Promoter-mediated Transcriptional Dynamics

Zhang, Jiajun; Zhou, Tianshou

doi:10.1016/j.bpj.2013.12.011

Cited by 97 publications

(97 citation statements)

References 74 publications

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“…In general, reaction rate k + or k − is regulated by external signals that are stochastically generated due to biochemical reactions, e.g., We have seen that the noise in gene expression levels can affect both first passage time and timing variability and there are control strategies for buffering the noise in event timing. However, sources of gene expression noise may be complex, e.g., promoter noise generated by switching between multi-states of the promoter (55), and the noise resulting from chromatin remodeling, DNA methylation, and nucleosome positioning (56). These stochastic sources can influence not only gene expression levels but also threshold crossing.…”

Section: Discussionmentioning

confidence: 99%

Control strategies for the timing of intracellular events

Cao

Qiu

Zhang

2019

Preprint

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While the timing of intracellular events is essential for many cellular processes, gene expression inside a cell can exhibit substantial cell-to-cell variability, raising the question of how cells ensure precision in the event timing despite such stochasticity. We address this question by analyzing a biologically reasonable model of gene expression in the context of first passage time (FPT), focusing on two experimentally measurable statistics: mean FPT (MFPT) and timing variability (TV). We show that: (1) transcriptional burst size (BS) and burst frequency (BF) can minimize the TV; (2) translational BS monotonically reduces the MFPT to a nonzero low bound and can minimize the TV; (3) the timescale of promoter kinetics can minimize both the MFPT and the TV, depending on the ratio of the off-switching rate over the on-switching rate; and (4) positive feedback regulation of any form can all minimize the TV, whereas negative feedback regulation of transcriptional BF or BS always enhances the TV. These control strategies can have broad implications for diverse cellular processes relying on precise temporal triggering of events.

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Section: Discussionmentioning

confidence: 99%

Control strategies for the timing of intracellular events

Cao

Qiu

Zhang

2019

Preprint

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“…where 1 1 F denotes the confluent hypergeometric function [50]. This distribution was ever derived in previous works [10,51,52].…”

Section: Analytical Distributionsmentioning

confidence: 99%

Queuing models of gene expression: Analytical distributions and beyond

Shi

Jiang

2020

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Activation of a gene is a multistep biochemical process, involving recruitments of transcription factors and histone kinases as well as modification of histones. Many of these intermediate reaction steps would have been unspecified by experiments. Therefore, classical two-state models of gene expression established based on the memoryless (or Markovian) assumption would not well describe the reality in gene expression. In fact, recent experimental data have indicated that the inactive phases of gene promoters are differently distributed, showing strong memory. Here, we use a non-exponential waiting-time distribution to model the complex activation process of a gene, and analyze a queuing model of stochastic transcription. We successfully derive the analytical expression for the mRNA distribution, which provides insight into the effect of molecular memory created by complex activating events on the mRNA expression. We find that the reduction in the waiting-time noise may result in the increase in the mRNA noise, contrary to the previous conclusion. Based on the derived distribution, we also develop a method to infer the waiting-time distribution from a known mRNA distribution. Data analysis on a realistic example verifies the validity of this method. SIGNIFICANCE:Activation of a gene is a complex biochemical process and involve several intermediate reaction steps, many of which have been unspecified by experiments. Stochastic models of gene expression that were previously established based on the constant reaction rates would not well reflect the reality in gene expression.To this end, we study a queuing model of stochastic transcription which assume that the reaction waiting time follows a general distribution and derive the analytical expression for mRNA distribution. Our results provide insight into the role of molecular memory in fine-tuning the gene expression noise, and can be used to infer the underlying molecular mechanism.

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“…The 3-stage model of gene expression [12][13][14][15]57,58] can find its prototypes in prokaryotic and eukaryotic cells. This model, which can capture many essential features of transcriptional biology, has extensively been used and studied.…”

Section: On-off Model Of Gene Expression With Molecular Memorymentioning

confidence: 99%

Computation of stationary distributions in stochastic models of cellular processes with molecular memory

Zhang

2019

Preprint

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Modeling stochastic dynamics of intracellular processes has long rested on Markovian (i.e., memoryless) hypothesis. However, many of these processes are non-Markovian (i.e., memorial) due to, e.g., small reaction steps involved in synthesis or degradation of a macroscopic molecule.When interrogating aspects of a cellular network by experimental measurements (e.g., by singlemolecule and single-cell measurement technologies) of network components, a key need is to develop efficient approaches to simulate and compute joint distributions of these components. To cope with this computational challenge, we develop two efficient algorithms: stationary generalized Gillespie algorithm and stationary generalized finite state projection, both being established based on a stationary generalized chemical master equation. We show how these algorithms can be combined in a streamlined procedure for evaluation of non-Markovian effects in a general cellular network. Stationary distributions are evaluated in two models of constitutive and bursty gene expressions as well as a model of genetic toggle switch, each considering molecular memory. Our approach significantly expands the capability of stochastic simulation to investigate gene regulatory network dynamics, which has the potential to advance both understanding of molecular systems biology and design of synthetic circuits. Author summaryCellular systems are driven by interactions between subsystems via time-stamped discrete events, involving numerous components and reaction steps and spanning several time scales. Such biochemical reactions are subject to inherent noise due to the small numbers of molecules. Also, they could involve several small steps, creating a memory between individual events. Delineating 2 these molecular stochasticity and memory of biomolecular networks are continuing challenges for molecular systems biology. We present a novel approach to compute the probability distribution in stochastic models of cellular processes with molecular memory based on stationary generalized chemical master equation. We map a stochastic system with memory onto a Markovian model with effective reaction propensity functions. This formulation enables us to efficiently develop algorithms under the Markovian framework, and thus systematically analyze how molecular memories regulate stochastic behaviors of biomolecular networks. Here we propose two representative algorithms: stationary generalized Gillespie algorithm and stationary generalized finite state projection algorithm. The former generate realizations with Monte Carlo simulation, but the later compute approximations of the probability distribution by solving a truncated version of stochastic process. Our approach is demonstrated by applying it to three different examples from systems biology: generalized birth-death process, a stochastic toggle switch model, and a 3-stage gene expression model.  1 is a row vector with the same dimension as that of J A , J P and J  P are the stationary probability distributions correspondin...

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Promoter-mediated Transcriptional Dynamics

Cited by 97 publications

References 74 publications

Control strategies for the timing of intracellular events

Control strategies for the timing of intracellular events

Queuing models of gene expression: Analytical distributions and beyond

Computation of stationary distributions in stochastic models of cellular processes with molecular memory

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