ArticlePLoS computational biology2023
One model fits all: Combining inference and simulation of gene regulatory networks.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Cell trajectory inference based on schrödinger problem and a mechanistic model of stochastic gene expression.NPJ systems biology and applications · 2026Article
- Trajectory inference for a branching SDE model of cell differentiation via lineage tracing.Journal of mathematical biology · 2026Article
- Multiscale modeling of the spatial structure of stem cells in neuroblastoma patient-derived tumoroids reveals a critical role for a short-range diffusive process.PLoS computational biology · 2026Article
- Navigating the landscape of direct cellular reprogramming with DiReG.NPJ systems biology and applications · 2026Article
- Gene regulatory network inference with popInfer reveals the dynamic regulation of hematopoietic stem cell quiescence.iScience · 2025Article
- Learning stochastic processes with intrinsic noise from cross-sectional biological data.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Machine learning methods for gene regulatory network inference.Briefings in bioinformatics · 2025Review
- Dynamic gene regulatory network inference from single-cell data using optimal transport.Bioinformatics (Oxford, England) · 2025Article
- Uncovering candidate Nanog-Helper genes in early mouse embryo differentiation using differential entropy and network inference.Scientific reports · 2025Article
- Optimal transport reveals dynamic gene regulatory networks via gene velocity estimation.PLoS computational biology · 2025Article
- Modeling relaxation experiments with a mechanistic model of gene expression.BMC bioinformatics · 2024Article
- Reconstruction of gene regulatory networks for Caenorhabditis elegans using tree-shaped gene expression data.Briefings in bioinformatics · 2024Article
- Approaches for Benchmarking Single-Cell Gene Regulatory Network Methods.Bioinformatics and biology insights · 2024Review
- scTIGER: A Deep-Learning Method for Inferring Gene Regulatory Networks from Case versus Control scRNA-seq Datasets.International journal of molecular sciences · 2023Article
- From time-series transcriptomics to gene regulatory networks: A review on inference methods.PLoS computational biology · 2023Review
- Computational approaches to understand transcription regulation in development.Biochemical Society transactions · 2023Review
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The rise of single-cell data highlights the need for a nondeterministic view of gene expression, while offering new opportunities regarding gene regulatory network inference. We recently introduced two strategies that specifically exploit time-course data, where single-cell profiling is performed after a stimulus: HARISSA, a mechanistic network model with a highly efficient simulation procedure, and CARDAMOM, a scalable inference method seen as model calibration. Here, we combine the two approaches and show that the same model driven by transcriptional bursting can be used simultaneously as an inference tool, to reconstruct biologically relevant networks, and as a simulation tool, to generate realistic transcriptional profiles emerging from gene interactions. We verify that CARDAMOM quantitatively reconstructs causal links when the data is simulated from HARISSA, and demonstrate its performance on experimental data collected on in vitro differentiating mouse embryonic stem cells. Overall, this integrated strategy largely overcomes the limitations of disconnected inference and simulation.
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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.