What is my research about?

Developing a set of open-source computational tools in Python to perform multi-scale analysis of structure-function relationship in mitochondrial networks. My model organism is Saccharomyces cerevisiae, also known as budding yeast.

Segmentation of mitochondrial networks from live yeast cells 3D images taken with a spinning disk confocal microscope

• This segmentation utilizes MitoGraph, a C++ skeletonization and segmentation program developed by Matheus Viana. It has been fully validated in yeast cells (Viana, Lim et al.).
• The pipeline folder contains modules used to map mitochondrial function to structure. This pipeline utilizes Mayavi's wrapped version of VTK to take advantage of Pythonic coding syntax when working with VTK data.
• This notebook shows an example of a typical workflow.
• The output data from the pipeline was 'munged' into a database using Pandas before further calculations of various statistical and functional parameters.

Modulation of mitochondrial function by altering carbon source growth conditions

• We modulated the metabolic state of the cell by altering the carbon source used during cell growth. This notebook shows an example of how carbon source growth conditions alters oxygen consumption and mitochondrial membrane potential (Δψ), which is a key parameter of cell bioenergetics.

Investigating the link between structure of mitochondria and heterogeneity of mitochondrial membrane potential (Δψ)

• We showed the non-random heterogeneous distribution of Δψ within a single mitochondrial tubule example here.
• We also detail our investigation into the relationship between heterogeneity of mitochondrial function and network topology example here.

Analysis of mitochondrial functional asymmetry in mother and daughter yeast cells

• A set of tools to interactively visualize the 3D mitochondrial skeleton and pick points to classify the cell as a mother or daughter region. This Ipython notebook demonstrates an application of these tools to study how mitochondrial membrane potential (Δψ) is distributed differently between the mother and daughter cell.

Requirements

If you wish to run the source code for the pipeline, you need to have these dependencies installed:

• Pandas
• Matplotlib
• Mayavi
• NetworkX
• Numpy
• Seaborn
• Scipy
• VTK

You can have these dependencies installed in one step using the Miniconda package manager. Copy the minimal.yml file into the current directory of your shell. Then type the following (this will install all the above dependencies into an enviromenment called minimal.):

conda env create -f minimal.yml

You must activate the minimal environment after this:

Linux, OS X: source activate minimal

Windows: activate minimal