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Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering
Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophistica...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902046/ https://www.ncbi.nlm.nih.gov/pubmed/33634086 http://dx.doi.org/10.3389/fbioe.2021.612893 |
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author | Roy, Somtirtha Radivojevic, Tijana Forrer, Mark Marti, Jose Manuel Jonnalagadda, Vamshi Backman, Tyler Morrell, William Plahar, Hector Kim, Joonhoon Hillson, Nathan Garcia Martin, Hector |
author_facet | Roy, Somtirtha Radivojevic, Tijana Forrer, Mark Marti, Jose Manuel Jonnalagadda, Vamshi Backman, Tyler Morrell, William Plahar, Hector Kim, Joonhoon Hillson, Nathan Garcia Martin, Hector |
author_sort | Roy, Somtirtha |
collection | PubMed |
description | Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains. |
format | Online Article Text |
id | pubmed-7902046 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-79020462021-02-24 Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering Roy, Somtirtha Radivojevic, Tijana Forrer, Mark Marti, Jose Manuel Jonnalagadda, Vamshi Backman, Tyler Morrell, William Plahar, Hector Kim, Joonhoon Hillson, Nathan Garcia Martin, Hector Front Bioeng Biotechnol Bioengineering and Biotechnology Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains. Frontiers Media S.A. 2021-02-09 /pmc/articles/PMC7902046/ /pubmed/33634086 http://dx.doi.org/10.3389/fbioe.2021.612893 Text en Copyright © 2021 Roy, Radivojevic, Forrer, Marti, Jonnalagadda, Backman, Morrell, Plahar, Kim, Hillson and Garcia Martin. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Bioengineering and Biotechnology Roy, Somtirtha Radivojevic, Tijana Forrer, Mark Marti, Jose Manuel Jonnalagadda, Vamshi Backman, Tyler Morrell, William Plahar, Hector Kim, Joonhoon Hillson, Nathan Garcia Martin, Hector Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering |
title | Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering |
title_full | Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering |
title_fullStr | Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering |
title_full_unstemmed | Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering |
title_short | Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering |
title_sort | multiomics data collection, visualization, and utilization for guiding metabolic engineering |
topic | Bioengineering and Biotechnology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7902046/ https://www.ncbi.nlm.nih.gov/pubmed/33634086 http://dx.doi.org/10.3389/fbioe.2021.612893 |
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