Assessing transfer entropy from biochemical data

Imaizumi, Tsutomu; Umeki, Nobuhisa; Yoshizawa, Ryo; Obuchi, Tomoyuki; Sako, Yasushi; Kabashima, Yoshiyuki

doi:10.1103/physreve.105.034403

Cited by 4 publications

(3 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The source code for the calculations has been deposited to https://github.com/transfer_entropy_2 ./ , which is a modification of https://github.com/kabashiy/transfer_entropy/. The method used in these programs is precisely described by Imaizumi et al (2021).…”

Section: Discussionmentioning

confidence: 99%

Evaluation of information flows in the RAS-MAPK system using transfer entropy measurements

Umeki,

Kabashima,

Sako

2023

Preprint

Self Cite

View full text Add to dashboard Cite

The RAS-MAPK system serves as a prominent signal processing network within animal cells, controlling various cell fates such as proliferation, differentiation, and cell death. Dysregulation of this system has been identified in genetic diseases and cancer affecting diverse tissues. To better understand the dynamics of this system, we employed information flow analysis based on transfer entropy (TE) between the activation dynamics of two key elements: SOS, a guanine nucleotide exchanger for the small GTPase RAS, and RAF, a RAS effector serine/threonine kinase. TE analysis enables a model-free assessment of the direction and strength of the control capability between components within the network. Here, we analyzed the information flows HeLa cells stimulated with EGF. Notably, we observed a switching of information flows during the progression of signal transduction. In the early phase, the dominant regulator was the forward information flow from SOS to RAF, contingent on the EGF dose. However, in the late phase, the EGF-dependency diminished, and mutual regulation between SOS and RAF took precedence. TE analyses on cells treated with a MEK inhibitor or expressing a SOS mutant associated with Noonan syndrome, a human genetic disease revealed anomalies in the information flow, leading to decreased followability of RAF inputs. Our findings demonstrate that reaction network analysis based on TE holds significant promise for applications in molecular pharmacology and pathology.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluation of information flows in the RAS-MAPK system using transfer entropy measurements

Umeki,

Kabashima,

Sako

2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…where f(x t ) is the probability density function [19]. Suppose there is another real-valued stationary process…”

Section: Methodsmentioning

confidence: 99%

Price-Volume Relationship in Bitcoin Futures ETF Market: An Information Perspective

Wang,

Hui

2024

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Bitcoin futures exchange-traded funds (ETFs) are recent innovations in cryptocurrency investment. This article studies the price-volume relationship in this market from an information perspective. We first propose effective mutual information which has better estimation accuracy to analyze the contemporaneous relationship. Using half-hourly trading data of the world’s largest Bitcoin futures ETF, we find that trading volume changes and returns contain information about each other and are contemporaneously dependent. Then, we employ effective transfer entropy to examine the intertemporal relationship. The results show that there exists information transfer from volume changes to returns in most of our sample period, suggesting the presence of return predictability and market inefficiency. However, information transfer in the opposite direction occurs much less frequently, and the amount is typically smaller.

show abstract

“…When multiple coupled systems co-evolve across time, information transfer, where systems exchange information with each other, frequently occurs [1]. Information transfer suggests latent causal relation between systems [1] and contributes to entropy production [2], being critical for understanding diverse complex system behaviours in physics (e.g., emergent traveling coherence [3] and chaotic communication [4]), computer science (e.g., thermodynamic properties of computation [2]), biology (e.g., neural information propagation [5,6], gene regulatory mechanisms [7], and biochemical reactions [8]), and social sciences (e.g., information-driven collective behaviours [9] and stock market dynamics [10]). As various information transfer phenomena are discovered across different disciplines, the measurement of transferred information quantities become increasingly significant.…”

mentioning

confidence: 99%

A unified theory of information transfer and causal relation

Tian¹,

Hou²,

Wang³

et al. 2022

Preprint

View full text Add to dashboard Cite

Information transfer between coupled stochastic dynamics, measured by transfer entropy and information flow, is suggested as a physical process underlying the causal relation of systems. While information transfer analysis has booming applications in both science and engineering fields, critical mysteries about its foundations remain unsolved. Fundamental yet difficult questions concern how information transfer and causal relation originate, what they depend on, how they differ from each other, and if they are created by a unified and general quantity. These questions essentially determine the validity of causal relation measurement via information transfer. Here we pursue to lay a complete theoretical basis of information transfer and causal relation. Beyond the well-known relations between these concepts that conditionally hold, we demonstrate that information transfer and causal relation universally originate from specific information synergy and redundancy phenomena characterized by high-order mutual information. More importantly, our theory analytically explains the mechanisms for information transfer and causal relation to originate, vanish, and differ from each other. Moreover, our theory naturally defines the effect sizes of information transfer and causal relation based on high-dimensional coupling events. These results may provide a unified view of information, synergy, and causal relation to bridge Pearl's causal inference theory in computer science and information transfer analysis in physics.

show abstract

Assessing transfer entropy from biochemical data

Cited by 4 publications

References 40 publications

Evaluation of information flows in the RAS-MAPK system using transfer entropy measurements

Evaluation of information flows in the RAS-MAPK system using transfer entropy measurements

Price-Volume Relationship in Bitcoin Futures ETF Market: An Information Perspective

A unified theory of information transfer and causal relation

Contact Info

Product

Resources

About