CDEV-CT09

CDEV Subgroup Contributed Talks

Thursday, June 17 at 06:45am (PDT)
Thursday, June 17 at 02:45pm (BST)
Thursday, June 17 10:45pm (KST)

SMB2021 SMB2021 Follow Wednesday (Thursday) during the "CT09" time block.
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William Martinson

University of Oxford
"Extracellular matrix remodelling by neural crest cells provides a robust signal for collective migration"
Neural crest cells (NCCs) exhibit highly invasive phenotypes in vertebrates; they migrate from the neural tube of an embryo throughout its developing tissues. Since many NCC progenitors contribute to homeostasis in mature organisms, it is unsurprising that disruptions to NCC migration can have severe consequences on individual health, ranging from developmental defects to embryonic lethality. However, the relative importance of the biological mechanisms that contribute to the emergence and maintenance of NCC migration patterns remains to be established. Here, we model discrete NCC migratory streams using experimental data in the chick embryo. In collaboration with developmental biologists, we create a new agent-based model (ABM) for NCC migration that examines how remodelling of the extracellular matrix (ECM) can provide a non-local signal that allows cells to maintain coherent streams. We perform a global sensitivity analysis to identify model mechanisms that most contribute to successful migration, and use the ABM to generate in silico predictions to test through in vivo experiments. We find that ECM remodelling, haptotaxis, and contact guidance provide sufficient signals for NCCs to establish robust in silico streams; however, additional mechanisms are required to steer cells towards appropriate target sites.


Sangita Swapnasrita

Maastricht University
"Kinetic modeling of toxin transport in a bio-artificial kidney"
The organic anion transporters (OATs) in the kidney are mainly responsible for transepithelial removal of uremic toxins out of the blood. To improve current (passive) dialysis treatments, researchers are trying to mimic this active removal by culturing kidney cells expressing the toxin transporters directly on outer surface of a hollow fibre membrane. Using a computational model with independent contributions of the activity and density of the toxin transporters, we have theoretically shown how the transporter density distribution can influence the local toxin clearance. More specifically, we tested twelve different patterns with varying total cell area, while keeping the total number of transporters constant. The computational findings showed that a more homogeneous transporter distribution resulted in a higher toxin clearance. We also demonstrated that short, serially connected cultures of cells would provide equivalent clearance compared to long fibers. In summary, this study contributes to an improved understanding of toxin transport in cellularized hollow fibers, which represent a promising strategy for renal replacement therapies.


Chiara Villa

University of St Andrews
"A mathematical model of endothelial progenitor cell cluster formation during the early stages of vasculogenesis"
The formation of new vascular networks is essential for tissue development and regeneration, in addition to playing a key role in pathological settings such as ischemia and tumour development. Experimental findings in the past two decades have led to the identification of a new mechanism of neovascularisation - cluster-based vasculogenesis - occurring in a variety of hypoxic settings in vivo. The focus of this talk is on the early stages of cluster-based vasculogenesis, during which endothelial progenitor cell (EPC) cluster formation is mediated by the action of matrix degrading enzymes and EPC proliferation. We present a mathematical model which sheds light on the spatio-temporal mechanisms responsible for cluster formation and cluster size. The numerical results, which qualitatively compare with data from in vitro experiments, provide further insights on the underlying dynamics indicating promising, fruitful future modelling and experimental research perspectives.


Supriya Krishnamurthy

"Stochastic chemical reaction networks"
Abstract to be determined. Please check back later.




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