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Mixed positive and negative feedback loops are often found in biological systems which support oscillations. In this work we consider a prototype of such systems, which has been recently found at the core of many genetic circuits showing…

统计力学 · 物理学 2015-06-03 Ashok Garai , Bartlomiej Waclaw , Hannes Nagel , Hildegard Meyer-Ortmanns

We show that noise-induced oscillations in a gene circuit model display stochastic coherence, that is, a maximum in the regularity of the oscillations as a function of noise amplitude. The effect is manifest as a system-size effect in a…

分子网络 · 定量生物学 2009-08-05 Robert C. Hilborn , Jessie D. Erwin

We explore the connection between a stochastic simulation model and an ordinary differential equations (ODEs) model of the dynamics of an excitable gene circuit that exhibits noise-induced oscillations. Near a bifurcation point in the ODE…

分子网络 · 定量生物学 2012-04-26 Robert C. Hilborn , Benjamin Brookshire , Jenna Mattingly , Anusha Purushotham , Anuraag Sharma

A key goal of systems biology is the predictive mathematical description of gene regulatory circuits. Different approaches are used such as deterministic and stochastic models, models that describe cell growth and division explicitly or…

分子网络 · 定量生物学 2012-10-12 Rahul Marathe , Veronika Bierbaum , David Gomez , Stefan Klumpp

Within systems biology there is an increasing interest in the stochastic behavior of genetic and biochemical reaction networks. An appropriate stochastic description is provided by the chemical master equation, which represents a continuous…

生物物理 · 物理学 2011-06-23 E. Giampieri , D. Remondini , L. de Oliveira , G. Castellani , P. Lió

Many cellular behaviors are regulated by gene regulation networks, kinetics of which is one of the main subjects in the study of systems biology. Because of the low number molecules in these reacting systems, stochastic effects are…

定量方法 · 定量生物学 2011-04-26 Jinzhi Lei

Stochasticity in gene expression can give rise to fluctuations in protein levels and lead to phenotypic variation across a population of genetically identical cells. Recent experiments indicate that bursting and feedback mechanisms play…

分子网络 · 定量生物学 2015-06-22 Niraj Kumar , Thierry Platini , Rahul V. Kulkarni

Auto-regulatory feedback loops are one of the most common network motifs. A wide variety of stochastic models have been constructed to understand how the fluctuations in protein numbers in these loops are influenced by the kinetic…

亚细胞过程 · 定量生物学 2020-04-22 James Holehouse , Zhixing Cao , Ramon Grima

Biochemical reaction networks are subjected to large fluctuations attributable to small molecule numbers, yet underlie reliable biological functions. Most theoretical approaches describe them as purely deterministic or stochastic dynamical…

分子网络 · 定量生物学 2013-06-11 Jingkui Wang , Marc Lefranc , Quentin Thommen

We show how one may analytically compute the stationary density of the distribution of molecular constituents in populations of cells in the presence of noise arising from either bursting transcription or translation, or noise in…

分子网络 · 定量生物学 2015-10-15 Michael C. Mackey , Marta Tyran-Kamińska , Romain Yvinec

Feedback in cellular processes is typically inferred through cellular responses to experimental perturbations. Modular response analysis provides a theoretical framework for translating specific perturbations into feedback sensitivities…

分子网络 · 定量生物学 2025-05-09 Seshu Iyengar , Andreas Hilfinger

It is well known that the kinetics of an intracellular biochemical network is stochastic. This is due to intrinsic noise arising from the random timing of biochemical reactions in the network as well as due to extrinsic noise stemming from…

亚细胞过程 · 定量生物学 2019-06-05 Emma M. Keizer , Bjorn Bastian , Robert W. Smith , Ramon Grima , Christian Fleck

According to the chemical reaction network theory, the topology of a certain class of chemical reaction networks, regardless of the kinetic details, sets a limit on the dynamical properties that a particular network can potentially admit;…

分子网络 · 定量生物学 2014-08-21 Jaewook Joo , Sanjeev Chauhan

We present the numerical estimation of noise parameter induced in the dynamics of the variables by random particle interactions involved in the stochastic chemical oscillator and use it as order parameter to detect the transition from…

计算物理 · 物理学 2011-09-02 R. K. Brojen Singh

Gene regulatory circuits show significant stochastic fluctuations in their circuit signals due to the low copy number of transcription factors. When a gene circuit component is connected to an existing circuit, the dynamic properties of the…

定量方法 · 定量生物学 2009-10-30 Kyung Hyuk Kim , Herbert M. Sauro

Gene expression is inherently noisy as many steps in the read-out of the genetic information are stochastic. To disentangle the effect of different sources of stochasticity in such systems, we consider various models that describe some…

分子网络 · 定量生物学 2015-06-05 Rahul Marathe , David Gomez , Stefan Klumpp

Deterministic and stochastic Boolean network models are build for the dynamics of negative feedback loops of the p53 pathways. It is shown that the main function of the negative feedback in the p53 pathways is to keep p53 at a low steady…

分子网络 · 定量生物学 2009-04-16 Hao Ge , Min Qian

The effects of intrinsic noise on stochastic delay systems is studied within an expansion in the inverse system size. We show that the stochastic nature of the underlying dynamics may induce oscillatory behaviour in parameter ranges where…

分子网络 · 定量生物学 2015-05-13 Tobias Galla

While noise is generally associated with uncertainties and often has a negative connotation in engineering, living organisms have evolved to adapt to (and even exploit) such uncertainty to ensure the survival of a species or implement…

分子网络 · 定量生物学 2022-09-29 Corentin Briat , Mustafa Khammash

The intrinsic stochasticity of gene expression can lead to large variability of protein levels across a population of cells. Variability (or noise) in protein distributions can be modulated by cellular mechanisms of gene regulation; in…

分子网络 · 定量生物学 2011-03-02 Tao Jia , Rahul V. Kulkarni
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