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Biophysics informed neural network based constitutive modelling of biologic meta-materials (EPS2022/43)

   School of Engineering & Physical Sciences

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  Dr Uwe Wolfram, Dr M Vallejo, Dr A Ozel  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Hard and soft tissues such as bones, cartilage, lung tissue, or brain tissue feature cell-seeded material architectures and a portfolio of multi-physical properties that range from providing mechanistic stability, to heat and mass transport, or to sensing and control of biophysical signals. They, thus, constitute biologic metamaterials whose functional capability goes beyond the properties of the individual constituents. Proper tissue function is essential to maintain health and quality of life. For example, musculoskeletal or respiratory diseases affect millions of patients in worldwide and represent a massive socioeconomic burden.

Constitutive models that capture the multi-physical and architectured characteristics of these materials are exciting tools to aid theranostics and potentially lower socioeconomic costs. However, the current gold standard are constitutive models targeting individual or small sets of properties with limited incorporation of the biologic aspects of the living tissue. This prohibits crosstalk of signals across different constitutive behaviours. For example, neurostimulation requires models for neural signal transmission which then need to translate into muscle growth. Other examples are bone mechanobiology where mechanical loads stimulate bone cell activity or the diffusion-dominated drug/oxygen exchange in lung. While combining multiple physical process across multiple length scales that often run in parallel poses a challenge on its own, computational efficacy is of ample importance if such models were to be used in subject specific analyses to underpin theranostics.

Biophysically informed neural networks offer the opportunity to capture the property spectrum of biological metamaterials while retaining computational efficiency. Using such models in subject specific computational analyses would offer new databased theranostic routes that incorporate whole organ/system behaviour. This project aims at developing biophysically informed neural network based constitutive models for biologic metamaterials. Our objectives are:

  • (i) Develop a templated constitutive modelling framework for disparate materials such as bone, lung, and brain tissue based on tensor informed neural networks (TINNs) or more general biophysical informed neural networks
  • (ii) Integrate the template into an image-based modelling pipeline to realise rapid subject specific modelling of, e.g., oxygen/drug exchange, disturbed cell signalling in bone remodelling, or neurostimulation.
  • (iii) Run a population-based in silico analysis to investigate novel theranostic routes for neurostimulation, mechanobiologically controlled bone healing, or tailored drug deposition.

How to Apply

1. Important Information before you Apply

When applying through the Heriot-Watt on-line system please ensure you provide the following information:

(a) in ‘Study Option’

You will need to select ‘Edinburgh’ and ‘Postgraduate Research’. ‘Programme’ presents you with a drop-down menu. Choose Chemistry PhD, Physics PhD, Chemical Engineering PhD, Mechanical Engineering PhD, Bio-science & Bio-Engineering PhD or Electrical PhD as appropriate and select September 2022 for study option (this can be updated at a later date if required)

(b) in ‘Research Project Information’

You will be provided with a free text box for details of your research project. Enter Title and Reference number of the project for which you are applying and also enter the potential supervisor’s name.

This information will greatly assist us in tracking your application.

Please note that once you have submitted your application, it will not be considered until you have uploaded your CV and transcripts. 

Funding Notes

There are a number of scholarships available which offer funding from between 3 and 3.5 years at an average stipend rate of £15,000 per year.
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