Abstract
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.
Highlights
Synthetic biology represents another step in the development of biology as an engineering discipline
The data provided by Omics Mock Generator (OMG) is not real, it is more realistic than randomly generated data, providing a useful resource to test the scaling of algorithms and computational tools
The strain is available on the Inventory of Composable Elements (ICE) instance and is assigned a part number that will be used to enter experimental data into Experiment Data Depot (EDD) as the step
Summary
Synthetic biology represents another step in the development of biology as an engineering discipline. The application of engineering principles such as standardized genetic parts (Canton et al, 2008; Müller and Arndt, 2012) or the application of Design-Build-Test-Learn (DBTL) cycles (Petzold et al, 2015; Nielsen and Keasling, 2016) has transformed genetic and metabolic engineering in significant ways Armed with this new engineering framework, synthetic biology is creating products to tackle societal problems in ways that only biology can enable. Metabolic engineering is often mired in trial-anderror approaches that result in very long development times (Hodgman and Jewett, 2012) In this context, machine learning has recently appeared as a powerful tool that can provide the predictive power that bioengineering needs to be effective and impactful (Carbonell et al, 2019; Radivojevicet al., 2020; Zhang et al, 2020)
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