Tracing the footsteps of autophagy in computational biology
| Autor: | Samrat Chatterjee, Dipanka Tanu Sarmah, Nandadulal Bairagi |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Programmed cell death
AcademicSubjects/SCI01060 Computer science Process (engineering) Systems biology Cellular homeostasis Computational biology autophagy mechanism Models Biological 03 medical and health sciences 0302 clinical medicine Neoplasms Autophagy Humans Molecular Biology network analysis 030304 developmental biology Method Review 0303 health sciences Future perspective Autophagy database apoptosis Computational Biology differential equations System of differential equations 030220 oncology & carcinogenesis autophagy database mathematical models Information Systems Signal Transduction |
| Zdroj: | Briefings in Bioinformatics |
| ISSN: | 1477-4054 |
| Popis: | Autophagy plays a crucial role in maintaining cellular homeostasis through the degradation of unwanted materials like damaged mitochondria and misfolded proteins. However, the contribution of autophagy toward a healthy cell environment is not only limited to the cleaning process. It also assists in protein synthesis when the system lacks the amino acids’ inflow from the extracellular environment due to diet consumptions. Reduction in the autophagy process is associated with diseases like cancer, diabetes, non-alcoholic steatohepatitis, etc., while uncontrolled autophagy may facilitate cell death. We need a better understanding of the autophagy processes and their regulatory mechanisms at various levels (molecules, cells, tissues). This demands a thorough understanding of the system with the help of mathematical and computational tools. The present review illuminates how systems biology approaches are being used for the study of the autophagy process. A comprehensive insight is provided on the application of computational methods involving mathematical modeling and network analysis in the autophagy process. Various mathematical models based on the system of differential equations for studying autophagy are covered here. We have also highlighted the significance of network analysis and machine learning in capturing the core regulatory machinery governing the autophagy process. We explored the available autophagic databases and related resources along with their attributes that are useful in investigating autophagy through computational methods. We conclude the article addressing the potential future perspective in this area, which might provide a more in-depth insight into the dynamics of autophagy. |
| Databáze: | OpenAIRE |
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