Analytics for Metabolic Engineering

Abstract

Realizing the promise of metabolic engineering has been slowed by challenges related to moving beyond proof-of-concept examples to robust and economically viable systems. Key to advancing metabolic engineering beyond trial-and-error research is access to parts with well-defined performance metrics that can be readily applied in vastly different contexts with predictable effects. As the field now stands, research depends greatly on analytical tools that assay target molecules, transcripts, proteins, and metabolites across different hosts and pathways. Screening technologies yield specific information for many thousands of strain variants, while deep omics analysis provides a systems-level view of the cell factory. Efforts focused on a combination of these analyses yield quantitative information of dynamic processes between parts and the host chassis that drive the next engineering steps. Overall, the data generated from these types of assays aid better decision-making at the design and strain construction stages to speed progress in metabolic engineering research.

Keywords: RNA-seq; high-throughput screening; metabolic engineering; metabolomics; microfluidics; proteomics.

Figure 1

The design–build–test–learn cycle of metabolic engineering highlighting important parts of each of the components. The Design component identifies the problem, selects the desired pathway and host; the Build component selects, synthesizes, and assembles parts for incorporation into the host; the Test component validates the engineered strains for target molecule production, transcripts, proteins, and metabolites; the Learn component analyzes the Test data and informs subsequent iterations of the cycle.

Figure 2

Methods for target molecule measurements, (A) chromatography; (B) spectroscopy-based fluorescent-activated cell sorting (FACS); (C) biosensors; (D) direct injection mass spectrometry; (E) selection-based assays.

Figure 3

Next-generation sequencing data examples for (A) engineered strain QA/QC indicating potential problems in transcript levels in parts of the pathway and (B) comparative RNA-seq analysis indicating higher expression of three genes in one strain relative to another strain.

Figure 4

(Left column) Targeted proteomic assay workflows: acquire data from LC/MS, curate data in Skyline, quantify and analyze protein levels and behavior; (Right column) Applications of targeted proteomics for metabolic engineering: identification of pathway bottlenecks, characterization of synthetic biology parts, and tracking dynamic processes.

Figure 5

Developing microfluidic assays for metabolic engineering with FACS-RNA-seq, nanostructure initiator mass spectrometry (NIMS), and electrostatic spray ionization (ESTASI) mass spectrometry analysis for high-throughput, small volume analysis.