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Simplified stochastic models are widely used in the study of frequency-resolved noise propagation in biochemical reaction networks, a common measure being the coherence between random fluctuations in molecule number trajectories. Such…

Molecular Networks · Quantitative Biology 2025-11-03 Juan David Marmolejo Lozano , Nikola Popovic , Ramon Grima

Stochastic kinetic models of genetic expression are able to describe protein fluctuations. A comparative study of the canonical and a feedback model is given here by using stochastic simulation methods. The feedback model is skeleton model…

Molecular Networks · Quantitative Biology 2018-09-06 Raoul R. Wadhwa , Laszlo Zalanyi , Judit Szente , Laszlo Negyessy , Peter Erdi

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…

Molecular Networks · Quantitative Biology 2015-06-05 Rahul Marathe , David Gomez , Stefan Klumpp

Stochastic resonance is a counter-intuitive concept[1,2], ; the addition of noise to a noisy system induces coherent amplification of its response. First suggested as a mechanism for the cyclic recurrence of ice ages, stochastic resonance…

Mesoscale and Nanoscale Physics · Physics 2009-11-11 R. L. Badzey , P. Mohanty

We describe a mechanism for pronounced biochemical oscillations, relevant to microscopic systems, such as the intracellular environment. This mechanism operates for reaction schemes which, when modeled using deterministic rate equations,…

Cell Behavior · Quantitative Biology 2009-11-13 A. J. McKane , J. D. Nagy , T. J. Newman , M. O. Stefanini

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…

Molecular Networks · Quantitative Biology 2022-09-29 Corentin Briat , Mustafa Khammash

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…

Molecular Networks · Quantitative Biology 2015-06-22 Niraj Kumar , Thierry Platini , Rahul V. Kulkarni

Regulation of intrinsic noise in gene expression is essential for many cellular functions. Correspondingly, there is considerable interest in understanding how different molecular mechanisms of gene expression impact variations in protein…

Molecular Networks · Quantitative Biology 2011-03-02 Tao Jia , Rahul V. Kulkarni

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…

Molecular Networks · Quantitative Biology 2011-03-02 Tao Jia , Rahul V. Kulkarni

It has been generally recognized that stochasticity can play an important role in the information processing accomplished by reaction networks in biological cells. Most treatments of that stochasticity employ Gaussian noise even though it…

Molecular Networks · Quantitative Biology 2015-05-30 Neda Bostani , David A. Kessler , Nadav M. Shnerb , Wouter-Jan Rappel , Herbert Levine

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…

Molecular Networks · Quantitative Biology 2012-10-12 Rahul Marathe , Veronika Bierbaum , David Gomez , Stefan Klumpp

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…

Molecular Networks · Quantitative Biology 2013-06-11 Jingkui Wang , Marc Lefranc , Quentin Thommen

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…

Subcellular Processes · Quantitative Biology 2020-04-22 James Holehouse , Zhixing Cao , Ramon Grima

Expression of many genes varies as a cell transitions through different cell-cycle stages. How coupling between stochastic expression and cell cycle impacts cell-to-cell variability (noise) in the level of protein is not well understood. We…

Molecular Networks · Quantitative Biology 2016-05-10 Mohammad Soltani , Abhyudai Singh

We study a stochastic model proposed recently in the genetic literature to explain the heterogeneity of cell populations or of gene products. Cells are located in two colonies, whose sizes fluctuate as birth and migration processes in…

Populations and Evolution · Quantitative Biology 2007-05-23 Martin Gander , Christian Mazza , Hansklaus Rummler

We study the noise characteristics of stochastic oscillations in protein number dynamics of simple genetic oscillatory systems. Using the three-component negative feedback transcription regulatory system called the repressilator as a…

Molecular Networks · Quantitative Biology 2015-03-13 Byungjoon Min , K. -I. Goh , I. -M. Kim

A common model of stochastic auto-regulatory gene expression describes promoter switching via cooperative protein binding, effective protein production in the active state and dilution of proteins. Here we consider an extension of this…

Subcellular Processes · Quantitative Biology 2020-04-07 James Holehouse , Abhishek Gupta , Ramon Grima

Gene products (RNAs, proteins) often occur at low molecular counts inside individual cells, and hence are subject to considerable random fluctuations (noise) in copy number over time. Not surprisingly, cells encode diverse regulatory…

Molecular Networks · Quantitative Biology 2015-10-01 Thierry Platini , Mohammad Soltani , Abhyudai Singh

A formalism is developed which describes the extent to which stochastic oscillations in biochemical models are synchronised. It is based on the calculation of the complex coherence function within the linear noise approximation. The method…

Statistical Mechanics · Physics 2013-07-29 Joseph D. Challenger , Alan J. McKane

We consider an ensemble of coupled nonlinear noisy oscillators demonstrating in the thermodynamic limit an Ising-type transition. In the ordered phase and for finite ensembles stochastic flips of the mean field are observed with the rate…

Statistical Mechanics · Physics 2009-11-07 A. Pikovsky , A. Zaikin , M. A. de la Casa