Effects of oxidative damage on the mitochondrial membrane structure through molecular dynamics simulations

Supervisors: Iain Johnston and Markus Miettinen

Background: Mitochondria are crucial organelles in eukaryotic cells, responsible for energy production and various cellular functions. The inner mitochondrial membrane (IMM) plays a crucial role in the generation of adenosine triphosphate (ATP) via oxidative phosphorylation. The IMM consists of a complex mixture of lipids, including cardiolipin (CL), phosphatidylcholine (PC), and phosphatidylethanolamine (PE). The physical properties of the IMM, including its structure and elasticity, are important for its function and are influenced by various factors, including oxidative damage. Understand the impact of oxidative damage on the physical properties of the IMM is thus crucial for understanding mitochondrial function and dysfunction.

Research Questions: What is the atom-level structural impact of oxidative damage on a model IMM made of the three key lipid components? How do such structural changes affect the elastic properties of the IMM?

(1) Use Molecular Dynamics (MD) simulations to investigate the atom-level structure of the model IMM under different oxidative-damage -imposed conditions.
(2) Validate the MD results against solid-state NMR (ssNMR) data.
(3) Extract the oxidative-damage-imposed changes on the elastic properties of the IMM from the validated MD.

Expected outcomes:
(1) The development of a validated MD simulation of a model IMM consisting of CL, PC, and PE that can be used to study the impact of various conditions on IMM physical properties and structure.
(2) Insights into how changes in molecular structure affect the overall elastic properties of the IMM.
(3) An understanding of the impact of oxidative damage on the physical properties of the IMM, specifically the effects of oxidative damage on CL and other lipid components.

Conclusion: We will contribute to the understanding of the physical properties of the IMM and how oxidative damage influences these properties. Our results will provide insights into the structure and function of mitochondria and may have implications for the development of therapies for mitochondrial dysfunction.

Visualization of protein mutations for patient and clinician education using molecular dynamics simulation and 3D printing

Supervisors: Marc Vaudel and Markus Miettinen

Objective: To develop an interactive and informative visualizations that helps patients and clinicians understand the impact of disease-causing mutations on protein structure and function.

Methodology: Molecular dynamics simulations will be performed to model effects of sequence variation on protein structure and dynamics. The resulting molecular-level videos will provide patients and clinicians an intuitive interface for exploring the protein structure, visualizing the disease-causing mutations, and understanding their impact on the folded as well as intrinsically disordered parts of the protein. Characteristic protein structures from the molecular dynamics simulations will be used to create physical 3D models using a 3D printer. The 3D models will be used as a tactile tool to help patients and clinicians better understand the protein structure and the impact of the sequence variation.

Expected outcomes: (1) Visualization pipeline for sequence variants using a combination of molecular dynamics simulations and 3D printing. (2) A better understanding of the patients and clinicians on the impact of disease-causing mutations on protein structure, dynamics, and function. (3) Potential to apply this visualization pipeline to other diseases caused by mutations in proteins.

Target audience: The target audience for the visualization tool includes people carrying the variants and the clinicians treating them. The tool can be used to explain the underlying causes of the disease and the rationale behind the treatment options.

Towards precision medicine for cancer patient stratification

Supervisor: Anagha Joshi

On average, a drug or a treatment is effective in only about half of patients who take it. This means patients need to try several until they find one that is effective at the cost of side effects associated with every treatment. The ultimate goal of precision medicine is to provide a treatment best suited for every individual. Sequencing technologies have now made genomics data available in abundance to be used towards this goal.

In this project we will specifically focus on cancer. Most cancer patients get a particular treatment based on the cancer type and the stage, though different individuals will react differently to a treatment. It is now well established that genetic mutations cause cancer growth and spreading and importantly, these mutations are different in individual patients. The aim of this project is use genomic data allow to better stratification of cancer patients, to predict the treatment most likely to work. Specifically, the project will use machine learning approach to integrate genomic data and build a classifier for stratification of cancer patients.

