CDEV Subgroup Contributed Talks

Gordon R. McNicol

University of Waterloo

"Mechanotransducing structures promote self-driven cell surface patterning"

Cells respond to their local environment through mechanotransduction, converting mechanical signals into a biological response (e.g. cell growth, proliferation or differentiation). The cell cytoskeleton, particularly actomyosin stress fibres (SFs), and focal adhesions (FAs), which bind the cytoskeleton to the extra-cellular matrix (ECM), are central to this process, activating intracellular signalling cascades in response to deformation. We present a novel two-dimensional bio-chemo-mechanical model to describe the development of these structures, coupled through a positive feedback loop, and the associated cell deformation. Building on our previous one-dimensional approach, we similarly employ reaction-diffusion-advection equations to describe the evolution of key scaffolding and signalling proteins, and connect their concentrations to a viscoelastic description of the cell cytoplasm, ECM and adhesions. Further, we now incorporate other key mechanotransducing structures including the stiff cell nucleus, and plasma and cortical membranes. Working in an axisymmetric framework, we employ this model to explain how, dependent upon the mechanical properties of the surrounding ECM, non-uniform patterns of cell striation develop, leading to FA and SF localisation at the cell periphery. Moreover, a linear stability analysis reveals the stability of the axisymmetric configuration to various normal modes of deformation. By identifying non-axisymmetric modes with positive growth rates our model demonstrates a possible mechanism for self-driven surface patterning of cells in vitro.

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Marc Roussel

University of Lethbridge

"The bacterial dimeric transcription factor NsrR: a case study of a regulatory protein with a large number of states"

In a number of bacteria, nitric oxide (NO) is converted to nitrate by an enzyme called Hmp. In emph{Streptomyces coelicolor}, synthesis of Hmp is in turn controlled by an iron-sulfur protein called NsrR. NsrR represses the transcription of two copies of the emph{hmp} gene in the emph{S. coelicolor} genome, but reaction of NsrR's iron-sulfur cluster with NO causes NsrR to dissociate from the emph{hmp} promoter, thus allowing Hmp to be expressed. While this is a straightforward control mechanism, NsrR is a dimer, and the iron-sulfur cluster in each monomer of NsrR can react with NO several times. Eventually, a repair system restores the NO-damaged iron-sulfur clusters of the dimers. But given that a single reaction with NO is sufficient to cause the NsrR dimer to dissociate from the emph{hmp} promoter, do we need to model the complex chemistry of the dimer, or is a highly simplified model that considers a single NsrR unit and its iron-sulfur cluster sufficient to capture the dynamics of this control system?

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Paco Castaneda Ruan

The University of Auckland

"Exploring the role of Ca2+ influx in controlling competing oscillatory mechanisms in T cells using ODEs"

Across the spectrum of cell types, the concentration of calcium controls a wide array of cellular functions. These calcium signals, usually in the form of periodic oscillations, play a paramount role in correct cellular activity. T cells are fundamental to the correct behaviour of the immune system. These cells have recently been shown to exhibit two competing oscillatory mechanisms, depending on the influx of extracellular Ca2+. Ca2+ influx is controlled by two molecules, STIM1 and STIM2. When both STIMs are present, T cells showcase sinusoidal Ca2+ oscillations on a raised baseline, but when one of them is absent the nature of the oscillation changes to a mix of Ca2+ spikes and bursting periods. In this talk, we will present an ODE that attempts to explain how these two molecules control the nature of these oscillations in T cells

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Lynne Cherchia

University of Southern California

"A tale of trafficking: On prolactin receptor localization in pancreatic β-cells"

The prolactin receptor (PRLR) is a single-pass transmembrane receptor driving pancreatic β-cell proliferation via JAK/STAT signaling activation. This signal transduction pathway enables insulin-secreting β-cells to adapt to metabolic stress; however, the precise mechanisms underlying the pathway’s proliferative effect remain ill-defined. Here we implement a pipeline that uses live-cell fluorescence imaging, reconstitution approaches, and fluorescence correlation spectroscopy (FCS) to inform a mathematical model of PRLR signaling in β-cells and build a quantitative, mechanistic understanding of the signaling network. PRLR signaling is dynamic, involving changes in the spatial organization of signaling molecules. We have observed PRLR undergoing rapid internalization, a behavior that has been shown and modeled in other signaling pathways but has not been considered in a mathematical model of PRLR signaling. Such a model is useful for predicting strategies to modulate β-cell function. PRLR internalization is observed in both our minimal engineered PRLR expression system and in native pancreatic tissue, while FCS and chemigenetic labeling with SNAP-tag confirm the presence of a low concentration plasma membrane pool of PRLR. Our imaging data are used to integrate PRLR trafficking dynamics into an ordinary differential equation (ODE) model of PRLR signaling. We employ the ODE model to test hypotheses targeting how the spatial heterogeneity of PRLR signaling dynamics affects downstream signaling outcomes. Our data underscore the versatility of building a generalizable modeling-imaging framework to quantitatively understand signal transduction in and beyond β-cells.

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Rebecca Crossley

University of Oxford

"Travelling waves of phenotypically structured cell populations migrating into extracellular matrix"

Collective cell migration plays a crucial role in numerous biological processes, including cancer growth, wound healing, and the immune response. Often, the migrating population consists of cells with various different phenotypes. This study derives a general mathematical framework for modelling cell migration into the micro-environment, which is coarse-grained from an underlying individual-based model that captures some of the dynamics of cell migration that are influenced by the phenotype of the cell, such as: random movement, proliferation, phenotypic transitions, and interactions with the external environment. The resulting model provides a continuum, macroscopic description of cell invasion, which represents the phenotype of the cell as a continuous variable and is much more amenable to simulation and analysis than its individual-based counterpart when considering a large number of phenotypes. The results highlight how phenotypic structuring impacts the spatial and temporal dynamics of cell populations, demonstrating that different environmental pressures and phenotypic transition mechanisms significantly influence invasion patterns.

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