ArticleComputational and structural biotechnology journal2020
Landscape inferred from gene expression data governs pluripotency in embryonic stem cells.
Article in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling.Current stem cell reports · 2023Review
- The Art of Reprogramming for Regenerative Medicine.Frontiers in cell and developmental biology · 2022Review
- A Dimension Reduction Approach for Energy Landscape: Identifying Intermediate States in Metabolism-EMT Network.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
- Dual roles of SIRT1 in the BAX switch through the P53 module: A mathematical modeling study.Computational and structural biotechnology journal · 2021Article
- Probing pluripotency gene regulatory networks with quantitative live cell imaging.Computational and structural biotechnology journal · 2020Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Embryonic stem cells (ESCs) can differentiate into diverse cell types and have the ability of self-renewal. Therefore, the study of cell fate decisions on embryonic stem cells has far-reaching significance for regenerative medicine and other biomedical fields. Mathematical models have been used to study emryonic stem cell differentiation. However, the underlying mechanisms of cell differentiation and lineage reprogramming remain to be elucidated. Especially, how to integrate the computational models with quantitative experimental data is still challenging. In this work, we developed a data-constrained modelling approach, and established a model of mouse embryonic stem cells. We used the truncated moment equations (TME) method to quantify the potential landscape of the ESC network. We identified two attractors on the landscape, which represent the embryonic stem cell (ESC) state and differentiated cell (DC) state, respectively, and quantified high dimensional biological paths for differentiation and reprogramming process. Through identifying the optimal combinations of gene targets based on a landscape control strategy, we offered some predictions about the key regulatory factors that govern the differentiation and reprogramming in ESCs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.