Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases.
Neurodegenerative diseases are a group of disorders characterized by progressive degeneration or death of neurons. The complexity of clinical symptoms and irreversibility of disease progression significantly affects individual lives, leading to premature mortality. The prevalence of neurodegenerativ...
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| Format: | Article |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012743 |
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| author | Peng Ren Xuehua Cui Xia Liang |
| author_facet | Peng Ren Xuehua Cui Xia Liang |
| author_sort | Peng Ren |
| collection | DOAJ |
| description | Neurodegenerative diseases are a group of disorders characterized by progressive degeneration or death of neurons. The complexity of clinical symptoms and irreversibility of disease progression significantly affects individual lives, leading to premature mortality. The prevalence of neurodegenerative diseases keeps increasing, yet the specific pathogenic mechanisms remain incompletely understood and effective treatment strategies are lacking. In recent years, convergent experimental evidence supports the "prion-like transmission" assumption that abnormal proteins induce misfolding of normal proteins, and these misfolded proteins propagate throughout the neural networks to cause neuronal death. To elucidate this dynamic process in vivo from a computational perspective, researchers have proposed three connectome-based biophysical models to simulate the spread of pathological proteins: the Network Diffusion Model, the Epidemic Spreading Model, and the agent-based Susceptible-Infectious-Removed model. These models have demonstrated promising predictive capabilities. This review focuses on the explanations of their fundamental principles and applications. Then, we compare the strengths and weaknesses of the models. Building upon this foundation, we introduce new directions for model optimization and propose a unified framework for the evaluation of connectome-based biophysical models. We expect that this review could lower the entry barrier for researchers in this field, accelerate model optimization, and thereby advance the clinical translation of connectome-based biophysical models. |
| format | Article |
| id | doaj-art-1b980ed11aad416e8c629dbcd7aa9fc5 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-1b980ed11aad416e8c629dbcd7aa9fc52025-08-20T03:52:37ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-01-01211e101274310.1371/journal.pcbi.1012743Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases.Peng RenXuehua CuiXia LiangNeurodegenerative diseases are a group of disorders characterized by progressive degeneration or death of neurons. The complexity of clinical symptoms and irreversibility of disease progression significantly affects individual lives, leading to premature mortality. The prevalence of neurodegenerative diseases keeps increasing, yet the specific pathogenic mechanisms remain incompletely understood and effective treatment strategies are lacking. In recent years, convergent experimental evidence supports the "prion-like transmission" assumption that abnormal proteins induce misfolding of normal proteins, and these misfolded proteins propagate throughout the neural networks to cause neuronal death. To elucidate this dynamic process in vivo from a computational perspective, researchers have proposed three connectome-based biophysical models to simulate the spread of pathological proteins: the Network Diffusion Model, the Epidemic Spreading Model, and the agent-based Susceptible-Infectious-Removed model. These models have demonstrated promising predictive capabilities. This review focuses on the explanations of their fundamental principles and applications. Then, we compare the strengths and weaknesses of the models. Building upon this foundation, we introduce new directions for model optimization and propose a unified framework for the evaluation of connectome-based biophysical models. We expect that this review could lower the entry barrier for researchers in this field, accelerate model optimization, and thereby advance the clinical translation of connectome-based biophysical models.https://doi.org/10.1371/journal.pcbi.1012743 |
| spellingShingle | Peng Ren Xuehua Cui Xia Liang Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. PLoS Computational Biology |
| title | Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. |
| title_full | Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. |
| title_fullStr | Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. |
| title_full_unstemmed | Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. |
| title_short | Connectome-based biophysical models of pathological protein spreading in neurodegenerative diseases. |
| title_sort | connectome based biophysical models of pathological protein spreading in neurodegenerative diseases |
| url | https://doi.org/10.1371/journal.pcbi.1012743 |
| work_keys_str_mv | AT pengren connectomebasedbiophysicalmodelsofpathologicalproteinspreadinginneurodegenerativediseases AT xuehuacui connectomebasedbiophysicalmodelsofpathologicalproteinspreadinginneurodegenerativediseases AT xialiang connectomebasedbiophysicalmodelsofpathologicalproteinspreadinginneurodegenerativediseases |