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A retrosynthetic biology approach to metabolic pathway design for therapeutic production
BACKGROUND: Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ dru...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2011
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163555/ https://www.ncbi.nlm.nih.gov/pubmed/21819595 http://dx.doi.org/10.1186/1752-0509-5-122 |
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author | Carbonell, Pablo Planson, Anne-Gaëlle Fichera, Davide Faulon, Jean-Loup |
author_facet | Carbonell, Pablo Planson, Anne-Gaëlle Fichera, Davide Faulon, Jean-Loup |
author_sort | Carbonell, Pablo |
collection | PubMed |
description | BACKGROUND: Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound. RESULTS: In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined. CONCLUSIONS: We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications. |
format | Online Article Text |
id | pubmed-3163555 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2011 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-31635552011-08-30 A retrosynthetic biology approach to metabolic pathway design for therapeutic production Carbonell, Pablo Planson, Anne-Gaëlle Fichera, Davide Faulon, Jean-Loup BMC Syst Biol Methodology Article BACKGROUND: Synthetic biology is used to develop cell factories for production of chemicals by constructively importing heterologous pathways into industrial microorganisms. In this work we present a retrosynthetic approach to the production of therapeutics with the goal of developing an in situ drug delivery device in host cells. Retrosynthesis, a concept originally proposed for synthetic chemistry, iteratively applies reversed chemical transformations (reversed enzyme-catalyzed reactions in the metabolic space) starting from a target product to reach precursors that are endogenous to the chassis. So far, a wider adoption of retrosynthesis into the manufacturing pipeline has been hindered by the complexity of enumerating all feasible biosynthetic pathways for a given compound. RESULTS: In our method, we efficiently address the complexity problem by coding substrates, products and reactions into molecular signatures. Metabolic maps are represented using hypergraphs and the complexity is controlled by varying the specificity of the molecular signature. Furthermore, our method enables candidate pathways to be ranked to determine which ones are best to engineer. The proposed ranking function can integrate data from different sources such as host compatibility for inserted genes, the estimation of steady-state fluxes from the genome-wide reconstruction of the organism's metabolism, or the estimation of metabolite toxicity from experimental assays. We use several machine-learning tools in order to estimate enzyme activity and reaction efficiency at each step of the identified pathways. Examples of production in bacteria and yeast for two antibiotics and for one antitumor agent, as well as for several essential metabolites are outlined. CONCLUSIONS: We present here a unified framework that integrates diverse techniques involved in the design of heterologous biosynthetic pathways through a retrosynthetic approach in the reaction signature space. Our engineering methodology enables the flexible design of industrial microorganisms for the efficient on-demand production of chemical compounds with therapeutic applications. BioMed Central 2011-08-05 /pmc/articles/PMC3163555/ /pubmed/21819595 http://dx.doi.org/10.1186/1752-0509-5-122 Text en Copyright ©2011 Carbonell et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Methodology Article Carbonell, Pablo Planson, Anne-Gaëlle Fichera, Davide Faulon, Jean-Loup A retrosynthetic biology approach to metabolic pathway design for therapeutic production |
title | A retrosynthetic biology approach to metabolic pathway design for therapeutic production |
title_full | A retrosynthetic biology approach to metabolic pathway design for therapeutic production |
title_fullStr | A retrosynthetic biology approach to metabolic pathway design for therapeutic production |
title_full_unstemmed | A retrosynthetic biology approach to metabolic pathway design for therapeutic production |
title_short | A retrosynthetic biology approach to metabolic pathway design for therapeutic production |
title_sort | retrosynthetic biology approach to metabolic pathway design for therapeutic production |
topic | Methodology Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3163555/ https://www.ncbi.nlm.nih.gov/pubmed/21819595 http://dx.doi.org/10.1186/1752-0509-5-122 |
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