Tracing the footsteps of autophagy in computational biology

Abstract

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.

Towards AI-driven longevity research: An overview

“…An autophagy flux sensor, named red-green-blue-LC3 (RGB-LC3) was developed to detect the different footsteps of autophagy progression and the deregulation of this process at different levels (Kim et al, 2020). In addition, different computational methods have been applied for the mathematical modeling of the core regulatory machine of autophagy (Sarmah et al, 2021). The integration of different data types can widen our knowledge of the molecular mechanisms governing autophagy.…”

“…The integration of different data types can widen our knowledge of the molecular mechanisms governing autophagy. This could be helpful in the context of the development of targeted therapies (Sarmah et al, 2021).…”

Bidirectional regulation between AP-1 and SUMOylation pathway genes modulates inflammatory signaling duringSalmonellainfection

“…The networks for each category are showed in (Figure S5A-C). PPI networks can be used to tailor the complex interactions between proteins, and their efficient analysis can reveal the core group driving the system's perturbations (38). To identify the core set of proteins, we opted for structural controllability analysis that searches for a minimal number of driver nodes that are sufficient to control the network, i.e., changes in the state of these nodes are sufficient to drive the system from any initial to any final states.…”

Perturbation-Based Modeling Unveils the Autophagic Modulation of Chemosensitivity and Immunogenicity in Breast Cancer Cells

“…Systems biology acts as a hatchet to unveil the underlying principles governing biological processes through the definition of how its components are organized and interlinked. By implementing various algorithms to analyze a network, the core modulators can be detected, and the dynamics of these core sets of modulators can be studied through mathematical modeling [ 31 ]. These strategies were used to assess autophagy in the context of neurodegenerative diseases [ 32 ], endoplasmic reticulum stress [ 33 ], lung cancer [ 34 ], and many other physiological and pathological processes [ 31 ].…”

“…By implementing various algorithms to analyze a network, the core modulators can be detected, and the dynamics of these core sets of modulators can be studied through mathematical modeling [ 31 ]. These strategies were used to assess autophagy in the context of neurodegenerative diseases [ 32 ], endoplasmic reticulum stress [ 33 ], lung cancer [ 34 ], and many other physiological and pathological processes [ 31 ]. Moreover, in perturbation-based models, changes in the outputs are attributed to perturbed inputs and used to estimate their importance for a particular instance.…”