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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...

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Autores principales: Roy, Somtirtha, Radivojevic, Tijana, Forrer, Mark, Marti, Jose Manuel, Jonnalagadda, Vamshi, Backman, Tyler, Morrell, William, Plahar, Hector, Kim, Joonhoon, Hillson, Nathan, Garcia Martin, Hector
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
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.
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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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