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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.10580 |
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| _version_ | 1866911879301955584 |
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| author | Franco, Eugenia Velázquez, Juan J. L. |
| author_facet | Franco, Eugenia Velázquez, Juan J. L. |
| contents | In this paper we study a simple stochastic version of the Hopfield-Ninio kinetic proofreading model. The model is characterized by means of two parameters, the unbinding time, which depends on the binding energy between a ligand and a receptor, and the number of times $M \geq 1$ that a ligand attaches to a receptor. We prove that, under suitable assumptions on M, our model has an extreme specificity, i.e. it is capable to discriminate between different ligands, and a high sensitivity, i.e. the response of the system does not change in a significant manner for ranges of ligands varying within several orders of magnitude. Additional quantities like the amount of energy used by the network or the time required to yield a response will be also computed. We also show that our results are robust, i.e., they do not depend on the specific choice of parameters that we make in this paper. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_10580 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A stochastic version of the Hopfield-Ninio kinetic proofreading model Franco, Eugenia Velázquez, Juan J. L. Probability In this paper we study a simple stochastic version of the Hopfield-Ninio kinetic proofreading model. The model is characterized by means of two parameters, the unbinding time, which depends on the binding energy between a ligand and a receptor, and the number of times $M \geq 1$ that a ligand attaches to a receptor. We prove that, under suitable assumptions on M, our model has an extreme specificity, i.e. it is capable to discriminate between different ligands, and a high sensitivity, i.e. the response of the system does not change in a significant manner for ranges of ligands varying within several orders of magnitude. Additional quantities like the amount of energy used by the network or the time required to yield a response will be also computed. We also show that our results are robust, i.e., they do not depend on the specific choice of parameters that we make in this paper. |
| title | A stochastic version of the Hopfield-Ninio kinetic proofreading model |
| topic | Probability |
| url | https://arxiv.org/abs/2405.10580 |