Geoffrey Woollard
Doctor of Philosophy in Computer Science (PhD)
Research Topic
Simulation and inference in cryogenic electron microscopy
Khanh Dao Duc
Assistant Professor
Research Classification
Research Interests
combine mathematical,computational and statistical tools to study fundamental biological processes
regulation and determinants of gene expression and translation
Machine Learning for Biological Imaging and Microscopy
Database development and management
Biological and Artificial Neural Networks for geometric representation
Relevant Thesis-Based Degree Programs
Affiliations to Research Centres, Institutes & Clusters
Research Options
I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
I am interested in working with undergraduate students on research projects.
Research Methodology
mathematical modelling
machine learning
High performance computing
Cryo-EM single particle data analysis
Atomic Force Microscopy Image Analysis
High-Throughput Sequencing data analysis (RiboSeq, scRNA seq)
Recruitment
Postdoctoral Fellows
2023
2024
Learning Spatial Representation from Brain recordings, in collaboration with Manu Madhav's lab at UBC
PhD in Computer Science, Neuroscience or related with experience in Deep Learning or Computational Geometry
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Master's Student Supervision
Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.
Differences in the regulation of translation dynamics in eukaryotic cell-free protein synthesis systems (2024)
Cell-free protein synthesis systems (CFPSS) constitute a powerful tool to study translation dynamics. Yet, they can produce various complex dynamics across conditions and systems that can be challenging to explain. To investigate and interpret differences between eukaryotic CFPSS, we studied two lysate systems; one from Rabbit Reticulocyte and the other from Plasmodium Falciparum. Additionally, we measured the level of protein production from a sequence expressing nanoluciferase over time under various conditions. Upon the addition of mRNA at varied concentrations and pre-incubation times, we observed that the two systems exhibit distinct dynamics, which are quantified and interpreted as the result of dissimilar reactions. In the Rabbit system, translation reaction establishes quickly, but the system is sensitive to a translation-independent degradation of resources. Conversely, the P. falciparum system exhibits a significant delay in initiating translation, while no significant impact of pre-incubation time was detected. Furthermore, we introduced a system of differential equations to mathematically model the translation dynamics, as a function of the mRNA concentration and available resources. Upon fitting the model parameters to the experimental data, the model effectively captured the dynamics of both systems. We observed a notable difference in the transition rate from inactive to active mRNA between Rabbit and P. falciparum, which highlights the different key reactions and limiting factors that regulate translation across eukaryotic CFPSS.
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Simulation-based inference of micelle geometry from 2D cryo-EM images of membrane proteins (2024)
Membrane proteins are important pharmaceutical targets, but the intrinsichydrophobic nature of their transmembrane domains imposes a challenge forexperimental structural studies. The inclusion of membrane-mimetic detergents inthe extraction stabilizes the protein but adds a new factor to consider in structuralexperiments. Although cryo-EM is a revolutionizing tool, there is still a lack of methodsto properly disentangle the contribution of the protein from the detergent micelle to thegenerated images. In this work, we review the properties and availabilities of micellesimages from cryo-EM studies of membrane proteins and outline the development of anin-silico model of the protein-detergent system and a pipeline for inferring geometricparameters of micelles from cryo-EM simulated images. Since our inference methodrelies on the Approximation Bayesian Computation (ABC) algorithm, we tested differentscoring metrics and ran the method on simulated images from existing structures. Asour inferring algorithm yields a distribution of micelle parameters that approximateswell the ground truth value in our experiments, we obtain a proof-of-concept that willneed more refinements to get applied to experimental data but also hold some promisingapplications for automating and improving cryo-EM imaging of membrane proteins.
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