Although motor deficits and memory issues characterize neurodegenerative diseases such as Parkinson’s (PD) and Alzheimer’s (AD), recent research has focused on the non-motor or cognitive deficiencies associated with these conditions. AD is one of the leading causes of dementia and studies have shown that more than 80% of PD patients develop dementia during the progression of the disease. This affects the daily activities of the patients as well as the caregivers and family. Despite all this evidence, no specific treatment or drug exists to cure the diseases. One of the major reasons is the disease mechanisms are poorly understood, especially how the cellular mechanisms are involved. Understanding the initial causes may help to identify exact mechanisms or how they spread between neurons at different regions of the brain involved in cognitive functioning. Biochemical network modelling, such as signal transduction and gene regulatory circuits, may aid in the study of experimentally immeasurable biological processes and for which animal models reproduce only certain pathological features.
Our research focus mainly on computational modelling of cellular and molecular networks including biochemical reactions, signal transduction and gene regulatory circuits in neurodegenerative diseases. In the case of experimentally immeasurable biological processes, such models can be used to observe and analyse the behaviour of a particular variable. The behaviour of these hidden system states can be crucial to understand the performance of biological systems where measurement is difficult or impractical. The applications of mathematical modelling have particular relevance to study neurodegenerative diseases including PD and AD, which are unique to the human brain and for which animal models reproduce only certain pathological features. Mapping cellular level predictions from this model to clinical symptoms and cognitive deficits of neurological disorders would help clinicians to diagnose the disease and initiate an early-stage treatment to delay the progression. This would help to build a digital twin to aid clinicians in early diagnosis and to initiate personalized treatment to delay the disease progression.
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