ArticleProceedings of the National Academy of Sciences of the United States of America2018
Distribution shapes govern the discovery of predictive models for gene regulation.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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61 citing papers in PubMed, 120 citations in OpenAlex.
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- Monod: model-based discovery and integration through fitting stochastic transcriptional dynamics to single-cell sequencing data.Nature methods · 2025Article
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- Transcriptional stochasticity reveals multiple mechanisms of long non-coding RNA regulation at the Xist-Tsix locus.Nature communications · 2025Article
- Incorporating spatial diffusion into models of bursty stochastic transcription.Journal of the Royal Society, Interface · 2025Article
- Transient power-law behaviour following induction distinguishes between competing models of stochastic gene expression.Nature communications · 2025Article
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- Single-cell heterogeneity in ribosome content and the consequences for the growth laws.bioRxiv : the preprint server for biology · 2024Article
- Spectral neural approximations for models of transcriptional dynamics.Biophysical journal · 2024Article
- Biophysical modeling with variational autoencoders for bimodal, single-cell RNA sequencing data.Nature methods · 2024Article
- Solving stochastic gene-expression models using queueing theory: A tutorial review.Biophysical journal · 2024Review
- What can we learn when fitting a simple telegraph model to a complex gene expression model?PLoS computational biology · 2024Article
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- Quantifying and correcting bias in transcriptional parameter inference from single-cell data.Biophysical journal · 2024Article
- Phenotypic consequences of logarithmic signaling in MAPK stress response.bioRxiv : the preprint server for biology · 2023Article
- Review
1 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Despite substantial experimental and computational efforts, mechanistic modeling remains more predictive in engineering than in systems biology. The reason for this discrepancy is not fully understood. One might argue that the randomness and complexity of biological systems are the main barriers to predictive understanding, but these issues are not unique to biology. Instead, we hypothesize that the specific shapes of rare single-molecule event distributions produce substantial yet overlooked challenges for biological models. We demonstrate why modern statistical tools to disentangle complexity and stochasticity, which assume normally distributed fluctuations or enormous datasets, do not apply to the discrete, positive, and nonsymmetric distributions that characterize mRNA fluctuations in single cells. As an example, we integrate single-molecule measurements and advanced computational analyses to explore mitogen-activated protein kinase induction of multiple stress response genes. Through systematic analyses of different metrics to compare the same model to the same data, we elucidate why standard modeling approaches yield nonpredictive models for single-cell gene regulation. We further explain how advanced tools recover precise, reproducible, and predictive understanding of transcription regulation mechanisms, including gene activation, polymerase initiation, elongation, mRNA accumulation, spatial transport, and decay.
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