the roles of collagen fiber morphology and matrix stiffness on ovarian cancer cell migration dynamics using multiphoton fabricated orthogonal image-based models.Acta biomaterialia · 2022Article
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- Bacterial Proprioception: Can a Bacterium Sense Its Movement?Frontiers in microbiology · 2022Review
- BIO-LGCA: A cellular automaton modelling class for analysing collective cell migration.PLoS computational biology · 2021Article
- In Silico Mathematical Modelling for Glioblastoma: A Critical Review and a Patient-Specific Case.Journal of clinical medicine · 2021Review
- Role of neutrophil extracellular traps in regulation of lung cancer invasion and metastasis: Structural insights from a computational model.PLoS computational biology · 2021Article
- Nonlocal and local models for taxis in cell migration: a rigorous limit procedure.Journal of mathematical biology · 2020Article
- Computational models of migration modes improve our understanding of metastasis.APL bioengineering · 2020Review
- Role of Collagen Fiber Morphology on Ovarian Cancer Cell Migration Using Image-Based Models of the Extracellular Matrix.Cancers · 2020Article
- A mechanical toy model linking cell-substrate adhesion to multiple cellular migratory responses.Journal of biological physics · 2019Article
- Migration dynamics of ovarian epithelial cells on micro-fabricated image-based models of normal and malignant stroma.Acta biomaterialia · 2019Article
- Guidance and Self-Sorting of Active Swimmers: 3D Periodic Arrays Increase Persistence Length of Human Sperm Selecting for the Fittest.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2018Article
- A stochastic algorithm for accurately predicting path persistence of cells migrating in 3D matrix environments.PloS one · 2018Article
- Extracting cellular automaton rules from physical Langevin equation models for single and collective cell migration.Journal of mathematical biology · 2017Article
- Programming cancer through phase-functionalized silicon based biomaterials.Scientific reports · 2015Article
- Selection of functional human sperm with higher DNA integrity and fewer reactive oxygen species.Advanced healthcare materials · 2014Article
- Effect of adhesion and chemokine presentation on T-lymphocyte haptokinesis.Integrative biology : quantitative biosciences from nano to macro · 2014Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The active migration of blood and tissue cells is important in a number of physiological processes including inflammation, wound healing, embryogenesis, and tumor cell metastasis. These cells move by transmitting cytoplasmic force through membrane receptors which are bound specifically to adhesion ligands in the surrounding substratum. Recently, much research has focused on the influence of the composition of extracellular matrix and the distribution of its components on the speed and direction of cell migration. It is commonly believed that the magnitude of the adhesion influences cell speed and/or random turning behavior, whereas a gradient of adhesion may bias the net direction of the cell movement, a phenomenon known as haptotaxis. The mechanisms underlying these responses are presently not understood. A stochastic model is presented to provide a mechanistic understanding of how the magnitude and distribution of adhesion ligands in the substratum influence cell movement. The receptor-mediated cell migration is modeled as an interrelation of random processes on distinct time scales. Adhesion receptors undergo rapid binding and transport, resulting in a stochastic spatial distribution of bound receptors fluctuating about some mean distribution. This results in a fluctuating spatio-temporal pattern of forces on the cell, which in turn affects the speed and turning behavior on a longer time scale. The model equations are a system of nonlinear stochastic differential equations (SDE's) which govern the time evolution of the spatial distribution of bound and free receptors, and the orientation and position of the cell. These SDE's are integrated numerically to simulate the behavior of the model cell on both a uniform substratum, and on a gradient of adhesion ligand concentration. Furthermore, analysis of the governing SDE system and corresponding Fokker-Planck equation (FPE) yields analytical expressions for indices which characterize cell movement on multiple time scales in terms of cell cytomechanical, morphological, and receptor binding and transport parameters. For a uniform adhesion ligand concentration, this analysis provides expressions for traditional cell movement indices such as mean speed, directional persistence time, and random motility coefficient. In a small gradient of adhesion, a perturbation analysis of the FPE yields a constitutive cell flux expression which includes a drift term for haptotactic directional cell migration. The haptotactic drift contains terms identified as contributions from directional orientation bias (taxis), kinesis, and orthotaxis, of which taxis appears to be predominant given estimates of the model parameters.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.