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Scholarly Works (5 results)

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UC San Diego Electronic Theses and Dissertations (2023)

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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  • Article
  • Peer Reviewed
UC San Diego Previously Published Works (2020)
Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this work, we outline the steps for a naïve user to approach the Geometry-preserving Adaptive MeshER software version 2, a mesh generation code written in C++ designed to convert structural data sets to realistic geometric meshes while preserving the underlying shapes. We present two example cases: 1) mesh generation at the subcellular scale as informed by electron tomography and 2) meshing a protein with a structure from x-ray crystallography. We further demonstrate that the meshes generated by the Geometry-preserving Adaptive MeshER software are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.
    • Article
    • Peer Reviewed
    UC San Diego Previously Published Works (2019)

    ABSTRACT

    Advances in imaging methods such as electron microscopy, tomography, and other modalities are enabling high-resolution reconstructions of cellular and organelle geometries. Such advances pave the way for using these geometries for biophysical and mathematical modeling once these data can be represented as a geometric mesh, which, when carefully conditioned, enables the discretization and solution of partial differential equations. In this study, we outline the steps for a naïve user to approach GAMer 2 , a mesh generation code written in C++ designed to convert structural datasets to realistic geometric meshes, while preserving the underlying shapes. We present two example cases, 1) mesh generation at the subcellular scale as informed by electron tomography, and 2) meshing a protein with structure from x-ray crystallography. We further demonstrate that the meshes generated by GAMer are suitable for use with numerical methods. Together, this collection of libraries and tools simplifies the process of constructing realistic geometric meshes from structural biology data.

    SIGNIFICANCE

    As biophysical structure determination methods improve, the rate of new structural data is increasing. New methods that allow the interpretation, analysis, and reuse of such structural information will thus take on commensurate importance. In particular, geometric meshes, such as those commonly used in graphics and mathematics, can enable a myriad of mathematical analysis. In this work, we describe GAMer 2 , a mesh generation library designed for biological datasets. Using GAMer 2 and associated tools PyGAMer and BlendGAMer , biologists can robustly generate computer and algorithm friendly geometric mesh representations informed by structural biology data. We expect that GAMer 2 will be a valuable tool to bring realistic geometries to biophysical models.
      Cover page: An Open Source Mesh Generation Platform for Biophysical Modeling Using Realistic Cellular Geometries
      • Article
      • Peer Reviewed
      UC San Diego Previously Published Works (2020)
      Recent advances in electron microscopy have enabled the imaging of single cells in 3D at nanometer length scale resolutions. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. Enabling such simulations requires watertight meshing of electron micrograph images into 3D volume meshes, which can then form the basis of computer simulations of such processes using numerical techniques such as the finite element method. In this paper, we describe the use of our recently rewritten mesh processing software, GAMer 2, to bridge the gap between poorly conditioned meshes generated from segmented micrographs and boundary marked tetrahedral meshes which are compatible with simulation. We demonstrate the application of a workflow using GAMer 2 to a series of electron micrographs of neuronal dendrite morphology explored at three different length scales and show that the resulting meshes are suitable for finite element simulations. This work is an important step towards making physical simulations of biological processes in realistic geometries routine. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery at the interface of geometry and cellular processes. We posit that a new frontier at the intersection of computational technologies and single cell biology is now open.
        • Article
        • Peer Reviewed
        UC San Diego Previously Published Works (2019)
        Abstract Objective Recent advances in electron microscopy have, for the first time, enabled imaging of single cells in 3D at a nanometer length scale resolution. An uncharted frontier for in silico biology is the ability to simulate cellular processes using these observed geometries. However, this will require a system for going from EM images to 3D volume meshes which can be used in finite element simulations. Methods In this paper, we develop an end-to-end pipeline for this task by adapting and extending computer graphics mesh processing and smoothing algorithms. Our workflow makes use of our recently rewritten mesh processing software, GAMer 2 , which implements several mesh conditioning algorithms and serves as a platform to connect different pipeline steps. Results We apply this pipeline to a series of electron micrographs of dendrite morphology explored at three different length scales and show that the resultant meshes are suitable for finite element simulations. Conclusion Our pipeline, which consists of free and open-source community driven tools, is a step towards routine physical simulations of biological processes in realistic geometries. Significance We posit that a new frontier at the intersection of computational technologies and single cell biology is now open. Innovations in algorithms to reconstruct and simulate cellular length scale phenomena based on emerging structural data will enable realistic physical models and advance discovery.
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