Supervisor:Dr Susanne Bornelöv
Deadline for application:31st October 2024
Course start date:1st October 2025
Overview
Dr Susanne Bornelöv wishes to recruit a student to work on the project entitled: "Unlocking the code within the code: Using AI to decipher the role of codon usage in protein synthesis and gene regulation".
This is a unique opportunity for PhD study in the world-leading Cancer Research UK Cambridge Institute (CRUK CI), to start a research career in an environment committed to training outstanding cancer research scientists of the future.
The Institute's particular strengths are in genomics, computational biology and imaging; and significant research effort is currently devoted to cancers arising in the breast, pancreas, brain, and colon. Our Core Facilities provide researchers with access to state-of-the-art equipment, in-house expertise and training. Scientists at CRUK CI aim to understand the fundamental biology of cancer and translate these findings into the clinic to benefit patients.
There are around 100 postgraduate students at the Cambridge Institute, who play a vital and pivotal role in its continuing success. We are committed to providing an inclusive and supportive working environment that fosters intellectual curiosity and scientific excellence.
Project details
The genetic code contains 61 codons encoding 20 amino acids and most amino acids are therefore encoded by two or more 'synonymous' codons. Despite producing the same protein, the choice of one synonymous codon over another plays an important role in gene regulation [1,2]. Some codons slow down translation, which in turn triggers mRNA degradation and halt protein synthesis. Protein synthesis is often dysregulated in cancer, making mRNA translation an attractive therapeutic target.
This project uses artificial intelligence (AI) to unravel the underlying mechanism by which codon-level information regulates translation.
Our group uses computational methods to study the inner workings and control of biological systems. For instance, by systematically changing the input mRNA sequence to a model capable of predicting mRNA stability or localisation, and analysing resultant patterns, we can gain understanding of what sequence elements regulate these processes. Additionally, we can use disease-associated alterations as inputs to unravel the underlying mechanism.
These models enable us to conduct experiments on a scale far surpassing what is achievable through traditional methods. The primary challenge lies in making models that accurately capture the complexities of biological systems [3]. In this project, you will use cutting-edge deep learning techniques, such as foundation models, to address this challenge. Foundation models, such as those underlying ChatGPT, are trained on a vast array of data and are capable of generalising across a multitude of problems. Your work will use similar models, but trained on 'omics' data, to study the regulation of translation.
References/further reading
Bornelöv S. A code within the genetic code. Nat Rev Mol Cell Biol. 2024 25(6):423. . Bornelöv S, Selmi T, Flad S, Dietmann S, Frye M. Codon usage optimization in pluripotent embryonic stem cells. Genome Biol. 2019 Jun 7;20(1):119. . Rafi AM, Nogina D, Penzar D, Lee D, Lee D, Kim N, Kim S, Kim D, Shin Y, Kwak IY, Meshcheryakov G, Lando A, Zinkevich A, Kim BC, Lee J, Kang T, Vaishnav ED, Yadollahpour P; Random Promoter DREAM Challenge Consortium; Kim S, Albrecht J, Regev A, Gong W, Kulakovskiy IV, Meyer P, de Boer C. Evaluation and optimization of sequence-based gene regulatory deep learning models. BioRxiv. 2023 2023.04.26.538471. .
Preferred skills/knowledge
Funding
Eligibility
We welcome applications from both UK and overseas students.
Applications are invited from recent graduates or final-year undergraduates who hold or expect to gain a First/Upper Second Class degree (or equivalent) in a relevant subject from any recognised university worldwide.
Additional information
To complete your online application, you will need to answer/provide the following:
- Choice of project and supervisor
- Course-specific questions
You will be asked to give details of your Research Experience (up to 2,500 characters). Your Statement of Interest (up to 2,500 characters) should explain why you wish to be considered for the studentship and what qualities and experience you will bring to the role.
- Supporting documents
Applicants will be asked to provide:
Academic transcripts. Evidence of competence in English (if appropriate). Details of two academic referees. CV/resume.
Deadline
The closing date for applications is 31st October 2024 with interviews expected to take place in January 2025.
The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.
The University has a responsibility to ensure that all employees are eligible to live and work in the UK.