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Bertaglia G, Pareschi L, Toscani G. Modelling contagious viral dynamics: a kinetic approach based on mutual utility. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:4241-4268. [PMID: 38549326 DOI: 10.3934/mbe.2024187] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/02/2024]
Abstract
The temporal evolution of a contagious viral disease is modelled as the dynamic progression of different classes of population with individuals interacting pairwise. This interaction follows a binary mechanism typical of kinetic theory, wherein agents aim to improve their condition with respect to a mutual utility target. To this end, we introduce kinetic equations of Boltzmann-type to describe the time evolution of the probability distributions of the multi-agent system. The interactions between agents are defined using principles from price theory, specifically employing Cobb-Douglas utility functions for binary exchange and the Edgeworth box to depict the common exchange area where utility increases for both agents. Several numerical experiments presented in the paper highlight the significance of this mechanism in driving the phenomenon toward endemicity.
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Affiliation(s)
- Giulia Bertaglia
- Department of Environmental and Prevention Sciences, University of Ferrara, Ferrara, Italy
| | - Lorenzo Pareschi
- Maxwell Institute and Department of Mathematics, Heriot-Watt University, Edinburgh, UK
- Department of Mathematics and Computer Science, University of Ferrara, Ferrara, Italy
| | - Giuseppe Toscani
- Department of Mathematics, University of Pavia, Pavia, Italy
- IMATI, Institute for Applied Mathematics and Information Technologies "Enrico Magenes", Pavia, Italy
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Sanchez R, Mackenzie SA. On the thermodynamics of DNA methylation process. Sci Rep 2023; 13:8914. [PMID: 37264042 DOI: 10.1038/s41598-023-35166-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 05/13/2023] [Indexed: 06/03/2023] Open
Abstract
DNA methylation is an epigenetic mechanism that plays important roles in various biological processes including transcriptional and post-transcriptional regulation, genomic imprinting, aging, and stress response to environmental changes and disease. Consistent with thermodynamic principles acting within living systems and the application of maximum entropy principle, we propose a theoretical framework to understand and decode the DNA methylation process. A central tenet of this argument is that the probability density function of DNA methylation information-divergence summarizes the statistical biophysics underlying spontaneous methylation background and implicitly bears on the channel capacity of molecular machines conforming to Shannon's capacity theorem. On this theoretical basis, contributions from the molecular machine (enzyme) logical operations to Gibb entropy (S) and Helmholtz free energy (F) are intrinsic. Application to the estimations of S on datasets from Arabidopsis thaliana suggests that, as a thermodynamic state variable, individual methylome entropy is completely determined by the current state of the system, which in biological terms translates to a correspondence between estimated entropy values and observable phenotypic state. In patients with different types of cancer, results suggest that a significant information loss occurs in the transition from differentiated (healthy) tissues to cancer cells. This type of analysis may have important implications for early-stage diagnostics. The analysis of entropy fluctuations on experimental datasets revealed existence of restrictions on the magnitude of genome-wide methylation changes originating by organismal response to environmental changes. Only dysfunctional stages observed in the Arabidopsis mutant met1 and in cancer cells do not conform to these rules.
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Affiliation(s)
- Robersy Sanchez
- Department of Biology, The Pennsylvania State University, 361 Frear North Bldg, University Park, PA, 16802, USA.
| | - Sally A Mackenzie
- Departments of Biology and Plant Science, The Pennsylvania State University, 362 Frear North Bldg, University Park, PA, 16802, USA.
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Aristov VV, Buchelnikov AS, Nechipurenko YD. The Use of the Statistical Entropy in Some New Approaches for the Description of Biosystems. ENTROPY (BASEL, SWITZERLAND) 2022; 24:172. [PMID: 35205467 PMCID: PMC8871276 DOI: 10.3390/e24020172] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/19/2022] [Accepted: 01/21/2022] [Indexed: 01/13/2023]
Abstract
Some problems of describing biological systems with the use of entropy as a measure of the complexity of these systems are considered. Entropy is studied both for the organism as a whole and for its parts down to the molecular level. Correlation of actions of various parts of the whole organism, intercellular interactions and control, as well as cooperativity on the microlevel lead to a more complex structure and lower statistical entropy. For a multicellular organism, entropy is much lower than entropy for the same mass of a colony of unicellular organisms. Cooperativity always reduces the entropy of the system; a simple example of ligand binding to a macromolecule carrying two reaction centers shows how entropy is consistent with the ambiguity of the result in the Bernoulli test scheme. Particular attention is paid to the qualitative and quantitative relationship between the entropy of the system and the cooperativity of ligand binding to macromolecules. A kinetic model of metabolism. corresponding to Schrödinger's concept of the maintenance biosystems by "negentropy feeding", is proposed. This model allows calculating the nonequilibrium local entropy and comparing it with the local equilibrium entropy inherent in non-living matter.
