Machine learning the dynamics of force-sensing proteins

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.

Application Procedure

Start your application:

·      To submit a formal application, please register on our online application system King’s Apply.

Choose a programme:

·      Under Programme Name, select ‘Centre for the Physical Science of Life Doctoral Studentship’

·      Select ‘02 June 2025’ as the start date / week commencing date

Personal information:

·      Enter the required details in each section, following the prompts to progress to the next section

·      It is the applicant’s responsibility to submit all required documents

Education

·      Provide details of educational qualifications, including English language

Employment history

·      Upload a PDF of your CV as an attachment, even if you do not have employment information to provide

·      Include previous work experience if applicable

Supporting statement

·      Enter the Project Title or Reference: Machine learning the dynamics of force-sensing proteins.

·      Select “Yes” for “I have identified the King’s supervisor I would like to study under” and enter Bo as the Supervisor Name.

·      In the Research Proposal box, please write ‘This is not required’

·      Please upload a supporting statement, in PDF format, addressing the following elements: (1) Why are you applying to this PhD position, (2) How has your experience leading up to now prepared you to pursue a PhD, and (3) What are your short-term and long-term career goals, and how will an interdisciplinary PhD in Physical Sciences help you to achieve them?

References

·      Provide the contact details for two academic referees or relevant employers in research institutions or companies.

·      Please inform your referees that we will be contacting them for a reference as soon as possible.

·      All references must be received within one week of the application deadline.

Funding

·      Select Option 5 – I am applying for a funding award or scholarship administered by King’s College London

·      For “Award Scheme Code or Name”, enter: CentPhyLife7

Check and submit

·      Ensure all sections have been completed and the information provided is correct

Submit your application

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