skip to content

Cambridge Centre for Physical Biology


List of projects - 2021

For details about the application process check here

An assay to resolve dynamic fitness costs of gene-expression at single cell level

Lead supervisor: Dr Somenath Bakshi , University Lecturer, Department of Engineering

Project Description:

Fitness costs of gene-expression are of fundamental interest since these dictate the ecology and evolution of the species. The 'cost' of expressing a gene is dictated by the required amount of cellular resources, which are otherwise invested in cellular growth and maintenance, often termed as metabolic burden. We plan to develop a new assay to quantify metabolic burden of dynamic gene-expression in individual cells, to estimate the corresponding fitness costs and compare with bulk measurements. 

In this project, we plan to run pilot experiments with an inducible gene to switch between 'on' and 'off' states and develop an analysis pipeline to compute the fitness cost of the 'on' state. In addition to this, as an orthogonal control, we plan to build a platform to image and count colonies from cells from a bulk culture to estimate the fitness differences from the changes in their relative abundance over time. 

Specific aims
1. Compute the fitness costs of expression of a gene by turning it 'on' and 'off' using an inducer and estimating relative growth-rates of individual cells in the microfluidic device. 
2. Construct a plate imaging platform to count colonies from a mixed culture containing cells from strains with and without this gene (constantly 'on' or 'off'). 
3. Compute the relative fitness of these two strains using the relative colony counts and compare with results from aim 1. 


  • Duration 8 weeks
  • The project is primarily lab based but will include tasks that can be done remotely.
  • Useful knowledge, skills and attributes: microbiology, microscopy, instrumentation, image analysis, and statistics 


More information about the project here

The mechanics of biofilm morphogenesis

Lead supervisor: Dr Nuno Miguel Oliveira, Department of Applied Mathematics and Theoretical Physics

Project Description:

Microbiology has traditionally focused on how bacteria respond to their chemical environment and, therefore, our understanding of their mechanobiology is very limited. In particular, bacteria typically live as surface-associated communities (biofilms) where they experience a range of forces, but these have remained elusive. This project aims to quantify these forces by studying biofilms of bioluminescent bacteria, whose light emission is triggered by mechanical stimuli. The summer student will analyse experimental data where bacteria were grown on different stiffness substrates, which generate light patterns, and will develop a mathematical model that can help to explain the observed patterns. 


  • Duration 8 weeks.
  • Project can be done remotely.
  • Experimental work might be possible depending on pandemic-related restrictions at the time. 
  • The ideal candidate should be familiar with quantitative and image analysis, programming, fluid mechanics and pattern formation.


More information about the project here

Tracking individual bacterial cells and estimating their age in a growing biofilm

Lead supervisor: Dr Somenath Bakshi, University Lecturer, Department of Engineering

Project Description:

Recent experiments have shown that even symmetrically dividing bacteria can undergo aging. There is also preliminary evidence that suggests aging can have important implications in affecting the ability of bacteria to tolerate antibiotics. In this project, we would like to investigate how bacteria age as they form biofilms, which are colonies of bacteria that are major contributors to recalcitrant infections. 


Specific aims: 

  1. We will start with using computational models of biofilm (developed in our lab and elsewhere) to analyse replicative aging of cells in model biofilms. Replicative aging refers to the number of daughters produced from a cell. 
  2. We will then use the biofilm imaging platform developed in the lab to image biofilm development from individual cells with single-cell resolution. 
  3. Next, we will develop an image analysis pipeline to track individual cells in the images of the growing biofilm and estimate their age from the number of divisions undergone. The results will be compared with results from the model in step 1, and the model will be updated accordingly. 


  • Duration 8 weeks
  • The project is a good mix of computational work and experiments (70:30). The modelling work can be done remotely. We are quite confident about lab access, but in the unlikely event of another lockdown, we can provide the student with data for image analysis to directly work on step 3, which can be done remotely. 
  • Useful knowledge, skills and attributes: Image analysis, programming, statistics, basic microscopy and microbiology
  • Recommended references: Microbial ageing and longevity: Biased partitioning of multi-drug efflux pumps help older cells to tolerate antibiotics:

Variability in diameter of axonal endoplasmic reticulum tubules

Lead supervisor: Dr Cahir O'Kane, Reader in Genetics, Department of Genetics

Project description:

Endoplasmic reticulum (ER) forms a continuous intracellular tubular nanostructure along the length of axons. Axonal ER has an unusually narrow diameter, which constrains diffusion along its length; larger tubules, caused by loss of genes whose loss causes axon degeneration disease, allow faster diffusion. We will explore how uniformly this diffusion constraint is distributed along tubules, by (1) measuring how tubule diameter varies along their length, and (2) developing tracking of single molecules inside tubules. 


