A mathematical model of cell population dynamics with autophagy response to starvation

Jin, Hai; Lei, Jinzhi

doi:10.1016/j.mbs.2014.08.014

Cited by 9 publications

(10 citation statements)

References 24 publications

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“…For example, response to cellular starvation is an intrinsic property of autophagy and was mathematically addressed by Jin et al . [ 44 ]. They classified the cells to normal phase and autophagic phase, and by taking nutrition as the third variable, a logistic type (3D) model of yeast cell population was constructed and analyzed.…”

Section: Mathematical Modeling For Autophagymentioning

confidence: 99%

“…Mathematical models have been combined with experimental studies to decipher the complex dynamics of macro autophagy. An application of mathematical modeling is to study the effect of starvation-induced autophagy in cell (yeast) population dynamics [ 44 ]. Again, these models study the dynamics of the proteins that govern and modulate macro-autophagy at different phases.…”

Section: Introductionmentioning

confidence: 99%

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Tracing the footsteps of autophagy in computational biology

Sarmah

Bairagi

Chatterjee

2020

Briefings in Bioinformatics

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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.

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Section: Mathematical Modeling For Autophagymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Tracing the footsteps of autophagy in computational biology

Sarmah

Bairagi

Chatterjee

2020

Briefings in Bioinformatics

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“…A simplification of the network including only the core nodes was made [5]. In addition, a model with a logistic growth was developed to study the role of autophagy in yeast cells, and it was confirmed that autophagy was valuable for maintaining cell populations [20]. In this paper, a new model is developed by integrating the models of autophagy and apoptosis so as to study the interplay of these two processes, as shown in Fig.…”

Section: The Core Networkmentioning

confidence: 99%

Fate decisions mediated by crosstalk of autophagy and apoptosis in mammalian cells

Wang

2020

J Biol Phys

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Autophagy is an important cell activity which is the process of formation of autophagosomes, docking with lysosomes and degradation. The intrinsic pathway of apoptosis involves mitochondrial outer membrane permeabilization (MOMP) and cytochrome c release followed by caspase activation. Many molecules, e.g., Ca 2+ and mTOR, and different stresses such as endoplasmic reticulum (ER) stress and nutritional stress take part in these two processes. However, the mechanism of how they work together so as to determine cell fate decisions remains to be clarified. Here, we present a computational model for cell fate decisions based on intertwined dynamics with autophagy and apoptosis involving Ca 2+ , mTOR, and both ER stress and nutritional stress. In agreement with experimental observations, the model predicts that both Ca 2+ and the stresses play critical roles in regulating the choice between autophagy and apoptosis in a combinatorial way. The model presented here might be a good candidate for providing the qualitative mechanism of cell fate decisions mediated by Ca 2+ , mTOR, and two kinds of stress.

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“…Autophagy, a potential drug target for the treatment of PD, could protect neurons from injuries [31,32]. In order to confirm the effect of URE on autophagy, autophagy biomarkers LC3-I and LC3-II were detected by Western blotting analysis.…”

Section: Cellular Physiology and Biochemistry Cellular Physiology Andmentioning

confidence: 99%

Uncaria rhynchophylla Ameliorates Parkinson’s Disease by Inhibiting HSP90 Expression: Insights from Quantitative Proteomics

Lan

Zhou

Liu

et al. 2018

Cell Physiol Biochem

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Background/Aims: Uncaria rhynchophylla, known as “Gou-teng”, is a traditional Chinese medicine (TCM) used to extinguish wind, clear heat, arrest convulsions, and pacify the liver. Although U. rhynchophylla has a long history of being often used to treat central nervous system (CNS) diseases, its efficacy and potential mechanism are still uncertain. This study investigated neuroprotective effect and the underlying mechanism of U. rhynchophylla extract (URE) in MPP⁺-induced SH-SY5Y cells and MPTP-induced mice. Methods: MPP⁺-induced SH-SY5Y cells and MPTP-induced mice were used to established Parkinson’s disease (PD) models. Quantitative proteomics and bioinformatics were used to uncover proteomics changes of URE. Western blotting was used to validate main differentially expressed proteins and test HSP90 client proteins (apoptosis-related, autophagy-related, MAPKs, PI3K, and AKT proteins). Flow cytometry and JC-1 staining assay were further used to confirm the effect of URE on MPP⁺-induced apoptosis in SH-SY5Y cells. Gait analysis was used to detect the behavioral changes in MPTP-induced mice. The levels of dopamine (DA) and their metabolites were examined in striatum (STR) by HPLC-EC. The positive expression of tyrosine hydroxylase (TH) was detected by immunohischemical staining and Western blotting. Results: URE dose-dependently increased the cell viability in MPP⁺-induced SH-SY5Y cells. Quantitative proteomics and bioinformatics results confirmed that HSP90 was an important differentially expressed protein of URE. URE inhibited the expression of HSP90, which further reversed MPP⁺-induced cell apoptosis and autophagy by increasing the expressions of Bcl-2, Cyclin D1, p-ERK, p-PI3K p85, PI3K p110α, p-AKT, and LC3-I and decreasing cleaved caspase 3, Bax, p-JNK, p-p38, and LC3-II. URE also markedly decreased the apoptotic ratio and elevated mitochondrial transmembrane potential (DΨ_m). Furthermore, URE treatment ameliorated behavioral impairments, increased the contents of DA and its metabolites and elevated the positive expressions of TH in SN and STR as well as the TH protein. Conclusions: URE possessed the neuroprotective effect in vivo and in vitro, regulated MAPK and PI3K-AKT signal pathways, and inhibited the expression of HSP90. U. rhynchophylla has potentials as therapeutic agent in PD treatment.

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A mathematical model of cell population dynamics with autophagy response to starvation

Cited by 9 publications

References 24 publications

Tracing the footsteps of autophagy in computational biology

Tracing the footsteps of autophagy in computational biology

Fate decisions mediated by crosstalk of autophagy and apoptosis in mammalian cells

Uncaria rhynchophylla Ameliorates Parkinson’s Disease by Inhibiting HSP90 Expression: Insights from Quantitative Proteomics

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