A timeline of the studies investigating stochastic gene expression

(22 Dec 2020) biology

I spent a few days last week catching up with the literature on stochastic gene expression. Here is the timeline that I put together. It is a work in progress, but I found a lot of interesting new ideas when assembling this table!

Year Description Citation
1977 Gillespie publishes the exact SSA 1
1989 Tyson at VT builds on Alt-Tyson1986 in an attempt to build a model of cell size distribution for asymmetric division and stochastic process governing production of cell cycle activator 2
1997 McAdams and Arkin at Stanford propose the first model of “stochastic” gene expression, thinking about delays in activation of circuits if expression is noisy. Focus on prokaryotic expression, use SSA. Good untuitive development of Poisson statistics ideas. 3
2001 Thattai at MIT uses the SSA to characterize a few simple regulatory systems. Compare simulations to analytical results 4
2002 Swain and others at Rockefeller draw a distinction between intrinsic and extrinsic noise, and report the first formal analysis of expected noise including bursting. This paper also gives the reason for defining noise as variance/mean, showing that this definition allows us to simply add up intrinsic and extrinsic noise to get total noise. 5
2004 Raser and O’Shea introduce the two color experiments to distinguish intrinsic and extrinsic noise 6
2005 Paulsson reviews the state of art theory. Includes work by Elowitz, van Oudenaarden. 7
2005 Kussel and Leibler publish theory showing the advantages of stochastic phenotype switching. This puts stochastic gene expression as the basis of bet hedging, a non genetic basis of variation. 8
2006 First global survey of expression noise, led by Arren Bar-Even which systematically interrogates the origin of noise 9
2007 So what are the consequences of noise? Di Talia and others at Rockefeller ask if cell cycle time distributions are in agreement with the expected noise in molecular regulators. The style is different, the measurements are not even molecular! 10
2009 Taking a significant leap forward, Kar in Tyson’s group shows that a stochastic version of mechanistic cell cycle model produces the expected cell cycle timing distributions. 11
2013 Sanchez and Golding show a markedly different global transcriptional noise structure in yeast as compared to bacteria and mammalian cells. Consequences? 12
2015 Keren sources the origin of the growth rate coupling of noise to the population distributions in G1 and G2. The ploidy ratios are a simple explanation of growth rate dependent noise. 13
2015 Metzger in Patricia Wittkopp’s group at Michigan shows that noise has consequences at the sequence level, bringing evolution into the picture. 14
2018 Duveau studied the consequences for cellular fitness. An important contribution is the experiment design: increasing noise while holding mean expression constant. 15


  1. Daniel Gillespie, Exact Stochastic Simulation of Coupled Chemical Reactions, The Journal of Physical Chemistry, 81(25), 2340-2361 (1977). link. doi

  2. John Tyson, Effects of Asymmetric Division on a Stochastic Model of the Cell Division Cycle, Mathematical Biosciences, 96(2), 165-184 (1989). link. doi

  3. McAdams & Arkin, Stochastic Mechanisms in Gene Expression, Proceedings of the National Academy of Sciences, 94(3), 814-819 (1997). link. doi

  4. Thattai & van Oudenaarden, Intrinsic Noise in Gene Regulatory Networks, Proceedings of the National Academy of Sciences, 98(15), 8614-8619 (2001). link. doi

  5. Swain, Elowitz & Siggia, Intrinsic and Extrinsic Contributions To Stochasticity in Gene Expression, Proceedings of the National Academy of Sciences, 99(20), 12795-12800 (2002). link. doi

  6. Jonathan Raser & Erin O’Shea, Control of Stochasticity in Eukaryotic Gene Expression, Science, 304(5678), 1811-1814 (2004). link. doi

  7. Johan Paulsson, Models of Stochastic Gene Expression, Physics of Life Reviews, 2(2), 157-175 (2005). link. doi

  8. Edo Kussell & Stanislas Leibler, Phenotypic Diversity, Population Growth, and Information in Fluctuating Environments, Science, 309(5743), 2075-2078 (2005). link. doi

  9. Arren Bar-Even, Johan Paulsson, Narendra, Maheshri, Miri Carmi, Erin O’Shea, Yitzhak, Pilpel & Naama Barkai, Noise in Protein Expression Scales With Natural Protein Abundance, Nature Genetics, 38(6), 636-643 (2006). link. doi

  10. Stefano Di Talia, Jan Skotheim, James, Bean, Eric Siggia & Frederick Cross, The Effects of Molecular Noise and Size Control on Variability in the Budding Yeast Cell Cycle, Nature, 448(7156), 947-951 (2007). link. doi

  11. Kar, Baumann, Paul, & Tyson, Exploring the Roles of Noise in the Eukaryotic Cell Cycle, Proceedings of the National Academy of Sciences, 106(16), 6471-6476 (2009). link. doi

  12. Sanchez & Golding, Genetic Determinants and Cellular Constraints in Noisy Gene Expression, Science, 342(6163), 1188-1193 (2013). link. doi

  13. Leeat Keren, David van Dijk, Shira, Weingarten-Gabbay, Dan Davidi, Ghil Jona, , Adina Weinberger, Ron Milo & Eran Segal, Noise in Gene Expression Is Coupled To Growth Rate, Genome Research, 25(12), 1893-1902 (2015). link. doi

  14. Brian Metzger, David Yuan, Jonathan, Gruber, Fabien Duveau, Patricia & Wittkopp, Selection on Noise Constrains Variation in a Eukaryotic Promoter, Nature, 521(7552), 344-347 (2015). link. doi

  15. Fabien Duveau, Andrea Hodgins-Davis, Brian PH, Metzger, Bing Yang, Stephen Tryban, , Elizabeth A Walker, Tricia Lybrook, Patricia J & Wittkopp, Fitness Effects of Altering Gene Expression Noise in Saccharomyces Cerevisiae, eLife, 7(nil), nil (2018). link. doi