Development of network methodologies for brain data modelling
Lead Research Organisation:
University of Cambridge
Department Name: Computer Science and Technology
Abstract
Development of computational models of biological systems is becoming extremely important in order to
understand complex biological behaviours [1]. In particular, the so-called "executable biology" focuses on
the design of executable computer algorithms that mimic the behaviour of biological processes. Indeed, to
describe biological processes one can use mathematical and computational models, simulating biological
behaviours. Understanding the behaviour of biological systems, that for example underlie a disease, could
be fundamental in order to build algorithms for runtime monitoring of patients.
Biomedical signal monitoring devices are particularly important for the health system of a person. This is
because, for example, such devices could be able to detect the presence of an incoming heart attack, or a
diabetes crisis for patients exposed to potential risks.
The challenge of this research therefore would be to develop new algorithms to detect specific critical events
for patients, by interacting with Cyber-Physical Systems (CPS). CPS is the integration of physical processes,
jointly combined with computation and communication [2].
The goal of the research will be to develop ad-hoc algorithms to process real time data, coming from the
sensors and therefore detect critical situations. This could be done interfacing my algorithm with physical
sensors, or building ad-hoc applications for smartphones and laptops with which the user could interface
directly. The data so collected would then be analysed through the algorithm implemented and the output
would be the identification of a solution for the relevant problem. The research would then lead to building
a run time monitoring systems for patients.
This PhD proposal combines many different aspects and could give rise to different possible collaborations. It
will be interdisciplinary involving engineering, biology, medicine, computer science and it will include
aspects of run time monitoring, model checking and formal methods.
[1] J. Fisher and T. Henzinger, "Executable cell biology," Nature Biotechnology, vol. 25, pp. 1239-1249,
2007.
[2] S. Haque, S. Aziz and M. Rahman, "Review of Cyber-Physical System in Healthcare," International
Journal of Distributed Sensor Networks , p. 20, 2014.
understand complex biological behaviours [1]. In particular, the so-called "executable biology" focuses on
the design of executable computer algorithms that mimic the behaviour of biological processes. Indeed, to
describe biological processes one can use mathematical and computational models, simulating biological
behaviours. Understanding the behaviour of biological systems, that for example underlie a disease, could
be fundamental in order to build algorithms for runtime monitoring of patients.
Biomedical signal monitoring devices are particularly important for the health system of a person. This is
because, for example, such devices could be able to detect the presence of an incoming heart attack, or a
diabetes crisis for patients exposed to potential risks.
The challenge of this research therefore would be to develop new algorithms to detect specific critical events
for patients, by interacting with Cyber-Physical Systems (CPS). CPS is the integration of physical processes,
jointly combined with computation and communication [2].
The goal of the research will be to develop ad-hoc algorithms to process real time data, coming from the
sensors and therefore detect critical situations. This could be done interfacing my algorithm with physical
sensors, or building ad-hoc applications for smartphones and laptops with which the user could interface
directly. The data so collected would then be analysed through the algorithm implemented and the output
would be the identification of a solution for the relevant problem. The research would then lead to building
a run time monitoring systems for patients.
This PhD proposal combines many different aspects and could give rise to different possible collaborations. It
will be interdisciplinary involving engineering, biology, medicine, computer science and it will include
aspects of run time monitoring, model checking and formal methods.
[1] J. Fisher and T. Henzinger, "Executable cell biology," Nature Biotechnology, vol. 25, pp. 1239-1249,
2007.
[2] S. Haque, S. Aziz and M. Rahman, "Review of Cyber-Physical System in Healthcare," International
Journal of Distributed Sensor Networks , p. 20, 2014.
Organisations
People |
ORCID iD |
| Pietro Lio (Primary Supervisor) | |
| Giovanna Dimitri (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509620/1 | 01/10/2016 | 30/09/2022 | |||
| 1649557 | Studentship | EP/N509620/1 | 01/10/2015 | 31/03/2019 | Giovanna Dimitri |
| Description | Thanks to grant I had the hounour to be assigned with, I was able to pursue my research regarding brain data modelling and analysis. I have been involved in analysis regarding traumatized pediatric patients. In particular in my research I have concentrated on the relationship between raise in intracranial pressure and heart rate, relating this events to mortality and patients outcome. This has lead to interesting and important understanding of such events, and the potential impact of this could be in a better treatment and analysis of critical patients. My line of research had also developed towards a more theoretical way, in particular investigating multilayer network modelings, for the integration of different types of data sources. In particular I have investigated community detection algorithm and applied it to the study of healthy cognitive ageing. I have now finished succesfully my PhD and graduated on the 18th of July 2020. |
| Exploitation Route | The research I have conducted regarding traumatised brain patients, has a direct impact with respect to the clinical use. Same can be said for the healthy ageing analysis I have conducted, that has helped analysing healthy cognitive processes in a deeper way and making use of the mathematical modelling and analysis. |
| Sectors | Healthcare,Pharmaceuticals and Medical Biotechnology |