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Incorporating Realistic Cellular Geometry into Systems Biology Models

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It is well known that many cells adapt their geometry in response to their environment, and these changes in geometry can influence their function significantly. For years, the difficulty of obtaining accurate spatial data at the length scale of organelles resulted in a paucity of quantitative descriptions for these relationships between cellular geometry and function. However, recent advances in imaging techniques such as electron microscopy have enabled researchers to capture three-dimensional reconstructions of internal cell geometries at nanometer resolutions. These reconstructions have indicated that cellular geometry is significantly more complex than previously thought; providing a powerful resource and an open invitation for computational scientists to help answer fundamental questions in systems biology.

Despite the increasing availability of high-resolution microscopy data, the process of incorporating these realistic cellular geometries into systems biology models remains exceptionally difficult due to the unique challenges it introduces. One major challenge is caused by the similarity in magnitudes between the current state-of-the-art imaging resolutions and the minuscule length-scales of finely detailed organelles such as the endoplasmic reticulum. Generating high-quality meshes of these intricate components with geometries faithful with biological principles (e.g. membrane physics) is an immense challenge. Another major challenge is constructing and solving the large, coupled, multi-domain systems of non-linear partial differential equations associated with the system.

The first several chapters of this dissertation are dedicated to describing our implementation of a "computational pipeline" which begins with high-resolution cellular geometries and ultimately incorporates them into systems biology models. Because a key objective of this research is to enable the broader scientific community to easily construct spatiotemporal models incorporating high-resolution cellular geometries; the computational pipeline is built entirely upon free and open-source codes. The last chapter is an in-depth application of the computational pipeline: a high-resolution geometric reconstruction of a Purkinje neuron is incorporated into a spatiotemporal signaling model, revealing complex spatially-dependent emergent behaviors. This work is an important first step towards enabling future efforts to incorporate realistic cellular geometries into systems biology models.

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This item is under embargo until April 6, 2025.