Deriving the Fluctuation Theorem for Information-Transmission Systems Using a Cyclic Chain-Reaction Sequence

2021 ◽  
Author(s):  
Tatsuaki Tsuruyama
Keyword(s):  
Signal Transduction ◽  
Reaction Time ◽  
Information Transmission ◽  
Cell Biology ◽  
Chemical Species ◽  
Fluctuation Theorem ◽  
Entropy Production Rate ◽  
Chain Reactions ◽  
Biochemical Systems ◽  
Reaction Chain

Abstract Chemical chain-reactions are pathways that can transmit information, as demonstrated by signal-transduction reactions in cell biology. In this study, we defined entropy as the logarithm of the concentration ratio of chemical species and considered the channel capacity for information transmission by maximizing the entropy. We hypothesized that the reaction chain has an orientation in which the reaction time for the reverse reaction is sufficiently long. According to this model, the logarithm of the forward and reverse transitional probability ratio was found to indicate the entropy-time average per unit reaction time, corresponding to the fluctuation theorem for thermodynamics regarding entropy production rate. This conclusion illuminates the process of signal transduction in cells and other biochemical systems and may provide insights into the relation between thermodynamic and information entropy.

scholarly journals Entropy in Cell Biology: Information Thermodynamics of a Binary Code and Szilard Engine Chain Model of Signal Transduction

Entropy ◽  
2018 ◽  
Vol 20 (8) ◽  
pp. 617 ◽  
Author(s):  
Tatsuaki Tsuruyama
Keyword(s):  
Signal Transduction ◽  
Cell Biology ◽  
Binary Code ◽  
Single Step ◽  
Entropy Production Rate ◽  
Chain Model ◽  
Code System ◽  
Signalling Molecules ◽  
Step Model ◽  
Biological Reaction

A model of signal transduction from the perspective of informational thermodynamics has been reported in recent studies, and several important achievements have been obtained. The first achievement is that signal transduction can be modelled as a binary code system, in which two forms of signalling molecules are utilised in individual steps. The second is that the average entropy production rate is consistent during the signal transduction cascade when the signal event number is maximised in the model. The third is that a Szilard engine can be a single-step model in the signal transduction. This article reviews these achievements and further introduces a new chain of Szilard engines as a biological reaction cascade (BRC) model. In conclusion, the presented model provides a way of computing the channel capacity of a BRC.

Plant Extracts and their Secondary Metabolites as Modulators of Kinases

2020 ◽  
Vol 20 (12) ◽  
pp. 1093-1104 ◽  
Author(s):  
Muhammad Shoaib Ali Gill ◽  
Hammad Saleem ◽  
Nafees Ahemad
Keyword(s):  
Signal Transduction ◽  
Secondary Metabolites ◽  
Plant Extracts ◽  
Cell Biology ◽  
Kinase Inhibitor ◽  
Plant Secondary Metabolites ◽  
Biological Properties ◽  
Drug Candidate ◽  
New Chemical Entities ◽  
Natural Flora

Natural Products (NP), specifically from medicinal plants or herbs, have been extensively utilized to analyze the fundamental mechanisms of ultimate natural sciences as well as therapeutics. Isolation of secondary metabolites from these sources and their respective biological properties, along with their lower toxicities and cost-effectiveness, make them a significant research focus for drug discovery. In recent times, there has been a considerable focus on isolating new chemical entities from natural flora to meet the immense demand for kinase modulators, and also to overcome major unmet medical challenges in relation to signal transduction pathways. The signal transduction systems are amongst the foremost pathways involved in the maintenance of life and protein kinases play an imperative part in these signaling pathways. It is important to find a kinase inhibitor, as it can be used not only to study cell biology but can also be used as a drug candidate for cancer and metabolic disorders. A number of plant extracts and their isolated secondary metabolites such as flavonoids, phenolics, terpenoids, and alkaloids have exhibited activities against various kinases. In the current review, we have presented a brief overview of some important classes of plant secondary metabolites as kinase modulators. Moreover, a number of phytocompounds with kinase inhibition potential, isolated from different plant species, are also discussed.

scholarly journals Training an asymmetric signal perceptron through reinforcement in an artificial chemistry

2014 ◽  
Vol 11 (93) ◽  
pp. 20131100 ◽  
Author(s):  
Peter Banda ◽  
Christof Teuscher ◽  
Darko Stefanovic
Keyword(s):  
State Of The Art ◽  
Chemical Species ◽  
Mass Action ◽  
Mass Action Kinetics ◽  
Dna Strand Displacement ◽  
Autonomous Adaptation ◽  
Biochemical Systems ◽  
Dna Strand ◽  
Logic Functions ◽  
Application Specific

State-of-the-art biochemical systems for medical applications and chemical computing are application-specific and cannot be reprogrammed or trained once fabricated. The implementation of adaptive biochemical systems that would offer flexibility through programmability and autonomous adaptation faces major challenges because of the large number of required chemical species as well as the timing-sensitive feedback loops required for learning. In this paper, we begin addressing these challenges with a novel chemical perceptron that can solve all 14 linearly separable logic functions. The system performs asymmetric chemical arithmetic, learns through reinforcement and supports both Michaelis–Menten as well as mass-action kinetics. To enable cascading of the chemical perceptrons, we introduce thresholds that amplify the outputs. The simplicity of our model makes an actual wet implementation, in particular by DNA-strand displacement, possible.

