ArticleBMC systems biology2013
Detecting cellular reprogramming determinants by differential stability analysis of gene regulatory networks.
Article in BMC systems biology, 2013. 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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18 citing papers in PubMed, 43 citations in OpenAlex.
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- Data-Driven Modeling of Breast Cancer Tumors Using Boolean Networks.Frontiers in big data · 2021Article
- Synapse alterations precede neuronal damage and storage pathology in a human cerebral organoid model of CLN3-juvenile neuronal ceroid lipofuscinosis.Acta neuropathologica communications · 2019Article
- Reprogramming cooperative monotone dynamical systems.Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control · 2018Article
- Therapeutic target discovery using Boolean network attractors: improvements of kali.Royal Society open science · 2018Article
- Single-cell gene expression analysis reveals regulators of distinct cell subpopulations among developing human neurons.Genome research · 2017Article
- Mathematical models of cell phenotype regulation and reprogramming: Make cancer cells sensitive again!Biochimica et biophysica acta. Reviews on cancer · 2017Review
- Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation.BioEssays : news and reviews in molecular, cellular and developmental biology · 2016Review
- Prediction of disease-gene-drug relationships following a differential network analysis.Cell death & disease · 2016Article
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- Role of Hepatic-Specific Transcription Factors and Polycomb Repressive Complex 2 during Induction of Fibroblasts to Hepatic Fate.PloS one · 2016Article
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- TRIM32 modulates pluripotency entry and exit by directly regulating Oct4 stability.Scientific reports · 2015Article
- Predicting involvement of polycomb repressive complex 2 in direct conversion of mouse fibroblasts into induced neural stem cells.Stem cell research & therapy · 2015Article
- Discrete Logic Modelling Optimization to Contextualize Prior Knowledge Networks Using PRUNET.PloS one · 2015Article
- Reprogramming multistable monotone systems with application to cell fate control.IEEE transactions on network science and engineeringArticle
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCellular differentiation and reprogramming are processes that are carefully orchestrated by the activation and repression of specific sets of genes. An increasing amount of experimental results show that despite the large number of genes participating in transcriptional programs of cellular phenotypes, only few key genes, which are coined here as reprogramming determinants, are required to be directly perturbed in order to induce cellular reprogramming. However, identification of reprogramming determinants still remains a combinatorial problem, and the state-of-art methods addressing this issue rests on exhaustive experimentation or prior knowledge to narrow down the list of candidates.
resultsHere we present a computational method, without any preliminary selection of candidate genes, to identify reduced subsets of genes, which when perturbed can induce transitions between cellular phenotypes. The method relies on the expression profiles of two stable cellular phenotypes along with a topological analysis stability elements in the gene regulatory network that are necessary to cause this multi-stability. Since stable cellular phenotypes can be considered as attractors of gene regulatory networks, cell fate and cellular reprogramming involves transition between these attractors, and therefore current method searches for combinations of genes that are able to destabilize a specific initial attractor and stabilize the final one in response to the appropriate perturbations.
conclusionsThe method presented here represents a useful framework to assist researchers in the field of cellular reprogramming to design experimental strategies with potential applications in the regenerative medicine and disease modelling.
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