We are currently recruiting new PhD and research masters students for a number of projects. The following list describes some of the available project areas.
Please contact Prof Edmund Crampin if interested, or for further information.
Energy-based Whole Cell Modelling: Design Tools for Systems and Synthetic Biology
Understanding how biological cells work requires combining information from many different domains – biochemical, electrical, mechanical. Working out how these different aspects of cells interact is a challenge. Predicting what happens if the system is altered (in disease, or through biotechnology, for example) is even harder. We are applying a set of techniques which engineers have developed to understand man-made systems, to biological systems. This approach tracks the flow of energy through a cell’s network of biochemical reactions. This allows us to effectively combine different aspects of the cell within the same unifying mathematical description. One of the reasons engineers have used these methods for man-made systems is because they want to be able to control them. In addition to understanding cell function, ultimately this approach will lead to the ability to more easily and reliably modify biological systems with predictable outcomes – so that we can better understand and hence treat disease, and so that we can design new biological systems for biotechnological and biomedical applications, and by applying engineering design principles we can design new biological systems (synthetic biology).
The Virtual Heart Cell
This project brings together a multi-disciplinary team of scientists in physiology, computational biology and cellular mechanics to gain a more comprehensive, integrated understanding of how heart cell structure and function determines the heart health and disease. We are developing a three-dimensional, biophysically realistic computational model of heart cell structure and function (a ‘virtual heart cell’) with two specific aims:
(i) To use the models to understand the effect of sub-cellular structural and biochemical alterations on cardiac cell performance;
(ii) To explore novel treatment strategies using this new virtual cell environment that enables us to appreciate the integrated and multi-faceted response of the cell to a range of clinical therapies.
Modelling Nano-Bio Interactions: Predictive Nanoparticle Dosimetry
Determining the affinity of a nanoparticle for a cell is an important step in characterizing the biological activity of that nanoparticle. This is usually determined in vitro by incubating cells with a solution of particles and then measuring cellular association or uptake. Comparing these measurements for different particle types is desirable. Unfortunately, it does not make sense to compare them directly, as the physical properties of the system can lead to substantially different amounts of particles being presented to the cell. For instance, consider a solution with particles that sediment rapidly compared to a well-dispersed colloidal suspension of particles. Clearly, if incubated in vitro with cells at the bottom of a well, the former solution will sediment onto the cells and a greater percentage of particles will be presented. This is recognized as a problem in the field, but attempts to characterize, control for, or model these effects are rare. We’re building computational models using partial differential equations to predict bulk movement of particles in solution. This will allows us to predict the presentation of particles to cells based on their physical properties, and by extension, to control for these physical effects and compare different particles. We currently incorporate the physical effects of a sphere moving in fluid, but this work provides a framework to investigate and include additional parameters, such as electrostatic interactions and variations due to cell type.
Metabolic modelling to target host-parasite interactions
Leishmania is a parasite that causes a spectrum of devastating human diseases in the tropics and subtropics. The parasite targets macrophages, one of the major cell types in the human body’s immune system, where they reside within the lysosome compartment that is normally involved in killing invading pathogens. There is intense interest in understanding how the Leishmania parasite survives in these cells and, in particular with the view of identifying new drug targets and better therapies. One of the key areas of research is understanding how the parasite metabolism interacts with the host cells. We will be building detailed mathematical models of Leishmania metabolism to be able to better predict the consequences of genetically or chemically inhibiting particular enzymes or pathways. We will use data from comprehensive metabolomics experiments being undertaken at Bio21 Institute (Prof Malcolm McConville) which measure the rates of different parts of the metabolic network under different conditions using advanced mass spectrometry approaches. This information will then be used to explore key parts of the parasite metabolism in order to understand how the parasite can be targeted pharmacologically without harming the host.
Gene regulatory networks and epigenetics in cancer
Despite major advances in molecular biology and genetics, our understanding of the molecular networks that control cell function, and the way in which dysregulation of these networks promotes diseases, including cancer, remains far from complete. Developing avenues for new therapies and diagnostic tests relies on our comprehension of the molecular mechanisms regulating the progress of disease. A Systems Biology approach to studying disease progression is to construct network models from high-quality, high-throughput molecular data, and to interrogate these networks through computational analysis. We are combining novel methods developed at the Systems Biology Laboratory with other techniques for network-based analysis, and applying this approach to datasets in a variety of cancers and tumour types to uncover potential targets for further investigation. We are also integrating clinical information such as patient history, survival, tumor grade and age, with molecular data to improve the predictive power and clinical applicability of this approach.