ArticleProceedings of the National Academy of Sciences of the United States of America2012
Stochastic branching-diffusion models for gene expression.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 37 citations in OpenAlex.
- Learning stochastic reaction-diffusion models from limited data using spatiotemporal features.bioRxiv : the preprint server for biology · 2025Article
- Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.Bulletin of mathematical biology · 2024Article
- Mitigating transcription noise via protein sharing in syncytial cells.Biophysical journal · 2024Article
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- Spatiotemporal Patterning Enabled by Gene Regulatory Networks.ACS omega · 2023Article
- Understanding biochemical processes in the presence of sub-diffusive behavior of biomolecules in solution and living cells.Biophysical reviews · 2019Review
- FGF Gradient Controls Boundary Position Between Proliferating and Differentiating Cells and Regulates Lacrimal Gland Growth Dynamics.Frontiers in genetics · 2019Article
- Cox process representation and inference for stochastic reaction-diffusion processes.Nature communications · 2016Article
- A moment-convergence method for stochastic analysis of biochemical reaction networks.The Journal of chemical physics · 2016Article
- Variance-corrected Michaelis-Menten equation predicts transient rates of single-enzyme reactions and response times in bacterial gene-regulation.Scientific reports · 2015Article
- The utility of simple mathematical models in understanding gene regulatory dynamics.In silico biology · 2015Review
- Space-induced bifurcation in repression-based transcriptional circuits.BMC systems biology · 2014Article
- Article
- Systems biophysics of gene expression.Biophysical journal · 2013Review
- Article
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A challenge to both understanding and modeling biochemical networks is integrating the effects of diffusion and stochasticity. Here, we use the theory of branching processes to give exact analytical expressions for the mean and variance of protein numbers as a function of time and position in a spatial version of an established model of gene expression. We show that both the mean and the magnitude of fluctuations are determined by the protein's Kuramoto length--the typical distance a protein diffuses over its lifetime--and find that the covariance between local concentrations of proteins often increases if there are substantial bursts of synthesis during translation. Using high-throughput data, we estimate that the Kuramoto length of cytoplasmic proteins in budding yeast to be an order of magnitude larger than the cell diameter, implying that many such proteins should have an approximately uniform concentration. For constitutively expressed proteins that live substantially longer than their mRNA, we give an exact expression for the deviation of their local fluctuations from Poisson fluctuations. If the Kuramoto length of mRNA is sufficiently small, we predict that such local fluctuations become approximately Poisson in bacteria in much of the cell, unless translational bursting is exceptionally strong. Our results therefore demonstrate that diffusion can act to both increase and decrease the complexity of fluctuations in biochemical networks.
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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.