ArticleProceedings of the National Academy of Sciences of the United States of America2014
Mathematical approaches to modeling development and reprogramming.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- hiPSCGEM01: A Genome-Scale Metabolic Model for Fibroblast-Derived Human iPSCs.Bioengineering (Basel, Switzerland) · 2025Article
- Modeling combinatorial regulation from single-cell multi-omics provides regulatory units underpinning cell type landscape using cRegulon.Genome biology · 2025Article
- Colony context and size-dependent compensation mechanisms give rise to variations in nuclear growth trajectories.Cell systems · 2025Article
- Is There a Need for a More Precise Description of Biomolecule Interactions to Understand Cell Function?Current issues in molecular biology · 2022Review
- Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.Nature communications · 2021Article
- The complex landscape of haematopoietic lineage commitments is encoded in the coarse-grained endogenous network.Royal Society open science · 2021Article
- Inference of high-resolution trajectories in single-cell RNA-seq data by using RNA velocity.Cell reports methods · 2021Article
- Adaptive Landscape Shaped by Core Endogenous Network Coordinates Complex Early Progenitor Fate Commitments in Embryonic Pancreas.Scientific reports · 2020Article
- Article
- Lineage marker synchrony in hematopoietic genealogies refutes the PU.1/GATA1 toggle switch paradigm.Nature communications · 2018Article
- Fundamental limits on dynamic inference from single-cell snapshots.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
- Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells.Cell reports · 2016Article
- Oncometabolic Nuclear Reprogramming of Cancer Stemness.Stem cell reports · 2016Article
- Effects of Collective Histone State Dynamics on Epigenetic Landscape and Kinetics of Cell Reprogramming.Scientific reports · 2015Article
- Increased robustness of early embryogenesis through collective decision-making by key transcription factors.BMC systems biology · 2015Article
- Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape.Proceedings of the National Academy of Sciences of the United States of America · 2014Article
- Reprogramming by lineage specifiers: blurring the lines between pluripotency and differentiation.Current opinion in genetics & development · 2014Review
- Metabostemness: a new cancer hallmark.Frontiers in oncology · 2014Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Induced pluripotent stem cells (iPSCs) are created by the reprogramming of somatic cells via overexpression of certain transcription factors, such as the originally described Yamanaka factors: Oct4, Sox2, Klf4, and c-Myc (OSKM). Here we discuss recent advancements in iPSC reprogramming and introduce mathematical approaches to help map the landscape between cell states during reprogramming. Our modelization indicates that OSKM expression diminishes and/or changes potential barriers between cell states and that epigenetic remodeling facilitate these transitions. From a practical perspective, the modeling approaches outlined here allow us to predict the time necessary to create a given number of iPSC colonies or the number of reprogrammed cells generated in a given time. Additional investigations will help to further refine modeling strategies, rendering them applicable toward the study of the development and stability of cancer cells or even other reprogramming processes such as lineage conversion. Ultimately, a quantitative understanding of cell state transitions might facilitate the establishment of regenerative medicine strategies and enhance the translation of reprogramming technologies into the clinic.
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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.