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Stochastic thermodynamics of multicomponent molecular machines

Record Type:	Electronic resources : Monograph/item
Title/Author:	Stochastic thermodynamics of multicomponent molecular machines by Matthew Leighton.
Author:	Leighton, Matthew.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xvi, 157 p. :ill., digital ;24 cm.
Notes:	"Doctoral thesis accepted by Simon Fraser University, Burnaby, Canada."
Contained By:	Springer Nature eBook
Subject:	Molecular machinery.
Online resource:	https://doi.org/10.1007/978-3-032-02204-2
ISBN:	9783032022042$q(electronic bk.)

Stochastic thermodynamics of multicomponent molecular machines
Leighton, Matthew.

Stochastic thermodynamics of multicomponent molecular machines [electronic resource] /by Matthew Leighton. - Cham :Springer Nature Switzerland :2025. - xvi, 157 p. :ill., digital ;24 cm. - Springer theses,2190-5061. - Springer theses..

"Doctoral thesis accepted by Simon Fraser University, Burnaby, Canada."

Introduction -- Theoretical Background -- Jensen Bound on the Entropy Production Rate for Multicomponent Stochastic Systems -- Performance Scaling and Trade-offs for Collective Motor-Driven Transport -- Dynamic and Thermodynamic Bounds for Collective Motor-Driven Transport -- Inferring Subsystem Efficiencies in Bipartite Molecular Machines -- Information Arbitrage in Bipartite Heat Engines -- Information Arbitrage in Light-Harvesting Molecular Machines -- Conclusion.

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.

ISBN: 9783032022042$q(electronic bk.)

Standard No.: 10.1007/978-3-032-02204-2doiSubjects--Topical Terms:

822571
Molecular machinery.

LC Class. No.: TP248.25.M645 / L45 2025

Dewey Class. No.: 621.4021

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505 0 $a Introduction -- Theoretical Background -- Jensen Bound on the Entropy Production Rate for Multicomponent Stochastic Systems -- Performance Scaling and Trade-offs for Collective Motor-Driven Transport -- Dynamic and Thermodynamic Bounds for Collective Motor-Driven Transport -- Inferring Subsystem Efficiencies in Bipartite Molecular Machines -- Information Arbitrage in Bipartite Heat Engines -- Information Arbitrage in Light-Harvesting Molecular Machines -- Conclusion.
520 $a 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.
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