scholarly journals Transients generate memory and break hyperbolicity in stochastic enzymatic networks

2021 ◽  
Vol 154 (3) ◽  
pp. 035101
Author(s):  
Ashutosh Kumar ◽  
R. Adhikari ◽  
Arti Dua
Keyword(s):  
Enzymatic Networks

scholarly journals A Characterization of Scale Invariant Responses in Enzymatic Networks

2012 ◽  
Vol 8 (11) ◽  
pp. e1002748 ◽  
Author(s):  
Maja Skataric ◽  
Eduardo D. Sontag
Keyword(s):  
Scale Invariant ◽  
Enzymatic Networks

scholarly journals Optimal information transfer in enzymatic networks: A field theoretic formulation

2017 ◽  
Author(s):  
Himadri S. Samanta ◽  
Michael Hinczewski ◽  
D. Thirumalai
Keyword(s):  
Linear Theory ◽  
Chemical Reactions ◽  
Information Transfer ◽  
Signal Transmission ◽  
Control Variable ◽  
Signal Propagation ◽  
Umbral Calculus ◽  
Theoretic Approach ◽  
Optimal Information ◽  
Enzymatic Networks

AbstractSignaling in enzymatic networks is typically triggered by environmental fluctuations, resulting in a series of stochastic chemical reactions, leading to corruption of the signal by noise. For example, information flow is initiated by binding of extracellular ligands to receptors, which is transmitted through a cascade involving kinase-phosphatase stochastic chemical reactions. For a class of such networks, we develop a general field-theoretic approach in order to calculate the error in signal transmission as a function of an appropriate control variable. Application of the theory to a simple push-pull network, a module in the kinase-phosphatase cascade, recovers the exact results for error in signal transmission previously obtained using umbral calculus (Phys. Rev. X.,4, 041017 (2014)). We illustrate the generality of the theory by studying the minimal errors in noise reduction in a reaction cascade with two connected push-pull modules. Such a cascade behaves as an effective three-species network with a pseudo intermediate. In this case, optimal information transfer, resulting in the smallest square of the error between the input and output, occurs with a time delay, which is given by the inverse of the decay rate of the pseudo intermediate. Surprisingly, in these examples the minimum error computed using simulations that take non-linearities and discrete nature of molecules into account coincides with the predictions of a linear theory. In contrast, there are substantial deviations between simulations and predictions of the linear theory in error in signal propagation in an enzymatic push-pull network for a certain range of parameters. Inclusion of second order perturbative corrections shows that differences between simulations and theoretical predictions are minimized. Our study establishes that a field theoretic formulation of stochastic biological signaling offers a systematic way to understand error propagation in networks of arbitrary complexity.

scholarly journals A chemically fueled non-enzymatic bistable network

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Indrajit Maity ◽  
Nathaniel Wagner ◽  
Rakesh Mukherjee ◽  
Dharm Dev ◽  
Enrique Peacock-Lopez ◽  
...  
Keyword(s):  
Information Transfer ◽  
Molecular Networks ◽  
Complex Dynamic ◽  
Chemistry Research ◽  
Systems Chemistry ◽  
Bistable Systems ◽  
Bistable Region ◽  
Quantitative Analyses ◽  
Far From Equilibrium ◽  
Enzymatic Networks

Abstract One of the grand challenges in contemporary systems chemistry research is to mimic life-like functions using simple synthetic molecular networks. This is particularly true for systems that are out of chemical equilibrium and show complex dynamic behaviour, such as multi-stability, oscillations and chaos. We report here on thiodepsipeptide-based non-enzymatic networks propelled by reversible replication processes out of equilibrium, displaying bistability. Accordingly, we present quantitative analyses of the bistable behaviour, featuring a phase transition from the simple equilibration processes taking place in reversible dynamic chemistry into the bistable region. This behaviour is observed only when the system is continuously fueled by a reducing agent that keeps it far from equilibrium, and only when operating within a specifically defined parameter space. We propose that the development of biomimetic bistable systems will pave the way towards the study of more elaborate functions, such as information transfer and signalling.

scholarly journals Correlations between Homeostasis and Genetic Robustness in Enzymatic Networks

2013 ◽  
Vol 104 (2) ◽  
pp. 494a-495a
Author(s):  
Zeina Shreif ◽  
Vipul Periwal
Keyword(s):  
Genetic Robustness ◽  
Enzymatic Networks

scholarly journals Criticality and Adaptivity in Enzymatic Networks

2016 ◽  
Vol 111 (5) ◽  
pp. 1078-1087 ◽  
Author(s):  
Paul J. Steiner ◽  
Ruth J. Williams ◽  
Jeff Hasty ◽  
Lev S. Tsimring
Keyword(s):  
Enzymatic Networks

scholarly journals Michaelis–Menten relations for complex enzymatic networks

2011 ◽  
Vol 134 (15) ◽  
pp. 155101 ◽  
Author(s):  
Anatoly B. Kolomeisky
Keyword(s):  
Enzymatic Networks

scholarly journals Regulation of reaction fluxes via enzyme sequestration and co-clustering

2019 ◽  
Vol 16 (156) ◽  
pp. 20190444 ◽  
Author(s):  
Florian Hinzpeter ◽  
Filipe Tostevin ◽  
Ulrich Gerland
Keyword(s):  
Theoretical Model ◽  
Large Fraction ◽  
Intracellular Localization ◽  
Reaction Diffusion ◽  
Regulatory Effect ◽  
Biomolecular Systems ◽  
Spatial Regulation ◽  
Reaction Flux ◽  
Regulation Strategies ◽  
Enzymatic Networks

Experimental observations suggest that cells change the intracellular localization of key enzymes to regulate the reaction fluxes in enzymatic networks. In particular, cells appear to use sequestration and co-clustering of enzymes as spatial regulation strategies. These strategies should be equally useful to achieve rapid flux regulation in synthetic biomolecular systems. Here, we leverage a theoretical model to analyse the capacity of enzyme sequestration and co-clustering to control the reaction flux in a branch of a reaction–diffusion network. We find that in both cases, the response of the system is determined by two dimensionless parameters, the ratio of total activities of the competing enzymes and the ratio of diffusion to reaction timescales. Using these dependencies, we determine the parameter range for which sequestration and co-clustering can yield a biologically significant regulatory effect. Based on the known kinetic parameters of enzymes, we conclude that sequestration and co-clustering represent a viable regulation strategy for a large fraction of metabolic enzymes, and suggest design principles for reaction flux regulation in natural or synthetic systems.

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