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 Optimal information transfer in enzymatic networks: A field theoretic formulation

2017 ◽  
Vol 96 (1) ◽  
Author(s):  
Himadri S. Samanta ◽  
Michael Hinczewski ◽  
D. Thirumalai
Keyword(s):  
Information Transfer ◽  
Optimal Information ◽  
Enzymatic Networks

scholarly journals Causality and Information Transfer Between the Solar Wind and the Magnetosphere–Ionosphere System

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 390
Author(s):  
Pouya Manshour ◽  
Georgios Balasis ◽  
Giuseppe Consolini ◽  
Constantinos Papadimitriou ◽  
Milan Paluš
Keyword(s):  
Solar Wind ◽  
Information Transfer ◽  
Linear Time ◽  
Vertical Component ◽  
Causal Link ◽  
Theoretic Approach ◽  
Geomagnetic Disturbances ◽  
Magnetospheric Substorms ◽  
Solar Wind Parameters ◽  
Information Theoretic Approach

An information-theoretic approach for detecting causality and information transfer is used to identify interactions of solar activity and interplanetary medium conditions with the Earth’s magnetosphere–ionosphere systems. A causal information transfer from the solar wind parameters to geomagnetic indices is detected. The vertical component of the interplanetary magnetic field (Bz) influences the auroral electrojet (AE) index with an information transfer delay of 10 min and the geomagnetic disturbances at mid-latitudes measured by the symmetric field in the H component (SYM-H) index with a delay of about 30 min. Using a properly conditioned causality measure, no causal link between AE and SYM-H, or between magnetospheric substorms and magnetic storms can be detected. The observed causal relations can be described as linear time-delayed information transfer.

scholarly journals Deep brain stimulation of the subthalamic nucleus reestablishes neuronal information transmission in the 6-OHDA rat model of parkinsonism

2014 ◽  
Vol 111 (10) ◽  
pp. 1949-1959 ◽  
Author(s):  
Alan D. Dorval ◽  
Warren M. Grill
Keyword(s):  
Deep Brain Stimulation ◽  
Basal Ganglia ◽  
Information Transmission ◽  
Brain Stimulation ◽  
Rat Model ◽  
Information Transfer ◽  
Firing Pattern ◽  
Signal Propagation ◽  
Neuronal Firing ◽  
Deep Brain

Pathophysiological activity of basal ganglia neurons accompanies the motor symptoms of Parkinson's disease. High-frequency (>90 Hz) deep brain stimulation (DBS) reduces parkinsonian symptoms, but the mechanisms remain unclear. We hypothesize that parkinsonism-associated electrophysiological changes constitute an increase in neuronal firing pattern disorder and a concomitant decrease in information transmission through the ventral basal ganglia, and that effective DBS alleviates symptoms by decreasing neuronal disorder while simultaneously increasing information transfer through the same regions. We tested these hypotheses in the freely behaving, 6-hydroxydopamine-lesioned rat model of hemiparkinsonism. Following the onset of parkinsonism, mean neuronal firing rates were unchanged, despite a significant increase in firing pattern disorder (i.e., neuronal entropy), in both the globus pallidus and substantia nigra pars reticulata. This increase in neuronal entropy was reversed by symptom-alleviating DBS. Whereas increases in signal entropy are most commonly indicative of similar increases in information transmission, directed information through both regions was substantially reduced (>70%) following the onset of parkinsonism. Again, this decrease in information transmission was partially reversed by DBS. Together, these results suggest that the parkinsonian basal ganglia are rife with entropic activity and incapable of functional information transmission. Furthermore, they indicate that symptom-alleviating DBS works by lowering the entropic noise floor, enabling more information-rich signal propagation. In this view, the symptoms of parkinsonism may be more a default mode, normally overridden by healthy basal ganglia information. When that information is abolished by parkinsonian pathophysiology, hypokinetic symptoms emerge.

scholarly journals Hierarchical coupling between ATP hydrolysis and Hsp90’s client binding site

2020 ◽  
Author(s):  
Steffen Wolf ◽  
Benedikt Sohmen ◽  
Björn Hellenkamp ◽  
Johann Thurn ◽  
Gerhard Stock ◽  
...  
Keyword(s):  
Binding Site ◽  
Single Molecule ◽  
Information Transfer ◽  
Atp Hydrolysis ◽  
Structural Information ◽  
Substrate Binding ◽  
Signal Propagation ◽  
Nonequilibrium Molecular Dynamics ◽  
Time Resolved ◽  
Substrate Binding Site