Key aims and tasks:
1. Use existing serial EM sections to manually measure individual tubule diameters along successive sections
2. Assess parameters, e.g. variance, interquartile range, to describe variability along length of individual tubules, and can be used to statistically compare variation across tubules, axons and genotypes
3. Test object segmentation and automated measurements, for consistency with manual measurements
4. Laboratory access permitting, use confocal microscopy to test efficiency of in vivo labeling and diffusion of lumenal markers 


  • Duration 8 weeks
  • The project has a major component that can be conducted remotely, and a component that is lab-based, if distancing/occupancy constraints allow lab access over the summer. If lab access is not practicable, the entire project can be remote.
  • Essential skills: numeracy, basic statistical knowledge
  • Desirable experience, but training will be given: image analysis; knowledge of subcellular organisation; fluorescence/confocal microsopy; statistical software; basic genetics 


More information about the project here

Reduced power measurements of Drosophila larvae for 2-photon optogenetic stimulation of neurons

Lead supervisor: Dr. Miranda Robbins, Department of Zoology and Co-supervisor Dr. Marta Zlatic, Department of Zoology

Project description:

This project aims to identify the optimal immobilisation method for 2-photon imaging in live samples using a novel biomaterial. Tasks include optimising an embedding method for live Drosophila larvae. Designing a 2-photon stimulation protocol to photoactivate fluorescent proteins in individual neurons. Comparing the optical properties of several embedding materials. The student will then use existing methods or develop their own image analysis pipeline to quantify the resulting data.


  • Duration 6 weeks (~5 weeks wet lab, 1week computational analysis)
  • The project requires wet lab techniques. Depending on government regulations at the time, it is possible to perform the project remotely through learning or developing image analysis methods
  • No essential previous skills are required, however it would be desirable for students to have a combined interest in optical microscopy, biotechnology, and neuroscience.


More information about the project here

The role of CTCF and chromosome topology in the regulation of gene expression in imprinted domains

Lead supervisor: Dr Carol Edwards, Department of Genetics and Co-supervisor Prof. Anne Ferguson-Smith, Department of Genetics

Project description:

The DNA of cells is exquisitely folded within the nucleus. Topologically associated domains (TADs) are self-interacting regions on a chromosome that are demarcated by CTCF binding and thought to constrain gene regulation to within the TAD. 

This project aims to study the role of topology in the control of gene expression using genomic imprinting as a model.  We will explore the impact of deleting CTCF sites at the edge an imprinting domain on gene expression, imprinting and topology.


Key aims and tasks: 

  1. Extract RNA from tissues dissected from mice heterozygous for CTCF binding site deletion.
  2. Perform quantitative PCR to assess expression levels in maternal and paternal heterozygotes and their wildtype littermates.
  3. Assess imprinting using allele specific pyrosequencing across known polymorphisms then compare these results with those from other models.
  4. Use circular chromosome conformation capture sequencing (4Cseq) to assess changes to topology in the region.


  • Duration 8 weeks 
  • In person: The project is lab based but some analysis can be done remotely.
  • Skills required: Basic knowledge of molecular biology and genetics. Some lab experience is desirable but full training will be given.


More information about the project here

Robustness of multi-tissue elongation in a chick embryo

Lead supervisor: Dr. Fengzhu Xiong, Wellcome Trust / CRUK Gurdon Institute

Project description:

A developing embryo is like a jigsaw puzzle where distinct pieces (tissues) must fit closely to ensure an overall correct structure. What mechanisms coordinate the sizes and shapes of different tissues are unclear. Our earlier work on the early chicken embryo shows that tissues in the elongating body axis shape each other through mechanical forces. Recently, we found that these forces appear to respond to changing tissue shapes as a feedback to minimize deviation from the target shape, raising a possible mechanism of robustness. In this project the student will have the opportunity to identify the cellular basis of this responsive mechanism with a combination of data analysis, modeling and experiment.


Key aims and tasks: 

1. Quantifying tissue deformation and cell dynamics of shape perturbed embryos;

2. Constructing a model to predict force changes with the quantitative measurements;

3. Testing the predictions in live avian embryos with mechanical and surgical tools. 


  • Duration 8 weeks 
  • Lab-based project but some components can be performed remotely if need be.
  • Basic knowledge of cell and developmental biology, mechanics. Math and programming skills are advantageous but not absolutely required.

RNA structure in health and disease - investigation of lncRNA structure function relationship in imprinting control regions

Lead supervisor: Dr Russell S. Hamilton, Department of Genetics

Project description:

Long non-coding RNAs (lncRNAs) are fundamental to the function of clusters of imprinted genes, however little is known about their structural features, likely to be key to understanding their functional roles and links to human disease. Here we propose to structurally characterising 5 key imprinted lncRNAs alongside an extensive assessment of their key structural features. These lncRNAs have been shown to have key roles in placenta and embryo development, as well as an emerging role in brain development and function.


