mso-bidi-language: AR-SA;">This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines.
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Hardcover. Etat : new. Hardcover. This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines. The work in this book derives fundamental limits, explores model systems, and develops tools for inference from experimental data, all of which allow for novel analysis of molecular machines. Particular innovations reported in this thesis include: a new Jensen inequality relating subsystem entropy production to physically accessible measurements, which leads to performance bounds and Pareto frontiers for collective transport of intracellular cargo; a new approach to quantify the efficiency of coupled components in multicomponent motors, drawing upon the language of information thermodynamics; and a new theoretical understanding of symmetries between heat and information engines, with surprising implications for light-harvesting molecular machines like those responsible for photosynthesis. Ultimately, these advances lead to the identification of design principles which will help to guide future engineering of synthetic nanomachines. mso-bidi-language: AR-SA;">This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9783032022035
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Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines. The work in this book derives fundamental limits, explores model systems, and develops tools for inference from experimental data, all of which allow for novel analysis of molecular machines. Particular innovations reported in this thesis include: a new Jensen inequality relating subsystem entropy production to physically accessible measurements, which leads to performance bounds and Pareto frontiers for collective transport of intracellular cargo; a new approach to quantify the efficiency of coupled components in multicomponent motors, drawing upon the language of information thermodynamics; and a new theoretical understanding of symmetries between heat and information engines, with surprising implications for light-harvesting molecular machines like those responsible for photosynthesis. Ultimately, these advances lead to the identification of design principles which will help to guide future engineering of synthetic nanomachines. 176 pp. Englisch. N° de réf. du vendeur 9783032022035
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Buch. Etat : Neu. Stochastic Thermodynamics of Multicomponent Molecular Machines | Matthew Leighton | Buch | xvi | Englisch | 2025 | Springer | EAN 9783032022035 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. N° de réf. du vendeur 134141406
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Buch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines. The work in this book derives fundamental limits, explores model systems, and develops tools for inference from experimental data, all of which allow for novel analysis of molecular machines. Particular innovations reported in this thesis include: a new Jensen inequality relating subsystem entropy production to physically accessible measurements, which leads to performance bounds and Pareto frontiers for collective transport of intracellular cargo; a new approach to quantify the efficiency of coupled components in multicomponent motors, drawing upon the language of information thermodynamics; and a new theoretical understanding of symmetries between heat and information engines, with surprising implications for light-harvesting molecular machines like those responsible for photosynthesis. Ultimately, these advances lead to the identification of design principles which will help to guide future engineering of synthetic nanomachines.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 176 pp. Englisch. N° de réf. du vendeur 9783032022035
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Buch. Etat : Neu. Neuware - This thesis makes significant advances in the theoretically-grounded analysis of experimental biophysical data, applying existing and novel tools from stochastic thermodynamics to study multicomponent biological molecular machines. The work in this book derives fundamental limits, explores model systems, and develops tools for inference from experimental data, all of which allow for novel analysis of molecular machines. Particular innovations reported in this thesis include: a new Jensen inequality relating subsystem entropy production to physically accessible measurements, which leads to performance bounds and Pareto frontiers for collective transport of intracellular cargo; a new approach to quantify the efficiency of coupled components in multicomponent motors, drawing upon the language of information thermodynamics; and a new theoretical understanding of symmetries between heat and information engines, with surprising implications for light-harvesting molecular machines like those responsible for photosynthesis. Ultimately, these advances lead to the identification of design principles which will help to guide future engineering of synthetic nanomachines. N° de réf. du vendeur 9783032022035
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