| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2012PNAS..109.8340Z |
| P356 | DOI | 10.1073/PNAS.1200161109 |
| P8608 | Fatcat ID | release_egkzjrx3p5fmdncg2tqpgfmu2e |
| P932 | PMC publication ID | 3361437 |
| P698 | PubMed publication ID | 22566653 |
| P5875 | ResearchGate publication ID | 224919042 |
| P50 | author | Serge Pelet | Q54537515 |
| Matthias Peter | Q58208774 | ||
| P2093 | author name string | Heinz Koeppl | |
| Christoph Zechner | |||
| John Lygeros | |||
| Jakob Ruess | |||
| Peter Krenn | |||
| P2860 | cites work | Global analysis of protein expression in yeast | Q27860658 |
| Genomic expression programs in the response of yeast cells to environmental changes | Q27860823 | ||
| Stochastic gene expression in a single cell | Q28131784 | ||
| Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise | Q29615953 | ||
| Osmotic stress signaling and osmoadaptation in yeasts | Q29617597 | ||
| Separating intrinsic from extrinsic fluctuations in dynamic biological systems | Q30502766 | ||
| Data-based identifiability analysis of non-linear dynamical models | Q33292131 | ||
| A quantitative study of the Hog1 MAPK response to fluctuating osmotic stress in Saccharomyces cerevisiae | Q33538019 | ||
| Solving the chemical master equation using sliding windows | Q33551049 | ||
| Identification of models of heterogeneous cell populations from population snapshot data | Q33884116 | ||
| Genomewide identification of Sko1 target promoters reveals a regulatory network that operates in response to osmotic stress in Saccharomyces cerevisiae | Q33995626 | ||
| Regulated cell-to-cell variation in a cell-fate decision system | Q34451943 | ||
| Predicting stochastic gene expression dynamics in single cells | Q34624471 | ||
| Colored extrinsic fluctuations and stochastic gene expression | Q36713989 | ||
| Listening to the noise: random fluctuations reveal gene network parameters. | Q37425287 | ||
| Structure and function of a transcriptional network activated by the MAPK Hog1. | Q41884350 | ||
| A systems-level analysis of perfect adaptation in yeast osmoregulation | Q42133339 | ||
| Dynamic signaling in the Hog1 MAPK pathway relies on high basal signal transduction. | Q42451365 | ||
| Transient transcriptional responses to stress are generated by opposing effects of mRNA production and degradation | Q43243061 | ||
| The finite state projection algorithm for the solution of the chemical master equation | Q46857588 | ||
| A bottom-up approach to gene regulation. | Q46947473 | ||
| Moment estimation for chemically reacting systems by extended Kalman filtering | Q48032238 | ||
| Bayesian inference for stochastic kinetic models using a diffusion approximation. | Q51965191 | ||
| Destabilized green fluorescent protein for monitoring dynamic changes in yeast gene expression with flow cytometry. | Q53903842 | ||
| Transient activation of the HOG MAPK pathway regulates bimodal gene expression. | Q54591702 | ||
| A general method for numerically simulating the stochastic time evolution of coupled chemical reactions | Q55934780 | ||
| Origins of regulated cell-to-cell variability | Q83171482 | ||
| P433 | issue | 21 | |
| P407 | language of work or name | English | Q1860 |
| P1104 | number of pages | 6 | |
| P304 | page(s) | 8340-8345 | |
| P577 | publication date | 2012-05-07 | |
| P1433 | published in | Proceedings of the National Academy of Sciences of the United States of America | Q1146531 |
| P1476 | title | Moment-based inference predicts bimodality in transient gene expression | |
| P478 | volume | 109 |
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