Summary
The main goal of this project will be to construct a thermodynamically consistent comprehensive model for biochemical replication using stochastic thermodynamics and control theory.
I will subjugate the process of kinetic proofreading (KPR) to several thermodynamic bounds that have recently been discovered, in order to study the thermodynamic performance of this process. This will be done by considering the process as a chemical reaction network where a sequence of irreversible steps consume energy to increase replication accuracy and are hence bounded by fundamental no-go theorems such as the thermodynamic uncertainty relation (TUR) or the thermodynamic speed limit (TSL). I then investigate how the topology and reaction rates of the KPR network can be optimized with respect to error rate and dissipation by means of machine learning, to deduce how close biological systems operate to these bounds. Subsequently, I will extend the results to the conformational proofreading (CPR) process, where an energy handicap is added to the free energy to increase binding specificity at the expense of binding affinity. The CPR is a proofreading scheme that does not consume energy by burning ATP/GTP, so the entropy production has its origin in the conformational change.
Finally, I test the robustness of the aforementioned KPR and CPR networks. In realistic systems, the kinetic rates can fluctuate as a consequence of e.g., temperature or chemical density variations, which can possibly destabilize the network and lead to more errors in the replication process. This will be done by considering the networks as input-output systems that can be studied by control theory. This provides fundamental bounds such as Bode's sensitivity integral to complement the TUR and TSL.
I expect that the research results will lead to a better understanding of the fundamental limits on error correction in biological systems and will inspire further research in biophysics and structural biology.
I will subjugate the process of kinetic proofreading (KPR) to several thermodynamic bounds that have recently been discovered, in order to study the thermodynamic performance of this process. This will be done by considering the process as a chemical reaction network where a sequence of irreversible steps consume energy to increase replication accuracy and are hence bounded by fundamental no-go theorems such as the thermodynamic uncertainty relation (TUR) or the thermodynamic speed limit (TSL). I then investigate how the topology and reaction rates of the KPR network can be optimized with respect to error rate and dissipation by means of machine learning, to deduce how close biological systems operate to these bounds. Subsequently, I will extend the results to the conformational proofreading (CPR) process, where an energy handicap is added to the free energy to increase binding specificity at the expense of binding affinity. The CPR is a proofreading scheme that does not consume energy by burning ATP/GTP, so the entropy production has its origin in the conformational change.
Finally, I test the robustness of the aforementioned KPR and CPR networks. In realistic systems, the kinetic rates can fluctuate as a consequence of e.g., temperature or chemical density variations, which can possibly destabilize the network and lead to more errors in the replication process. This will be done by considering the networks as input-output systems that can be studied by control theory. This provides fundamental bounds such as Bode's sensitivity integral to complement the TUR and TSL.
I expect that the research results will lead to a better understanding of the fundamental limits on error correction in biological systems and will inspire further research in biophysics and structural biology.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101104602 |
| Start date: | 01-05-2023 |
| End date: | 30-04-2025 |
| Total budget - Public funding: | - 230 774,00 Euro |
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Original description
The main goal of this project will be to construct a thermodynamically consistent comprehensive model for biochemical replication using stochastic thermodynamics and control theory.I will subjugate the process of kinetic proofreading (KPR) to several thermodynamic bounds that have recently been discovered, in order to study the thermodynamic performance of this process. This will be done by considering the process as a chemical reaction network where a sequence of irreversible steps consume energy to increase replication accuracy and are hence bounded by fundamental no-go theorems such as the thermodynamic uncertainty relation (TUR) or the thermodynamic speed limit (TSL). I then investigate how the topology and reaction rates of the KPR network can be optimized with respect to error rate and dissipation by means of machine learning, to deduce how close biological systems operate to these bounds. Subsequently, I will extend the results to the conformational proofreading (CPR) process, where an energy handicap is added to the free energy to increase binding specificity at the expense of binding affinity. The CPR is a proofreading scheme that does not consume energy by burning ATP/GTP, so the entropy production has its origin in the conformational change.
Finally, I test the robustness of the aforementioned KPR and CPR networks. In realistic systems, the kinetic rates can fluctuate as a consequence of e.g., temperature or chemical density variations, which can possibly destabilize the network and lead to more errors in the replication process. This will be done by considering the networks as input-output systems that can be studied by control theory. This provides fundamental bounds such as Bode's sensitivity integral to complement the TUR and TSL.
I expect that the research results will lead to a better understanding of the fundamental limits on error correction in biological systems and will inspire further research in biophysics and structural biology.
Status
SIGNEDCall topic
HORIZON-MSCA-2022-PF-01-01Update Date
31-07-2023
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