Machine learning the dynamics of force-sensing proteins

Award details

Mechanosensing proteins detect mechanical forces within cells and convert them into biochemical signals. Understanding the dynamic behaviour of these proteins under force is key for advancing our understanding of this process called mechanotransduction. Recent advances in single-molecule magnetic tweezers instrumentation have opened up exciting possibilities for studying these proteins under physiologically relevant forces, providing unprecedented insights into their functional states as done in a recent study by Rafael Tapia-Rojo [1]. The typical readout of such measurements is a stochastic trajectory that randomly oscillates around certain equilibrium points and quickly switches between them. In these trajectories, the signal is embedded in noise. We have recently developed machine-learning techniques to measure physical parameters from the stochastic motion of individual molecules [2,3].

Your Role: As a member of our research team, you'll develop cutting-edge machine-learning techniques to analyse stochastic trajectories of force-sensing proteins under the supervision of Dr Bo. To obtain such stochastic data, you will conduct single-molecule experiments in magnetic tweezers using protein model systems undergoing conformational transitions under force, performed in the lab of Dr. Tapia-Rojo. Special attention will be devoted to the interpretability of the networks and to building physics-informed methods with the tools of statistical physics. You will also have the possibility to complement these studies with molecular dynamics simulations supervised by Chris Lorenz [4].

Your profile Applicants should have, or expect to have, an integrated Master’s (e.g., MSci) with first-class honours or upper division second-class honours (2:1), or a BSc plus Master’s (MSc) degree with Merit or Distinction in Physics, Biophysics, Applied Mathematics, or related subject. Equivalent international degrees are equally accepted. The funding is not restricted to specific nationalities. The successful applicant will demonstrate strong interest and motivation in the subject, and ability to think critically and creatively. Previous research experience in biophysics and or an interdisciplinary research environment is desirable. Interested candidates are invited to contact the main supervisor (Stefano Bo, stefano.bo@kcl.ac.uk) with a transcript, CV, and motivation letter expressing interest in the project. Informal enquiries are encouraged.

Award value

Funding is available for 3.5 years, covering:

• Stipend (£21,237 p/a);
• Bench Fees (£4,500 p/a);
• Tuition fees (full home or overseas tuition fees).

Eligibility criteria

Applicants should have, or expect to have, an integrated Master’s (e.g., MSci) with first-class honours or upper division second-class honours (2:1), or a BSc plus Master’s (MSc) degree with Merit or Distinction in Physics, Biophysics, Applied Mathematics, or related subject. Equivalent international degrees are equally accepted. The funding is not restricted to specific nationalities. The successful applicant will demonstrate strong interest and motivation in the subject, and ability to think critically and creatively. Previous research experience in biophysics and or an interdisciplinary research environment is desirable.

Application process

To be considered for the position candidates must apply via King’s Apply online application system. Details are available at Physics Research | King's College London.

Please apply for Physics Research MPhil/PhD (Full-time) and indicate Stefano Bo as the supervisor and quote the project title in your application and all correspondence.

Please ensure to add the following code [CentPhyLife7.] in the Funding section of the application form. Please select option 5 ‘I am applying for a funding award or scholarship administered by King’s College London’ and type the code into the ‘Award Scheme Code or Name’ box. Please copy and paste the code exactly.

The selection process will involve a pre-selection on documents and, if selected, will be followed by an invitation to an interview. If successful at the interview, an offer will be provided in due course.

Please note that the intake is for June 2025.

If you require support with the application process please contact nmes-pgr@kcl.ac.uk.