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Updated: Aug 6, 2025
06:51
Techniques to Induce and Quantify Cellular Senescence
Published on: May 1, 2017
33.6K
Dynamic modeling of the cellular senescence gene regulatory network
José Américo Nabuco Leva Ferreira de Freitas1,2,3, Oliver Bischof1
1IMRB, Mondor Institute for Biomedical Research, INSERM U955 - Université Paris Est Créteil, UPEC, Faculté de Médecine de Créteil 8, rue du Général Sarrail, 94010 Créteil.
Heliyon
|March 20, 2023
Summary
We developed a data-driven method to map the gene regulatory network controlling cellular senescence. This mathematical platform aids in understanding senescence and may lead to clinical discoveries.
Area of Science:
- Cellular and Molecular Biology
- Systems Biology
- Computational Biology
Background:
- Cellular senescence is a crucial cell fate influencing health and disease.
- Understanding the gene regulatory network (GRN) governing senescence is complex.
- Gene expression changes significantly during senescence induction.
Purpose of the Study:
- To reconstruct the gene regulatory network (GRN) for cellular senescence.
- To develop a data-driven mathematical platform for senescence modeling.
- To enable *in silico* hypothesis testing for potential clinical impact.
Main Methods:
- Integrated time-series transcriptome and transcription factor depletion data.
- Employed the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm to infer differential equations.
- Validated the reconstructed GRN using independent time-point data.
Main Results:
- Successfully inferred a GRN for cellular senescence.
- Identified genes with potential hidden regulators.
- Demonstrated a robust, data-driven approach for GRN reconstruction.
Conclusions:
- This work presents a proof of concept for a powerful mathematical platform for senescence modeling.
- The developed method can accelerate the understanding of senescence mechanisms.
- The approach holds potential for future clinical discoveries related to senescence.
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