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Affiliation(s)
- Vladimir V. Aristov
- Dorodnicyn Computing Centre, Federal Research Center “Computer Science and Control” of Russian Academy of Sciences, Vavilova Str. 40, 119333 Moscow, Russia
| | - Anatoly S. Buchelnikov
- Laboratory of Molecular and Cellular Biophysics, Sevastopol State University, Universitetskaya Str. 33, 299053 Sevastopol, Russia;
| | - Yury D. Nechipurenko
- Laboratory of DNA–Protein Recognition, Engelhardt Institute of Molecular Biology of Russian Academy of Sciences, Vavilova Str. 32, 119991 Moscow, Russia;
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Natal J, Ávila I, Tsukahara VB, Pinheiro M, Maciel CD. Entropy: From Thermodynamics to Information Processing. ENTROPY 2021; 23:e23101340. [PMID: 34682064 PMCID: PMC8534765 DOI: 10.3390/e23101340] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/18/2022]
Abstract
Entropy is a concept that emerged in the 19th century. It used to be associated with heat harnessed by a thermal machine to perform work during the Industrial Revolution. However, there was an unprecedented scientific revolution in the 20th century due to one of its most essential innovations, i.e., the information theory, which also encompasses the concept of entropy. Therefore, the following question is naturally raised: “what is the difference, if any, between concepts of entropy in each field of knowledge?” There are misconceptions, as there have been multiple attempts to conciliate the entropy of thermodynamics with that of information theory. Entropy is most commonly defined as “disorder”, although it is not a good analogy since “order” is a subjective human concept, and “disorder” cannot always be obtained from entropy. Therefore, this paper presents a historical background on the evolution of the term “entropy”, and provides mathematical evidence and logical arguments regarding its interconnection in various scientific areas, with the objective of providing a theoretical review and reference material for a broad audience.
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Affiliation(s)
- Jordão Natal
- Signal Processing Laboratory, Department of Electrical and Computing Engineering, University of São Paulo (USP), São Carlos 3566-590, Brazil;
- Correspondence: (J.N.); (C.D.M.)
| | - Ivonete Ávila
- Laboratory of Combustion and Carbon Captur, Department of Energy, School of Engineering, State University of São Paulo (Unesp), São Carlos 3566-590, Brazil;
| | - Victor Batista Tsukahara
- Signal Processing Laboratory, Department of Electrical and Computing Engineering, University of São Paulo (USP), São Carlos 3566-590, Brazil;
| | | | - Carlos Dias Maciel
- Signal Processing Laboratory, Department of Electrical and Computing Engineering, University of São Paulo (USP), São Carlos 3566-590, Brazil;
- Correspondence: (J.N.); (C.D.M.)
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Korhonen O, Zanin M, Papo D. Principles and open questions in functional brain network reconstruction. Hum Brain Mapp 2021; 42:3680-3711. [PMID: 34013636 PMCID: PMC8249902 DOI: 10.1002/hbm.25462] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/11/2021] [Accepted: 04/10/2021] [Indexed: 12/12/2022] Open
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.
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Affiliation(s)
- Onerva Korhonen
- Department of Computer ScienceAalto University, School of ScienceHelsinki
- Centre for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de Alarcón
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus UIBPalma de MallorcaSpain
| | - David Papo
- Fondazione Istituto Italiano di TecnologiaFerrara
- Department of Neuroscience and Rehabilitation, Section of PhysiologyUniversity of FerraraFerrara
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Sanchez R, Yang X, Maher T, Mackenzie SA. Discrimination of DNA Methylation Signal from Background Variation for Clinical Diagnostics. Int J Mol Sci 2019; 20:E5343. [PMID: 31717838 PMCID: PMC6862328 DOI: 10.3390/ijms20215343] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 10/09/2019] [Accepted: 10/24/2019] [Indexed: 12/29/2022] Open
Abstract
Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.
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Affiliation(s)
- Robersy Sanchez
- Departments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA; (X.Y.); (T.M.)
| | | | | | - Sally A. Mackenzie
- Departments of Biology and Plant Science, The Pennsylvania State University, University Park, PA 16802, USA; (X.Y.); (T.M.)
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Jinwoo L. Fluctuation Theorem of Information Exchange within an Ensemble of Paths Conditioned on Correlated-Microstates. ENTROPY 2019; 21:e21050477. [PMID: 33267191 PMCID: PMC7514966 DOI: 10.3390/e21050477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Revised: 05/02/2019] [Accepted: 05/06/2019] [Indexed: 11/16/2022]
Abstract
Fluctuation theorems are a class of equalities that express universal properties of the probability distribution of a fluctuating path functional such as heat, work or entropy production over an ensemble of trajectories during a non-equilibrium process with a well-defined initial distribution. Jinwoo and Tanaka (Jinwoo, L.; Tanaka, H. Sci. Rep.2015, 5, 7832) have shown that work fluctuation theorems hold even within an ensemble of paths to each state, making it clear that entropy and free energy of each microstate encode heat and work, respectively, within the conditioned set. Here we show that information that is characterized by the point-wise mutual information for each correlated state between two subsystems in a heat bath encodes the entropy production of the subsystems and heat bath during a coupling process. To this end, we extend the fluctuation theorem of information exchange (Sagawa, T.; Ueda, M. Phys. Rev. Lett.2012, 109, 180602) by showing that the fluctuation theorem holds even within an ensemble of paths that reach a correlated state during dynamic co-evolution of two subsystems.
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Affiliation(s)
- Lee Jinwoo
- Department of Mathematics, Kwangwoon University, 20 Kwangwoon-ro, Seoul 01897, Korea
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Loutchko D, Eisbach M, Mikhailov AS. Stochastic thermodynamics of a chemical nanomachine: The channeling enzyme tryptophan synthase. J Chem Phys 2017; 146:025101. [DOI: 10.1063/1.4973544] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
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