Key aims and tasks:

  1. Generate accurate 3D models of imprinted lncRNAs
  2. Perform sequence (1D), 2D and 3D comparison across the lncRNAs
  3. Assess 1D/2D/3D conservation between human and mouse
  4. Present results in a publicly available web resource


  • Duration 8 weeks
  • The project is purely computational so can be conducted remotely. If restrictions allow the project can be conducted in within the Department
  • Essential skills: basic competency in R and using bioinformatics tools, although training will also be provided


More information about the project here

Live Modelling the Vertebrate Presomitic Mesoderm

Lead supervisor: Dr Ben Steventon, Department of Genetics. Co-supervisors: (Wet Lab) Mr Tim Fulton, Department of Genetics and (Mathematical Modelling) Dr Berta Verd, Department of Zoology, University of Oxford.

Project description:

Pattern formation in tissues also undergoing morphogenesis requires a cell to dynamically update its identity with respect to its changing position. The aims of this project are to:

  1. Investigate how morphogenetic perturbation of embryos, through inhibition of cell movements, results in changes to the patterns produced both experimentally and in silico.
  2. Investigate the translatability of a conserved gene regulatory network onto tracks from related species (Cichlids)


  • Duration 8 weeks
  • If permitted, we intend for the student to get hands on laboratory experience. If this is not possible, the entire project can be adapted for a working from home style project.
  • Some knowledge of coding using Python would be a significant advantage for the student. Any lab techniques will be taught and no previous lab experience is required. 


More information about the project here

Biological Data Engineering for Geometric Deep Learning

Lead supervisor: Prof Pietro Lio and co-supervisor Arian Jamasb Department of Computer Science & Technology.


Project description:

Graphein is a python library for facilitating geometric deep learning research in Biology. The library provides a suite of functionality for creating and representing data for deep learning projects from a variety of biological sources such as protein structures, interaction networks and chromatin structure data. The project is to develop further featurisation schemes, allowing users to build richer representations of biological data for deep learning tasks and to increase the number of data modalities the library can support.

We currently have a large number of users and are working to position this library as the gold-standard for processing biological graphs in the context of deep learning. We are also interested in creating rich graph objects, such as hierarchical, hyper and multigraphs and are happy for a motivated student to carve out their own project based on their interests.

This is an excellent project for a student looking to increase their exposure to deep learning and computational biology. 


key aims of the project are to increase the number of featurisation schemes and data modalities supported by our data processing library. 


  •  6-8 weeks depending on the student's availability.
  •  The project can be conducted entirely remotely. 
  • Students should be comfortable with python, scientific computing and git. The student can expect to gain knowledge of deep learning (specifically geometric deep learning - a frontier of the field with lots of early successes in biology & life sciences) and their associated frameworks. The student will also learn good software development practices. 

Spatial mechanotrancriptomics of the mouse embryo

Lead supervisor: Dr Bianca Dumitrascu Group Leader, Department of Computer Science & Technology and co-supervisor Dr Adrien Hallou Herchel Smith Research Fellow, Wellcome Trust/CRUK Gurdon Institute & Cavendish Laboratory.


Project description: 

What is the nature of the mechanisms leading to the formation of a highly complex and spatially organised organism from a single cell is the central question of developmental biology. To better understand the complex interplay between genomic information and the morphogenetic movements which sculpt the developing embryo in space and time, we propose to work on a new and unique approach combining mechanical force inference with the image-based single-cell transcriptomics method, seqFISH. Using this approach, we will simultaneously and precisely infer for each single cell of a 8-12 somite stage mouse embryo its mechanical state and the gene expression profile for 387 selected target genes. This will allow us to unravel correlations between gene expression profile and mechanical forces at the cellular, tissue and organism scale and then, using machine learning approaches, to compute pseudotime trajectories in order to quantify in space and time the role of mechanical forces on gene expression patterns and cell fate decisions.

Key aims

1. Segment images at cellular resolution of sections of an 8-12 somite stage mouse embryo using an established deep learning based image segmentation pipeline and extract for each cell SeqFISH signal for each of the 387 selected target genes.

2. Use and improve a mechanical force inference algorithm to infer for each cell in the image the mechanical forces acting on each of its cell-cell junctions and its stress and strain tensors.

3. Use bioinformatics machine learning tools to study correlations between mechanical forces and gene expression patterns in space at the cellular, tissue and embryo scales.


  • 8 weeks 
  • The project can be conducted entirely remotely if needed.
  • Essential skills: The student should be comfortable with scientific computing and programming in Python. Other skills: Knowledge of R, bioinformatics tools and machine learning would be really helpful, as well as a keen interest for physical and quantitative approaches of biological phenomena.