Neurodegenerative diseases and nerve injury are among the most formidable medical conditions today due to their complicated molecular basis, multiclonal clinical presentations, and restricted therapeutic options. Computational biology has revolutionized the tools available for studying these diseases by combining information from multiple high-throughput platforms, such as genomics, transcriptomics, epigenomics, proteomics, and metabolomics. Using computational pipelines, scientists have discovered dysregulated pathways, miRNA biomarkers, and epigenetic changes that underlie neuropathic pain and chronic nerve damage. For instance, bioinformatic approaches have identified the regulatory function of certain miRNAs in ion channel function, neuroinflammation, and neuronal excitability, while epigenetic analysis has accounted for the chronicity of pain by way of histone-based gene silencing. Multi-omics integration in neurodegeneration has found there are common molecular signatures across Alzheimer’s, Parkinson’s, and amyotrophic lateral sclerosis with implications for shared pathways of stress response, apoptosis, and synaptic dysfunction. Computational modeling, such as machine learning and network biology, also facilitates the discovery of predictive biomarkers and regulatory hubs as potential therapeutic targets. Notably, these approaches are also useful in uncommon neuropathies, when patient groups are small and for direct experimentation, usually restricted. In the future, the intersection of AI-enabled analytics, single-cell and spatial omics, and systemslevel network modeling will allow for improved precision diagnostics and inform personalized therapy approaches. So, computational biology not only enhances our mechanistic insight into nerve injury but also has the transformative power of personalized medicine in neuropathies and neurodegenerative diseases.
Review Article
English
P. 218-228
