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M.S. Bauer
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Molecular motors convert chemical energy into directed mechanical motion and
play an essential role in intracellular transport. Among these motors, kinesin is
one of the best-characterized examples, making it a useful reference system for understanding the design principles of synthetic nanoscale motors. In this thesis, a
Continuous-Time Markov Chain (CTMC) framework is developed to model the kinesin reaction cycle and to investigate how individual reaction rates influence stepping behavior, processivity, velocity, and efficiency. The model incorporates ATP-dependent transitions, external load, dissociation, and reversible reactions, allowing stochastic trajectories to be simulated using Gillespie’s algorithm. It reproduces
key experimentally observed features, including ATP-dependent velocity saturation, force-induced stalling, and realistic run lengths. Beyond reproducing kinesin-like behavior, the main objective of this work is to use this framework as a design tool
for synthetic DNA- and RNA-based molecular motors. Systematic parameter variation identifies the transitions that most strongly govern processivity, showing that
dissociation rates dominate motor performance compared to conformational transition rates. Bayesian optimization is used to identify rate combinations that maximize run length, demonstrating that substantially higher theoretical processivity is
achievable within the same kinetic architecture. The framework is then extended
to represent experimentally inspired DNA/RNA motor designs, enabling comparison between different reaction network architectures. This reveals that coordinated
changes across multiple rates are often required to improve performance beyond
single-parameter limits, and that modifications in state topology can lead to significant gains in processivity. Finally, the model serves as a general computational platform for de novo motor design, where kinesin acts as a reference architecture rather
than the primary focus. The results show that re-engineering state networks yields
synthetic motors with distinct performance regimes, and that both rate constants
and network structure must be optimized to achieve meaningful improvements. Although the model relies on simplifying assumptions, it provides an efficient and
interpretable framework for guiding the design of DNA- and RNA-based molecular
motors. ...
play an essential role in intracellular transport. Among these motors, kinesin is
one of the best-characterized examples, making it a useful reference system for understanding the design principles of synthetic nanoscale motors. In this thesis, a
Continuous-Time Markov Chain (CTMC) framework is developed to model the kinesin reaction cycle and to investigate how individual reaction rates influence stepping behavior, processivity, velocity, and efficiency. The model incorporates ATP-dependent transitions, external load, dissociation, and reversible reactions, allowing stochastic trajectories to be simulated using Gillespie’s algorithm. It reproduces
key experimentally observed features, including ATP-dependent velocity saturation, force-induced stalling, and realistic run lengths. Beyond reproducing kinesin-like behavior, the main objective of this work is to use this framework as a design tool
for synthetic DNA- and RNA-based molecular motors. Systematic parameter variation identifies the transitions that most strongly govern processivity, showing that
dissociation rates dominate motor performance compared to conformational transition rates. Bayesian optimization is used to identify rate combinations that maximize run length, demonstrating that substantially higher theoretical processivity is
achievable within the same kinetic architecture. The framework is then extended
to represent experimentally inspired DNA/RNA motor designs, enabling comparison between different reaction network architectures. This reveals that coordinated
changes across multiple rates are often required to improve performance beyond
single-parameter limits, and that modifications in state topology can lead to significant gains in processivity. Finally, the model serves as a general computational platform for de novo motor design, where kinesin acts as a reference architecture rather
than the primary focus. The results show that re-engineering state networks yields
synthetic motors with distinct performance regimes, and that both rate constants
and network structure must be optimized to achieve meaningful improvements. Although the model relies on simplifying assumptions, it provides an efficient and
interpretable framework for guiding the design of DNA- and RNA-based molecular
motors. ...
Molecular motors convert chemical energy into directed mechanical motion and
play an essential role in intracellular transport. Among these motors, kinesin is
one of the best-characterized examples, making it a useful reference system for understanding the design principles of synthetic nanoscale motors. In this thesis, a
Continuous-Time Markov Chain (CTMC) framework is developed to model the kinesin reaction cycle and to investigate how individual reaction rates influence stepping behavior, processivity, velocity, and efficiency. The model incorporates ATP-dependent transitions, external load, dissociation, and reversible reactions, allowing stochastic trajectories to be simulated using Gillespie’s algorithm. It reproduces
key experimentally observed features, including ATP-dependent velocity saturation, force-induced stalling, and realistic run lengths. Beyond reproducing kinesin-like behavior, the main objective of this work is to use this framework as a design tool
for synthetic DNA- and RNA-based molecular motors. Systematic parameter variation identifies the transitions that most strongly govern processivity, showing that
dissociation rates dominate motor performance compared to conformational transition rates. Bayesian optimization is used to identify rate combinations that maximize run length, demonstrating that substantially higher theoretical processivity is
achievable within the same kinetic architecture. The framework is then extended
to represent experimentally inspired DNA/RNA motor designs, enabling comparison between different reaction network architectures. This reveals that coordinated
changes across multiple rates are often required to improve performance beyond
single-parameter limits, and that modifications in state topology can lead to significant gains in processivity. Finally, the model serves as a general computational platform for de novo motor design, where kinesin acts as a reference architecture rather
than the primary focus. The results show that re-engineering state networks yields
synthetic motors with distinct performance regimes, and that both rate constants
and network structure must be optimized to achieve meaningful improvements. Although the model relies on simplifying assumptions, it provides an efficient and
interpretable framework for guiding the design of DNA- and RNA-based molecular
motors.
play an essential role in intracellular transport. Among these motors, kinesin is
one of the best-characterized examples, making it a useful reference system for understanding the design principles of synthetic nanoscale motors. In this thesis, a
Continuous-Time Markov Chain (CTMC) framework is developed to model the kinesin reaction cycle and to investigate how individual reaction rates influence stepping behavior, processivity, velocity, and efficiency. The model incorporates ATP-dependent transitions, external load, dissociation, and reversible reactions, allowing stochastic trajectories to be simulated using Gillespie’s algorithm. It reproduces
key experimentally observed features, including ATP-dependent velocity saturation, force-induced stalling, and realistic run lengths. Beyond reproducing kinesin-like behavior, the main objective of this work is to use this framework as a design tool
for synthetic DNA- and RNA-based molecular motors. Systematic parameter variation identifies the transitions that most strongly govern processivity, showing that
dissociation rates dominate motor performance compared to conformational transition rates. Bayesian optimization is used to identify rate combinations that maximize run length, demonstrating that substantially higher theoretical processivity is
achievable within the same kinetic architecture. The framework is then extended
to represent experimentally inspired DNA/RNA motor designs, enabling comparison between different reaction network architectures. This reveals that coordinated
changes across multiple rates are often required to improve performance beyond
single-parameter limits, and that modifications in state topology can lead to significant gains in processivity. Finally, the model serves as a general computational platform for de novo motor design, where kinesin acts as a reference architecture rather
than the primary focus. The results show that re-engineering state networks yields
synthetic motors with distinct performance regimes, and that both rate constants
and network structure must be optimized to achieve meaningful improvements. Although the model relies on simplifying assumptions, it provides an efficient and
interpretable framework for guiding the design of DNA- and RNA-based molecular
motors.