Stochastic models for cell motion and taxis

Edward L. Ionides, Kathy S. Fang, Roslyn Rivkah Isseroff, George F. Oster

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Certain biological experiments investigating cell motion result in time lapse video microscopy data which may be modeled using stochastic differential equations. These models suggest statistics for quantifying experimental results and testing relevant hypotheses, and carry implications for the qualitative behavior of cells and for underlying biophysical mechanisms. Directional cell motion in response to a stimulus, termed taxis, has previously been modeled at a phenomenological level using the Keller-Segel diffusion equation. The Keller-Segel model cannot distinguish certain modes of taxis, and this motivates the introduction of a richer class of models which is nevertheless still amenable to statistical analysis. A state space model formulation is used to link models proposed for cell velocity to observed data. Sequential Monte Carlo methods enable parameter estimation via maximum likelihood for a range of applicable models. One particular experimental situation, involving the effect of an electric field on cell behavior, is considered in detail. In this case, an Ornstein- Uhlenbeck model for cell velocity is found to compare favorably with a nonlinear diffusion model.

Original languageEnglish (US)
Pages (from-to)23-37
Number of pages15
JournalJournal of Mathematical Biology
Volume48
Issue number1
DOIs
StatePublished - Jan 2004

Keywords

  • Cell migration
  • Chemotaxis
  • Galvanotaxis
  • Nonlinear diffusion
  • Stochastic model

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Mathematics (miscellaneous)

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