I.ABSTRACTSeveral indicators for a signal propagation from a binding site to a distant functional site have been found in the Hsp90 dimer. Here we determined a time-resolved pathway from ATP hydrolysis to changes in a distant folding substrate binding site. This was possible by combining single-molecule fluorescence-based methods with extensive atomistic nonequilibrium molecular dynamics simulations. We find that hydrolysis of one ATP effects a structural asymmetry in the full Hsp90 dimer that leads to the collapse of a central folding substrate binding site. Arg380 is the major mediator in transferring structural information from the nucleotide to the substrate binding site. This allosteric process occurs via hierarchical dynamics that involve timescales from picoto milliseconds and length scales from Ångstroms to several nanometers. We presume that similar hierarchical mechanisms are fundamental for information transfer through many other proteins.

Signal Propagation Along Unidimensional Neuronal Networks

2005 ◽  
Vol 94 (5) ◽  
pp. 3406-3416 ◽  
Author(s):  
Ofer Feinerman ◽  
Menahem Segal ◽  
Elisha Moses
Keyword(s):  
Large Scale ◽  
Signal Transmission ◽  
Signal Propagation ◽  
High Amplitude ◽  
Adhesive Material ◽  
One Dimensional ◽  
Calcium Indicator ◽  
Scale Population ◽  
Population Burst ◽  
Linear Signal

Dissociated neurons were cultured on lines of various lengths covered with adhesive material to obtain an experimental model system of linear signal transmission. The neuronal connectivity in the linear culture is characterized, and it is demonstrated that local spiking activity is relayed by synaptic transmission along the line of neurons to develop into a large-scale population burst. Formally, this can be treated as a one-dimensional information channel. Directional propagation of both spontaneous and stimulated bursts along the line, imaged with the calcium indicator Fluo-4, revealed the existence of two different propagation velocities. Initially, a small number of neighboring neurons fire, leading to a slow, small and presumably asynchronous wave of activity. The signal then spontaneously develops to encompass much larger and further populations, and is characterized by fast propagation of high-amplitude activity, which is presumed to be synchronous. These results are well described by an existing theoretical framework for propagation based on an integrate-and-fire model.

Interplay between signaling network design and swarm dynamics

2016 ◽  
Vol 4 (2) ◽  
pp. 244-265 ◽  
Author(s):  
ANDRÉ SEKUNDA ◽  
MOHAMMAD KOMAREJI ◽  
ROLAND BOUFFANAIS
Keyword(s):  
Network Design ◽  
Information Transfer ◽  
Network Models ◽  
Signaling Network ◽  
Spectral Graph Theory ◽  
Theoretic Approach ◽  
Collective Behaviors ◽  
Global Consensus ◽  
Swarm Dynamics ◽  
The Impact

AbstractDistributed information transfer is of paramount importance to the effectiveness of dynamic collective behaviors, especially when a swarm is confronted with complex environmental circumstances. Recently, the signaling network of interaction underlying such effective information transfers has been revealed in the particular case of bird flocks governed by a topological interaction. Such biological systems are known to be evolutionary optimized, but are also constrained by the very nature of the signaling mechanisms—owing to intrinsic limitations in sensory modalities—enabling communication among individuals. Here, we propose that artificial swarm design can be tackled from the angle of signaling network design. To this aim, we use different network models to investigate the impact of some network structural properties on the effectiveness of a specific emergent swarming behavior, namely global consensus. Two new network models are introduced, which together with the well-known Watts–Strogatz model form the basis for an analysis of the relationship between clustering, shortest path and speed to consensus. A network-theoretic approach combined with spectral graph theory tools are used to propose some signaling network design principles. Eventually, one key design principle—a concomitant reduction in clustering and connecting path—is successfully tested on simulations of swarms of self-propelled particles.

