Therapy design for neurodegenerative diseases via nonlinear control
Lead Research Organisation:
Imperial College London
Department Name: Electrical and Electronic Engineering
Abstract
Neurodegenerative diseases are chronic nervous system medical conditions that are caused by the progressive death of nerve cells of the various nervous system regions, such as the brain, the spinal cord or the peripheral nervous system. Among the most common neurodegenerative diseases are Alzheimer's disease (AD), Parkinson's disease (PD) and Multiple Sclerosis (MS), with Alzheimer's being the primary cause of impaired mental functionality (dementia) within the middle aged and aged population worldwide. Current treatment techniques for neurodegenerative diseases have limited positive effect in the patients' quality of life with most targeting the symptoms rather than the actual initiation and progression of the disease. On top of that, neurodegenerative diseases exhibit great variability in the patients' clinical phenotypes, thus making the current generalized treatments insufficient and the development of personalized treatment methods highly beneficial.
In an effort to understand the complex behavior of the nervous system and effectively the mechanisms underlying the neurodegenerative diseases, multiple mathematical models to describe the sequence of highly interconnected events leading to cell death have been developed. Many of these models use the tested and scientifically reliable Hodgkin - Huxley (HH) model developed 60 years ago as starting point.
In this project, we are aiming at applying nonlinear control methods to such mathematical models to computationally identify the control inputs that drive the variables responsible for the appearance of the disease to a healthy region of operation. Starting from the process of firing of a single neuron using the original HH model, we will simulate the cell death by identifying the parameters that correspond to a failed neuronal firing. Then, we will simulate the progression of neuron degeneration by creating a network of neurons. Progressively, we will control the firing of a single diseased neuron using advanced nonlinear control methods and eventually control the whole network of neurons using consensus control theory. In parallel, we will explore the relation between AD, PD and MS and integrate all three in the same set of models with the goal of compactly describing their progression. Finally, we will investigate the aspect of treatment personalization by taking into consideration the variability of the neurodegenerative diseases, that directly reflect into the model parameters.
Potentially, this project will contribute to further understanding the progression of neurodegenerative diseases and the conditions under which the mechanism can be stopped or even reversed. It will provide a computational framework to study and visualize the neurodegenerative diseases as well as identify a possible treatment that can be later linked to current pharmaceutical products or other techniques such as deep brain stimulation. This project might lead to a platform, that will use patients' data as an input to simulate their case on a virtual patient, that will determine the appropriate and personalized course of action.
Relevance to EPSRC research area:
- Control Engineering
- Nonlinear Systems
In an effort to understand the complex behavior of the nervous system and effectively the mechanisms underlying the neurodegenerative diseases, multiple mathematical models to describe the sequence of highly interconnected events leading to cell death have been developed. Many of these models use the tested and scientifically reliable Hodgkin - Huxley (HH) model developed 60 years ago as starting point.
In this project, we are aiming at applying nonlinear control methods to such mathematical models to computationally identify the control inputs that drive the variables responsible for the appearance of the disease to a healthy region of operation. Starting from the process of firing of a single neuron using the original HH model, we will simulate the cell death by identifying the parameters that correspond to a failed neuronal firing. Then, we will simulate the progression of neuron degeneration by creating a network of neurons. Progressively, we will control the firing of a single diseased neuron using advanced nonlinear control methods and eventually control the whole network of neurons using consensus control theory. In parallel, we will explore the relation between AD, PD and MS and integrate all three in the same set of models with the goal of compactly describing their progression. Finally, we will investigate the aspect of treatment personalization by taking into consideration the variability of the neurodegenerative diseases, that directly reflect into the model parameters.
Potentially, this project will contribute to further understanding the progression of neurodegenerative diseases and the conditions under which the mechanism can be stopped or even reversed. It will provide a computational framework to study and visualize the neurodegenerative diseases as well as identify a possible treatment that can be later linked to current pharmaceutical products or other techniques such as deep brain stimulation. This project might lead to a platform, that will use patients' data as an input to simulate their case on a virtual patient, that will determine the appropriate and personalized course of action.
Relevance to EPSRC research area:
- Control Engineering
- Nonlinear Systems
Organisations
People |
ORCID iD |
| Alessandro Astolfi (Primary Supervisor) | |
| Anastasia Giannari (Student) |