Changes in spinal excitability during choice reaction time: The H reflex as a probe of information transmission

2000 ◽  
Vol 37 (3) ◽  
pp. 385-393 ◽  
Author(s):  
Thierry Hasbroucq ◽  
Motoyuki Akamatsu ◽  
Boris Burle ◽  
Michel Bonnet ◽  
Camille-Aime Possamai
Keyword(s):  
Reaction Time ◽  
Information Transmission ◽  
Choice Reaction Time ◽  
H Reflex ◽  
Spinal Excitability

scholarly journals SAM68: Signal Transduction and RNA Metabolism in Human Cancer

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Paola Frisone ◽  
Davide Pradella ◽  
Anna Di Matteo ◽  
Elisa Belloni ◽  
Claudia Ghigna ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Signal Transduction ◽  
Nuclear Export ◽  
Cell Biology ◽  
Rna Binding ◽  
Rna Binding Proteins ◽  
Splice Variants ◽  
Human Cancer ◽  
Rna Metabolism ◽  
Regulatory Sequences

Alterations in expression and/or activity of splicing factors as well as mutations incis-acting splicing regulatory sequences contribute to cancer phenotypes. Genome-wide studies have revealed more than 15,000 tumor-associated splice variants derived from genes involved in almost every aspect of cancer cell biology, including proliferation, differentiation, cell cycle control, metabolism, apoptosis, motility, invasion, and angiogenesis. In the past decades, several RNA binding proteins (RBPs) have been implicated in tumorigenesis. SAM68 (SRC associated in mitosis of 68 kDa) belongs to the STAR (signal transduction and activation of RNA metabolism) family of RBPs. SAM68 is involved in several steps of mRNA metabolism, from transcription to alternative splicing and then to nuclear export. Moreover, SAM68 participates in signaling pathways associated with cell response to stimuli, cell cycle transitions, and viral infections. Recent evidence has linked this RBP to the onset and progression of different tumors, highlighting misregulation of SAM68-regulated splicing events as a key step in neoplastic transformation and tumor progression. Here we review recent studies on the role of SAM68 in splicing regulation and we discuss its contribution to aberrant pre-mRNA processing in cancer.

The structure, biosynthesis and functions of glycosylphosphatidylinositol anchors, and the contributions of trypanosome research

1999 ◽  
Vol 112 (17) ◽  
pp. 2799-2809 ◽  
Author(s):  
M.A. Ferguson
Keyword(s):  
Signal Transduction ◽  
Cell Surface ◽  
Cell Biology ◽  
General Structure ◽  
Surface Glycoprotein ◽  
Variant Surface Glycoprotein ◽  
Membrane Microdomains ◽  
Surface Coat ◽  
Cell Surface Molecules ◽  
Surface Molecules

The discovery of glycosylphosphatidylinositol (GPI) membrane anchors has had a significant impact on several areas of eukaryote cell biology. Studies of the African trypanosome, which expresses a dense surface coat of GPI-anchored variant surface glycoprotein, have played important roles in establishing the general structure of GPI membrane anchors and in delineating the pathway of GPI biosynthesis. The major cell-surface molecules of related parasites are also rich in GPI-anchored glycoproteins and/or GPI-related glycophospholipids, and differences in substrate specificity between enzymes of trypanosomal and mammalian GPI biosynthesis may have potential for the development of anti-parasite therapies. Apart from providing stable membrane anchorage, GPI anchors have been implicated in the sequestration of GPI-anchored proteins into specialised membrane microdomains, known as lipid rafts, and in signal transduction events.

scholarly journals Exploring the Risk Factors of Infrastructure PPP Projects for Sustainable Delivery: A Social Network Perspective

2020 ◽  
Vol 12 (10) ◽  
pp. 4152 ◽  
Author(s):  
Yan Wang ◽  
Yujie Wang ◽  
Xiuyu Wu ◽  
Jiwang Li
Keyword(s):  
Risk Factors ◽  
Risk Factor ◽  
Social Network ◽  
Credit Rating ◽  
Legal Change ◽  
Structured Interviews ◽  
Chain Reactions ◽  
Financing Risk ◽  
Government Credit ◽  
Reaction Chain

Due to the relatively long period and large capital flow of public-private partnership (PPP) projects, PPP participants are faced with a complex risk situation impeding the sustainable project delivery. In recent years, risk management of PPP projects has received increasing attention. In this paper, twenty risk factors associated with infrastructure PPP projects were identified by literature review and in-depth case studies. Relationship data for these twenty typical risk factors were obtained through structured interviews. Based on the obtained data, the risk relationship network within infrastructure PPP projects was identified, and the network structure characteristics were analyzed, including individual node attributes and the influence and cohesion of subgroups. The results indicate that key risk factor nodes can form a reaction chain via bridge nodes that can trigger a risk domino effect within PPP projects. Specifically, the key risk factors of PPP projects are divided into two categories, the first of which include risk factors that have powerful and independent influence, such as delay in government approval, government credit, and imperfect legal and regulatory systems. The second category includes risk factors that are highly vulnerable and easily influenced, such as completion risks, insufficient revenue in the market, and fee change. A key risk factor reaction chain is one in which legal change leads to a decline in government credit rating, triggering a contract risk. Twelve bridge nodes were identified that play an important intermediary role in the network, e.g., legal change, public objection, and financing risk. This paper extends the application of social network analysis in PPP projects management research and identifies the key risk factors and crucial factors influencing chain reactions in PPP projects. The results provide a more in-depth understanding of sustainable PPP project management for government agencies and private enterprises.

Sign in / Sign up

Export Citation Format

Share Document