arXiv subjects

Isaac V. Chenchiah

Publications and source records attributed to Isaac V. Chenchiah.

4 recordsLinked to original sources

arXiv2026

Quantifying cell shape and density fluctuations in epithelial tissue in vivo

Controlling changes in cell shape are crucial for many biological processes, such as tissue development and wound healing. Tissues typically are heterogeneous with a variety of cell shapes and sizes. Of particular interest are local deviations from an average cell shape and size. These fluctuations may extend and transmit across tissues, potentially offering valuable insights into tissue characteristics such as variations in effective "stiffness" or rigidity. In this study, we present a theoretical framework that captures the dynamics of epithelial cell shapes within tissue, incorporating both their average behaviour and fluctuation patterns. We model cells as interacting soft ellipsoids of varying size and aspect ratio. Coarse-graining our model, we obtain a set of continuum stochastic differential equations from which we derive spatial-temporal correlation functions. These correlation functions fit with our experimental data from the developmental process of the \textit{Drosophila} pupal wing. From the correlation functions, critical parameters representing active cell shape changes and effective tissue "stiffness" can be determined.

cond-mat.stat-mech↗

arXiv2026

BeeNet: Reconstructing Flower Shapes from Electric Fields using Deep Learning

Pollinating insects can obtain information from electric fields arising from flowers. The density and usefulness of electric information remain unknown. Here, we show that electric information can be used to reconstruct geometrical features of the field source. We develop an algorithm that infers the shapes of polarisable flowers from the electric field generated in response to a nearby charged arthropod. We computed the electric fields arising from arthropod flower interactions for varying petal geometries, and used these data to train a deep learning U Net model to recreate the floral shapes. The model accurately reconstructed diverse shapes, including more complex flower morphologies not included in training. Reconstruction performance peaked at an optimal arthropod flower distance, indicating distance dependent encoding of shape information. These findings indicate that electroreception can impart rich spatial detail, offering insights into the electric ecology of arthropods. Together, this work introduces a deep learning framework for solving the inverse electrostatic imaging problem, enabling object shape reconstruction directly from measured electric fields.

q-bio.QM↗

arXiv2026

Dynamics of Wound Closure in Living Nematic Epithelia

We study theoretically the closure of a wound in a layer of epithelial cells in a living tissue after damage. Our analysis is informed by our recent experiments observing re-epithelialisation in vivo of Drosophila pupae. On time and length-scales such that the evolution of the epithelial tissue near the wound is well captured by that of a 2D active fluid with local nematic order, we consider the free-surface problem of a hole in a bounded region of tissue, and study the role that active stresses far from the hole play in the closure of the hole. For parallel anchored nematic order at the wound boundary (as we observe in our experiments), we find that closure is accelerated when the active stresses are contractile and slowed down when the stresses are extensile. Parallel anchoring also leads to the appearance of topological defects which annihilate upon wound closure.

cond-mat.soft↗

arXiv2019

The machine is the material: Structures that mimic one-dimensional thermoelastic materials

Materials that behave like machines, e.g. functional materials that are able to change shape in response to external stimuli (Bhattacharya and James, 2005), often do so by exploiting phase transitions. Shape memory materials and the tail sheath of Bacteriophage T4 are two well-known examples. For the resulting machine to be effective, the material needs to have desirable and tunable properties. Developing such materials has proven to be an endeavour which requires considerable expertise in materials science, engineering and mathematics (Zhang et al., 2009). Here, we reverse this approach by instead designing a machine that acts as a material. Our methodology is independent of characteristic length, allowing us to design behaviour from the architected material through to the macroscopic scale. Specifically, we present thermally-actuated structures whose effective continuum behaviour is that of one-dimensional thermoelastic materials. We show that these structures may possess a range of behaviours, such as shape memory, zero or negative thermal expansivity. Moreover, the amplitude of the behaviour, e.g. length change at critical temperature or magnitude of thermal expansivity, can exceed what is attainable through conventional materials. Seemingly incompatible features, such as low barriers to transformation and stiffness across high elongations, can be combined; the designer can independently control the critical heating and cooling temperatures and eliminate hysteresis, if desired; changes in length can be either continuous or discontinuous; and shape memory can be combined with negative thermal expansivity.

cond-mat.mtrl-sci↗