Unraveling gene regulation from single cell data

Supervisor: Anagha Joshi

Multi-cellularity is achieved by precise control of gene expression during development and differentiation and aberrations of this process leads to disease. A key regulatory process in gene regulation is at the transcriptional level where epigenetic and transcriptional regulators control the spatial and temporal expression of the target genes in response to environmental, developmental, and physiological cues obtained from a signalling cascade. The rapid advances in sequencing technology has now made it feasible to study this process by understanding the genomewide patterns of diverse epigenetic and transcription factors as well as at a single cell level.

Single cell RNA sequencing is highly important, particularly in cancer as it allows exploration of heterogenous tumor sample, obstructing therapeutic targeting which leads to poor survival. Despite huge clinical relevance and potential, analysis of single cell RNA-seq data is challenging. In this project, we will develop strategies to infer gene regulatory networks using network inference approaches (both supervised and un-supervised). It will be primarily tested on the single cell datasets in the context of cancer.

Mathematical modeling of macrophage cell polarization

Supervisor: Anna-Simone Frank

Frank et al. [1] used a system of coupled ODEs to study the dynamics of macrophage polarization. The paper showed that dependent on the set of model parameters, the system stability and dynamic behavior was dictated by the initial conditions of the interacting state variables, i.e., the transcription factors. Though the model in [1] represents a parsimonious representation of the system, it remains nonlinear and highly parameterized. Such a models may potentially overfit observations, which results in poor model prediction performance. Hence, there is a need to reduce the model complexity, and to understand model parameter connectivity.

An alternative approach to deriving model equations (such as in [1]) is to use methodologies that construct low-dimensional predictive models from observations. The algorithms usually combine sparsity promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. For macrophage polarization, it is essential that such models preserve the underlying (non-trivial) system dynamics, e.g., multistability. However, examining this property of discovered models is challenging due to data paucity and uncertainty. Most importantly, it is non-trivial to determine the underlying dynamics from short empirical observations. One approach is to use deterministic models with known dynamics to generate data with properties (statistical, temporal resolution) like empirical observations. Because the truth is known, we can better access the applicability of models to macrophage polarization.

The project will involve model reduction approaches applied to the model in [1], and the use of model discovery algorithms, where data from analytic models are used in lieu of empirical observations.


[1] Anna S Frank, Kamila Larripa, Hwayeon Ryu, Ryan G Snodgrass, and Susanna Röblitz. Bifurcation and sensitivity analysis reveal key drivers of multistability in a model of macrophage polarization. Journal of Theoretical Biology, 509:110511, 2021.

Modeling marine ecosystems dynamics using empirical data

Supervisor: Anna-Simone Frank

Marine ecosystems usually consist of a complex and heterogeneous network of species (ranging from microbes to whales), which interact on multiple space and/or time scales. By their nature, it is challenging to define models that adequately capture the inherent dynamics of marine ecosystems. Food webs are descriptive diagrams of biological communities within an ecosystem, with focus on interactions between predators and prey (or consumers and resources). They can be considered as idealized representations of ecosystem complexity that captures species interactions and community structure, as well as the inherent processes and drivers that determine the dynamics of energy transfer in the ecosystem. If correctly defined, food web models (FWM) can provide information about ecosystem predator-prey process dynamics. When multiple species are involved, however, defining the system (predator-prey) dynamics may be non-trivial.

In this project, we shall derive the system equations for an empirical food web system using methodologies that construct low-dimensional predictive models from observations. We use a simplified representation of the food web from the Barents Sea (see e.g., [1]) involving capelin, herring, and cod, to derive system dynamic (Ordinary Differential Equations – ODE) models.  The project will use algorithms that combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data We shall analyze the derived model analytically and using extended simulations. The derived model dynamics will be compared to empirically observed dynamics of the food web components.


[1] Dag Ø Hjermann, Geir Ottersen, and Nils Chr Stenseth. Competition among fishermen and fish causes the collapse of barents sea capelin. Proceedings of the National Academy of Sciences, 101(32):11679– 11684, 2004.