Biomimetics Models of Cellular Motility

A special issue of Biomimetics (ISSN 2313-7673). This special issue belongs to the section "Development of Biomimetic Methodology".

Deadline for manuscript submissions: closed (5 December 2023) | Viewed by 1293

Special Issue Editor


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Guest Editor
Department of Physics and Astronomy, Tufts University, Medford, MA 02155, USA
Interests: biological physics; condensed matter physics; quantum mechanics; neuronal cells

Special Issue Information

Dear Colleagues,

Cellular motility, defined as the capacity of cells to self-propel their motion, is a fundamental process underlying many biological phenomena, ranging from the single bacteria foraging for food, to the complex interplay of cells during immune response, wound healing, or embryonic development. Recent advances in computational capabilities and experimental data acquisition have catalyzed the development of accurate biomimetic models of cellular motility. These models are crucial for understanding the underpinnings of various physiological and pathological events, such as neural development, angiogenesis, chronic inflammation, or cancer metastasis. Fundamentally, cellular motility arises as the result of the interplay between deterministic and stochastic influences. Deterministic cues include chemotactic gradients, mechanical and geometrical properties of the surrounding environment, or external electric fields. Stochastic components are represented by a myriad of random fluctuations inherent to biological systems, such as intercellular signaling, molecular noise due to detection of biomolecules at very small concentrations, biochemical reactions taking place inside the cell, genetic variability, signal transduction, polymerization of cytoskeletal elements, etc. Biomimetic models based on stochastic differential equations have proven very effective for capturing the intricacies of cellular motility. Examples of such models include biased random walk models, Markov chains, Keller–Segel chemotaxis models, and models based on the Langevin and Fokker–Planck stochastic differential equations. Motivated by these insights, our aim is to establish a platform where current breakthroughs and developments in stochastic modelling of cellular motility can be gathered together and shared with the research community. We aim to feature articles that encapsulate the latest innovations, as well as those that introduce fresh perspectives and pioneer novel research avenues in this fascinating and rapidly evolving field. We are looking forward to your contributions.

Dr. Cristian Staii
Guest Editor

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Keywords

  • motility
  • cellular movement
  • cell migration
  • neural networks
  • stochastic processes
  • random walk
  • complex systems
  • mathematical modelling

Published Papers (1 paper)

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Research

15 pages, 3607 KiB  
Article
Nonlinear Growth Dynamics of Neuronal Cells Cultured on Directional Surfaces
by Cristian Staii
Biomimetics 2024, 9(4), 203; https://doi.org/10.3390/biomimetics9040203 - 28 Mar 2024
Viewed by 1023
Abstract
During the development of the nervous system, neuronal cells extend axons and dendrites that form complex neuronal networks, which are essential for transmitting and processing information. Understanding the physical processes that underlie the formation of neuronal networks is essential for gaining a deeper [...] Read more.
During the development of the nervous system, neuronal cells extend axons and dendrites that form complex neuronal networks, which are essential for transmitting and processing information. Understanding the physical processes that underlie the formation of neuronal networks is essential for gaining a deeper insight into higher-order brain functions such as sensory processing, learning, and memory. In the process of creating networks, axons travel towards other recipient neurons, directed by a combination of internal and external cues that include genetic instructions, biochemical signals, as well as external mechanical and geometrical stimuli. Although there have been significant recent advances, the basic principles governing axonal growth, collective dynamics, and the development of neuronal networks remain poorly understood. In this paper, we present a detailed analysis of nonlinear dynamics for axonal growth on surfaces with periodic geometrical patterns. We show that axonal growth on these surfaces is described by nonlinear Langevin equations with speed-dependent deterministic terms and gaussian stochastic noise. This theoretical model yields a comprehensive description of axonal growth at both intermediate and long time scales (tens of hours after cell plating), and predicts key dynamical parameters, such as speed and angular correlation functions, axonal mean squared lengths, and diffusion (cell motility) coefficients. We use this model to perform simulations of axonal trajectories on the growth surfaces, in turn demonstrating very good agreement between simulated growth and the experimental results. These results provide important insights into the current understanding of the dynamical behavior of neurons, the self-wiring of the nervous system, as well as for designing innovative biomimetic neural network models. Full article
(This article belongs to the Special Issue Biomimetics Models of Cellular Motility)
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