期刊
FRONTIERS IN BIOENGINEERING AND BIOTECHNOLOGY
卷 9, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fbioe.2021.612893
关键词
machine learning; flux analysis; metabolic engineering; biofuels; synthetic biology; multiomics analysis
资金
- U.S. Department of Energy, Energy Efficiency and Renewable Energy, Bioenergy Technologies Office
- Office of Science [DE-AC02-05CH11231]
- Basque Government through the BERC 2018-2021 program
- Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa excellence accreditation [SEV-2017-0718]
Biology has evolved from a descriptive science to a design science, utilizing computational tools to predict bioengineering outcomes by integrating multiomics data. By uploading, visualizing, and outputting data to online repositories, and training machine learning algorithms to recommend new strain designs, improvements in production can be achieved in bioengineered strains.
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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