Insights into noise modulation in oligomerization systems of increasing complexity

Pucci, Fabrizio; Rooman, Marianne

doi:10.1103/physreve.98.012137

Cited by 3 publications

(11 citation statements)

References 25 publications

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“…with the positive coefficient α = α x +α z . We thus recovered the general relation obtained in [32] for a system of rank 2 with deficiency δ = 1 and stochasticity level s = 1.…”

Section: Homo-oligomerization-type Systemssupporting

confidence: 58%

“…Two features are shown to be crucial for the noise modulation in the systems analyzed: the deficiency δ and the type of oligomers. On the basis of the results obtained in this paper and in [31,32], we propose the following conclusions and tentative generalizations:…”

Section: Discussionmentioning

confidence: 55%

“…• Systems with δ ≥ 1 and multiple internal fluxes Using the results of [32], we tentattively generalize Eq. (25) to generic CRNs with N J internal fluxes J :…”

Section: Discussionmentioning

confidence: 99%

“…It has furthermore been shown that the increase of the network complexity tends to decrease the intrinsic noise as well as to reduce the effect of the extrinsic noise for some multistable model systems [29], whereas a dependence of the noise reduction or amplification on the system parameters has been found in [30].This paper focuses on a class of biological networks linking several molecular species, which are produced, degraded and undergo homo-or hetero-oligomerizationtype reactions. It builds on our earlier works, in which we used the stochastic differential equation formalism to study the intrinsic noise in a series of closed [31] and open systems [32]. Here we computed the noise reduction or amplification on every individual molecular species at the steady state, and related the fluctuation levels to some structural characteristics of the reaction network and to the reaction fluxes flowing between the species.…”

mentioning

confidence: 99%

“…This paper focuses on a class of biological networks linking several molecular species, which are produced, degraded and undergo homo-or hetero-oligomerizationtype reactions. It builds on our earlier works, in which we used the stochastic differential equation formalism to study the intrinsic noise in a series of closed [31] and open systems [32]. Here we computed the noise reduction or amplification on every individual molecular species at the steady state, and related the fluctuation levels to some structural characteristics of the reaction network and to the reaction fluxes flowing between the species.…”

mentioning

confidence: 99%

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Deciphering noise amplification and reduction in open chemical reaction networks

Pucci

Rooman

2018

Preprint

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The impact of random fluctuations on the dynamical behavior a complex biological systems is a longstanding issue, whose understanding would shed light on the evolutionary pressure that nature imposes on the intrinsic noise levels and would allow rationally designing synthetic networks with controlled noise. Using the Itō stochastic differential equation formalism, we performed both analytic and numerical analyses of several model systems containing different molecular species in contact with the environment and interacting with each other through mass-action kinetics. These systems represent for example biomolecular oligomerization processes, complex-breakage reactions, signaling cascades or metabolic networks. For chemical reaction networks with zero deficiency values, which admit a detailed-or complex-balanced steady state, all molecular species are uncorrelated. The number of molecules of each species follow a Poisson distribution and their Fano factors, which measure the intrinsic noise, are equal to one. Systems with deficiency one have an unbalanced non-equilibrium steady state and a non-zero S-flux, defined as the flux flowing between the complexes multiplied by an adequate stoichiometric coefficient. In this case, the noise on each species is reduced if the flux flows from the species of lowest to highest complexity, and is amplified is the flux goes in the opposite direction. These results are generalized to systems of deficiency two, which possess two independent non-vanishing S-fluxes, and we conjecture that a similar relation holds for higher deficiency systems.

