@article{coomer2022,

title = {Noise distorts the epigenetic landscape and shapes cell-fate decisions},

author = {Megan A. Coomer and Lucy Ham and Michael P.H. Stumpf},

doi = {10.1016/j.cels.2021.09.002},

issn = {2405-4712},

year  = {2022},

date = {2022-01-01},

urldate = {2022-01-01},

journal = {Cell Systems},

volume = {13},

number = {1},

pages = {83-102.e6},

abstract = {The Waddington epigenetic landscape has become an iconic representation of the cellular differentiation process. Recent single-cell transcriptomic data provide new opportunities for quantifying this originally conceptual tool, offering insight into the gene regulatory networks underlying cellular development. While many methods for constructing the landscape have been proposed, by far the most commonly employed approach is based on computing the landscape as the negative logarithm of the steady-state probability distribution. Here, we use simple models to highlight the complexities and limitations that arise when reconstructing the potential landscape in the presence of stochastic fluctuations. We consider how the landscape changes in accordance with different stochastic systems and show that it is the subtle interplay between the deterministic and stochastic components of the system that ultimately shapes the landscape. We further discuss how the presence of noise has important implications for the identifiability of the regulatory dynamics from experimental data. A record of this paper’s transparent peer review process is included in the supplemental information.},

keywords = {Feature},

pubstate = {published},

tppubtype = {article}

}

@article{ham2020b,

title = {Exactly solvable models of stochastic gene expression},

author = {Lucy Ham and David Schnoerr and Rowan D. Brackston and Michael P.H. Stumpf},

doi = {10.1063/1.5143540},

year  = {2020},

date = {2020-01-01},

urldate = {2020-01-01},

journal = {The Journal of Chemical Physics},

volume = {152},

pages = {144106},

abstract = {Stochastic models are key to understanding the intricate dynamics of gene expression. However, the simplest models that only account for active and inactive states of a gene fail to capture common observations in both prokaryotic and eukaryotic organisms. Here, we consider multistate models of gene expression that generalize the canonical Telegraph process and are capable of capturing the joint effects of transcription factors, heterochromatin state, and DNA accessibility (or, in prokaryotes, sigma-factor activity) on transcript abundance. We propose two approaches for solving classes of these generalized systems. The first approach offers a fresh perspective on a general class of multistate models and allows us to “decompose” more complicated systems into simpler processes, each of which can be solved analytically. This enables us to obtain a solution of any model from this class. Next, we develop an approximation method based on a power series expansion of the stationary distribution for an even broader class of multistate models of gene transcription. We further show that models from both classes cannot have a heavy-tailed distribution in the absence of extrinsic noise. The combination of analytical and computational solutions for these realistic gene expression models also holds the potential to design synthetic systems and control the behavior of naturally evolved gene expression systems in guiding cell-fate decisions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}