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Improving the effectiveness of anti-aging modalities by using the constrained disorder principle-based management
Noa Hurvitz1, Narmine Elkhateeb1, Tal Sigawi1
1Faculty of Medicine, Hebrew University and Department of Medicine, Hadassah Medical Center, Jerusalem, Israel.
Frontiers in Aging
|January 2, 2023
Summary
Biological aging involves complex factors and reduced system variability. This study explores how artificial intelligence can enhance anti-aging therapies by leveraging constrained disorder principles to improve system efficiency and slow aging.
Area of Science:
- Gerontology and Systems Biology
- Biotechnology and Artificial Intelligence
Background:
- Aging is a multifactorial biological process influenced by genetic, environmental, and social factors.
- Biological systems exhibit variability from the genome to organ function, which changes with age.
- The constrained disorder principle posits that aging is associated with reduced variability and complexity.
Purpose of the Study:
- To review aging mechanisms and anti-aging therapies.
- To explore the association between system variability and the hallmarks of aging.
- To introduce second-generation artificial intelligence (AI) systems for enhancing anti-aging strategies.
Main Methods:
- Review of existing literature on aging mechanisms and therapies.
- Analysis of the role of system disorder and variability in aging pathogenesis.
- Conceptualization of AI systems based on the constrained disorder principle.
Main Results:
- Aging is characterized by alterations in system variability and complexity.
- Reduced variability is linked to the pathogenesis of aging.
- Second-generation AI platforms can utilize constrained noise to improve system efficiency.
Conclusions:
- Variability and disorder are key factors in aging.
- AI systems offer a novel approach to modulate aging processes.
- AI has potential applications in managing chronic diseases and supporting the aged population.
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