A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
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2020
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| _version_ | 1866914742408314880 |
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| author | Bardozzo, Francesco Lio', Pietro Tagliaferri, Roberto |
| author_facet | Bardozzo, Francesco Lio', Pietro Tagliaferri, Roberto |
| contents | In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features are shown as an example. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2001_04794 |
| institution | arXiv |
| publishDate | 2020 |
| record_format | arxiv |
| spellingShingle | A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways Bardozzo, Francesco Lio', Pietro Tagliaferri, Roberto Molecular Networks Machine Learning In this work, a machine learning approach for identifying the multi-omics metabolic regulatory control circuits inside the pathways is described. Therefore, the identification of bacterial metabolic pathways that are more regulated than others in term of their multi-omics follows from the analysis of these circuits . This is a consequence of the alternation of the omic values of codon usage and protein abundance along with the circuits. In this work, the E.Coli's Glycolysis and its multi-omic circuit features are shown as an example. |
| title | A machine learning approach to investigate regulatory control circuits in bacterial metabolic pathways |
| topic | Molecular Networks Machine Learning |
| url | https://arxiv.org/abs/2001.04794 |