Master’s Projects

Available Master’s projects in bioinformatics

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.

References

[1] Anna S Frank, Kamila Larripa, Hwayeon Ryu, Ryan G Snodgrass, and Susanna R¨oblitz. 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.

References

• 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.