ArticleIn silico biology2021
Reverse engineering of a mechanistic model of gene expression using metastability and temporal dynamics.
Article in In silico biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling.PLoS computational biology · 2026Article
- Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression.NPJ systems biology and applications · 2026Article
- Navigating the landscape of direct cellular reprogramming with DiReG.NPJ systems biology and applications · 2026Article
- SHISMA: SHape-driven inference of significant cell type-specific subnetworks from tiMe series single-cell trAnscriptomics.Bioinformatics advances · 2026Article
- Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference.Scientific reports · 2025Article
- Efficient construction of Markov state models for stochastic gene regulatory networks by domain decomposition.BMC bioinformatics · 2025Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- One model fits all: Combining inference and simulation of gene regulatory networks.PLoS computational biology · 2023Article
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
- Evidence for close molecular proximity between reverting and undifferentiated cells.BMC biology · 2022Article
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1 author.
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Abstract
Differentiation can be modeled at the single cell level as a stochastic process resulting from the dynamical functioning of an underlying Gene Regulatory Network (GRN), driving stem or progenitor cells to one or many differentiated cell types. Metastability seems inherent to differentiation process as a consequence of the limited number of cell types. Moreover, mRNA is known to be generally produced by bursts, which can give rise to highly variable non-Gaussian behavior, making the estimation of a GRN from transcriptional profiles challenging. In this article, we present CARDAMOM (Cell type Analysis from scRna-seq Data achieved from a Mixture MOdel), a new algorithm for inferring a GRN from timestamped scRNA-seq data, which crucially exploits these notions of metastability and transcriptional bursting. We show that such inference can be seen as the successive resolution of as many regression problem as timepoints, after a preliminary clustering of the whole set of cells with regards to their associated bursts frequency. We demonstrate the ability of CARDAMOM to infer a reliable GRN from in silico expression datasets, with good computational speed. To the best of our knowledge, this is the first description of a method which uses the concept of metastability for performing GRN inference.
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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.