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“…with the positive coefficient α = α x +α z . We thus recovered the general relation obtained in [32] for a system of rank 2 with deficiency δ = 1 and stochasticity level s = 1.…”

Section: Homo-oligomerization-type Systemssupporting

confidence: 58%

Section: Discussionmentioning

confidence: 55%

“…• Systems with δ ≥ 1 and multiple internal fluxes Using the results of [32], we tentattively generalize Eq. (25) to generic CRNs with N J internal fluxes J :…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Deciphering noise amplification and reduction in open chemical reaction networks

Pucci

Rooman

2018

Preprint

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Deciphering noise amplification and reduction in open chemical reaction networks

Pucci

Rooman

2018

J. R. Soc. Interface.

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The impact of fluctuations on the dynamical behaviour of complex biological systems is a longstanding issue, whose understanding would elucidate how evolutionary pressure tends to modulate intrinsic noise. Using the Itō stochastic differential equation formalism, we performed analytic and numerical analyses of model systems containing different molecular species in contact with the environment and interacting with each other through mass-action kinetics. For networks of zero deficiency, which admit a detailed-or complex-balanced steady state, all molecular species are uncorrelated and their Fano factors are Poissonian. Systems of higher deficiency have nonequilibrium steady states and non-zero reaction fluxes flowing between the complexes. When they model homo-oligomerization, the noise on each species is reduced when the flux flows from the oligomers of lowest to highest degree, and amplified otherwise. In the case of hetero-oligomerization systems, only the noise on the highest-degree species shows this behaviour.& 2018 The Author(s) Published by the Royal Society. All rights reserved.transcription [11][12][13][14]. This mechanism has indeed been shown to lower noise while reducing the metabolic cost of protein production, and speeds up the rise-times of transcription units [15]. However, not all systems with negative feedback loops decrease in the intrinsic noise levels [16][17][18]. Similarly, although it is generally accepted that positive feedback loops tend to increase noise levels [19], some appear to decrease them [20]. Hence, the problem is far from being totally elucidated.Despite the many valuable advances in the field, the mechanisms used to amplify or to suppress the fluctuation levels need to be further understood and clarified. Indeed, the huge complexity of biological systems, their dependence on a large number of variables and the system-to-system variability make the unravelling of these issues, whether using experimental or computational approaches, a highly non-trivial task.More specifically, while noise control is relatively well understood for small and simple networks, it is still far from clear how the fluctuations propagate through more general and complicated networks and what is the link between network topology and complexity with noise buffering or amplification. Different investigations addressed these issues from various perspectives, for example by characterizing the stochastic properties of the chemical reaction networks (CRNs) and studying the propagation of the fluctuations [21][22][23][24]. From a physics-oriented perspective, other studies have analysed the connection between the nonequilibrium thermodynamic properties of the network and the noise level [25 -27]. It has furthermore been shown that the increase in network complexity tends to decrease intrinsic noise as well as to reduce the effect of extrinsic noise for some multistable model systems [28], whereas the dependence of noise reduction or amplification on the system parameters has been found in [29].This paper focu...

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Stochastic fluctuations in protein interaction networks are nearly Poissonian

Albert¹

2021

Preprint

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Gene regulatory networks are comprised of biochemical reactions, which are inherently stochastic.Each reaction channel contributes to this stochasticity in different measure. In this paper we study the stochastic dynamics of protein interaction networks (PIN) that are made up of monomers and dimers. The network is defined by the dimers, which are formed by hybridizing two monomers.The size of a PIN was defined as the number of monomers that interacts with at least one other monomer (including itself). We generated 4200 random PIN of sizes between 2 and 8 (600 per size) and simulated via the Gillespie algorithm the stochastic evolution of copy numbers of all monomers and dimers until they reached a steady state. The simulations revealed that the Fano factors of both monomers and dimers in all networks and for all time points were close to one, either from below or above. Only 10% of Fano factors for monomers were above 1.3 and 10% of Fano factors for dimers were above 1.17, with 5.54 and 3.47 as the maximum value recorded for monomers and dimers, respectively. These findings suggests that PIN in real biological setting contribute to the overall stochastic noise that is close to Poisson. Our results also show a correlation between stochastic noise, network size and network connectivity: for monomers, the Fano factors tend towards 1 from above, while the Fano factors for dimers tend towards 1 from below. For monomers, this tendency is amplified with increased network connectivity.

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Insights into noise modulation in oligomerization systems of increasing complexity

Cited by 3 publications

References 25 publications

Deciphering noise amplification and reduction in open chemical reaction networks

Deciphering noise amplification and reduction in open chemical reaction networks

Deciphering noise amplification and reduction in open chemical reaction networks

Stochastic fluctuations in protein interaction networks are nearly Poissonian

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