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Development of a miRNA biosensor
Supervisor: Linda Dekker (Barnes Lab) | on campus project | eligible for IPLS studentship (Yr 2, Y3 or Y4 BSc/MSci registered undergraduate studying at a UK university for a basic science degree including but not limited to biology, physics, chemistry, engineering and computer science)
MicroRNAs (miRNAs) are small, non-coding RNA molecules, typically 20–24 nucleotides long, that play a critical role in the regulation of gene expression. Dysregulation of miRNAs can disrupt normal cellular processes and lead to various diseases. They are implicated in a broad range of conditions, including cancer, cardiovascular disease, neurodegeneration and immune disorders. The aim of this project is to build miRNA biosensors using Escherichia coli. These engineered strains will detect the miRNAs and produce GFP and other signalling molecules as output. More specifically, a CRISPR-based RNA sensing system can support the linkage of orthogonal and variable RNA input to almost any target gene expression as the output, while requiring minimal cloning effort. This can provide a strong basis for the expansion of our biosensing toolkit. Based on previously reported uptake of miRNA by E. coli in the gut environment (Liu et al., 2016), we propose a project to construct and characterise an E. coli biosensor that senses extracellular miRNA through toehold-mediated strand displacement and CRISPRi. A variety of engineering biology techniques will be used to undertake this project.
Nanomechanics and nutrients: how soft are hungry bacteria?
Supervisor: Will Trewby (Hoogenboom Lab) | on campus project | eligible for IPLS studentship (Yr 2, Y3 or Y4 BSc/MSci registered undergraduate studying at a UK university for a basic science degree including but not limited to biology, physics, chemistry, engineering and computer science)
The outer membrane (OM) of Gram-negative bacteria plays a critical role as the first line of defence against antibiotic compounds, while also maintaining mechanical loads and allowing for rapid cell growth and division. These essential functions are made possible by highly-structured nature of the pore proteins (OMPs) and lipopolysaccharides (LPS) that make up the membrane.
This key ability of the OM to sustain stress relies upon the molecular-level organisation of its constituents as well as their relative abundance, both of which are modulated depending on nutrients (such as glucose) available to the bacterium. However, it remains an open question how these significant changes impact upon the membrane's nanoscale mechanics and physical integrity.
This project will use cutting-edge atomic force microscopy (AFM) techniques to tackle this question by mapping the stiffness of living E. coli cells at different levels of glucose deficiency with nanometer-scale resolution. The student will gain experience at the interface between physics, biology and materials science, and will have the opportunity to develop theoretical models and/or data science approaches to the analysis.
A minimal model for the emergence of self-replication
Supervisor: Jaime Agudo-Canalejo | hybrid project | eligible for Brian Duff studentship (Yr 2 BSc or MSci/Yr 3 MSci registered undergraduate studying at UCL in the department of Physics and Astronomy or UCL Natural Sciences undergraduate with Physics as the mainstream)
An important question in origins of life research is the following: Given the rules of chemistry, how inevitable is the emergence of molecular self-replicators, i.e. molecules that can make copies of themselves? In this project, employing dynamical systems theory and Monte Carlo simulations, we will tackle this question by exploring a toy model of interacting molecules based on the physics of spin glasses. This will allow us to study different "chemistries" and the necessary conditions for self-replication to emerge. The project can be done remotely.
Investigating Ligand-Receptor Binding Kinetics
Supervisor: Edina Rosta | hybrid project | eligible for Brian Duff studentship (Yr 2 BSc or MSci/Yr 3 MSci registered undergraduate studying at UCL in the department of Physics and Astronomy or UCL Natural Sciences undergraduate with Physics as the mainstream)
Ligand-receptor kinetics is an important factor to be considered during drug development. The residence time (RT) of a ligand for a receptor, which is defined as the inverse of the dissociation rate constant (koff), has been shown to be key for in-vivo efficacy (1). A prime example of this is the binding of tiotropium to the Muscarinic 3 receptor (M3R) in the lungs, which is an important target for modern Chronic Obstructive Pulmonary Disease (COPD)/asthma drugs. The M3R is a G-protein coupled receptor (GPCR), the antagonising of which leads to lower secretion of mucus and thus respiratory relief. Tiotropium is a muscarinic antagonist, shown to have an exceptionally long half-life upon binding to the M3R (over 30 hours). It is also by far the most successful drug in the treatment of asthma and COPD, for this very reason. In this project, we will investigate ligand unbinding kinetics aided by enhanced sampling and machine learning methods. We aim to obtain novel mechanisms with key features for ligand unbinding to guide future drug discovery.
This project will introduce the students into molecular dynamics simulations of complex biomolecular systems (2). They will learn to set up simulations by retrieving experimental structures from the Protein Data Bank, use Linux bash shell scripts and python codes to manipulate files, generate input and process output, run code on HPC clusters. Use molecular dynamics and visualisation tools such as GROMACS, NAMD, OPENMM, Pymol and VMD. For the analysis of trajectories, we will use machine learning tools including. Knowledge of python programming is required.
1. Wijnand J. C. van der Velden, Laura H. Heitman, and Mette M. Rosenkilde (2020). ACS Pharmacol. Transl. Sci. 2020, 3, 179−189
2. Badaoui, M.; Buigues, P. J.; Berta, D.; Mandana, G. M.; Gu, H.; Földes, T.; Dickson, C. J.; Hornak, V.; Kato, M.; Molteni, C.; Parsons, S.; Rosta, E. Combined Free-Energy Calculation and Machine Learning Methods for Understanding Ligand Unbinding Kinetics. J. Chem. Theory Comput. (2022), 18 (4), 2543–2555. https://doi.org/10.1021/acs.jctc.1c00924.