Causality and information transfer in systems with extreme events

2020 ◽  
Author(s):  
Milan Palus
Keyword(s):  
Information Theory ◽  
Extreme Events ◽  
Information Transfer ◽  
Complex Dynamics ◽  
Probability Distributions ◽  
Mathematical Formulation ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Theoretic Approach ◽  
Heavy Tailed

<p>The mathematical formulation of causality in measurable terms of predictability was given by the father of cybernetics N. Wiener [1] and formulated for time series by C.W.J. Granger [2]. The Granger causality is based on the evaluation of predictability in bivariate autoregressive models. This concept has been generalized for nonlinear systems using methods rooted in information theory [3,4]. The information-theoretic approach, defining causality as information transfer, has been successful in many applications and generalized to multivariate data and causal networks [e.g., 5]. This approach, rooted in the information theory of Shannon, usually ignores two important properties of complex systems, such as the Earth climate: the systems evolve on multiple time scales and their variables have heavy-tailed probability distributions. While the multiscale character of complex dynamics, such as air temperature variability, can be studied within the Shannonian framework [6, 7], the entropy concepts of Rényi and Tsallis have been proposed to cope with variables with heavy-tailed probability distributions. We will discuss how such non-Shannonian entropy concepts can be applied in inference of causality in systems with heavy-tailed probability distributions and extreme events, using examples from the climate system.</p><p>This study was supported by the Czech Science Foundation, project GA19-16066S.</p><p> </p><p> [1] N. Wiener, in: E. F. Beckenbach (Editor), Modern Mathematics for Engineers (McGraw-Hill, New York, 1956)</p><p>[2] C.W.J. Granger, Econometrica 37 (1969) 424</p><p>[3] K. Hlaváčková-Schindler et al., Phys. Rep. 441 (2007)  1</p><p>[4] M. Paluš, M. Vejmelka, Phys. Rev. E 75 (2007) 056211</p><p>[5] J. Runge et al., Nature Communications 6 (2015) 8502</p><p>[6] M. Paluš, Phys. Rev. Lett. 112 (2014) 078702</p><p> [7] N. Jajcay, J. Hlinka, S. Kravtsov, A. A. Tsonis, M. Paluš, Geophys. Res. Lett. 43(2) (2016) 902–909</p>

Modeling and Simulation of the Propagation Characteristics by Underground Current Field

2013 ◽  
Vol 385-386 ◽  
pp. 1568-1571
Author(s):  
Yi Dong Xu
Keyword(s):  
Mathematical Model ◽  
Signal Transmission ◽  
Signal Propagation ◽  
Underground Mines ◽  
Constant Current ◽  
Propagation Characteristics ◽  
Current Field ◽  
Channel Characteristics ◽  
Underground Communication ◽  
The Mathematical Model

It's one of the effective channels for short-distance wireless communications through stratum in underground mines on emergency communications. The geologic structure is complex in mines, in practical environments. It means there is a considerable difference in conductance property and channel characteristics. In order to realize effective communication through stratum, its necessary to explore and study underground signal transmission by establishing mathematical model of underground signal propagation characteristics based on the theory of constant current field and simulating the mathematical model with MATLAB. We established a simulation model of underground communication channel physics experiments and obtained the amplitude frequency characteristic of the channel, with which we can get the error comparison between theory and test.

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.

Controlling the Magnification Factor of Self-Organizing Feature Maps

1996 ◽  
Vol 8 (4) ◽  
pp. 757-771 ◽  
Author(s):  
H.-U. Bauer ◽  
R. Der
Keyword(s):  
Information Transfer ◽  
Categorical Perception ◽  
Feature Maps ◽  
Feature Map ◽  
Magnet Effect ◽  
Perceptual Magnet Effect ◽  
Optimal Value ◽  
Optimal Information ◽  
One Dimensional Maps ◽  
Self Organizing

The magnification exponents μ occurring in adaptive map formation algorithms like Kohonen's self-organizing feature map deviate for the information theoretically optimal value μ = 1 as well as from the values that optimize, e.g., the mean square distortion error (μ = 1/3 for one-dimensional maps). At the same time, models for categorical perception such as the "perceptual magnet" effect, which are based on topographic maps, require negative magnification exponents μ < 0. We present an extension of the self-organizing feature map algorithm, which utilizes adaptive local learning step sizes to actually control the magnification properties of the map. By change of a single parameter, maps with optimal information transfer, with various minimal reconstruction errors, or with an inverted magnification can be generated. Analytic results on this new algorithm are complemented by numerical simulations.

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