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Updated: Nov 24, 2025

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing

Published on: May 21, 2020

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale.

Daniel Craig Zielinski¹, Arjun Patel¹, Bernhard O Palsson^1,2

¹Department of Bioengineering, University of California, San Diego, San Diego, CA 92093, USA.

|December 29, 2020

Summary

Computational tools are advancing microbial strain engineering for diverse applications. Recent progress in areas like metabolic modeling and genome analysis is closing the gap with traditional engineering, enabling more powerful strain design.

Keywords:

machine learning metabolic engineering metabolic modeling synthetic biology

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Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

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Published on: November 12, 2012

Area of Science:

Synthetic Biology and Metabolic Engineering
Computational Biology and Bioinformatics

Background:

Microbial strain engineering is expanding into critical areas like chemical production and human health.
Developing predictive design tools for biological systems has been challenging due to inherent complexity and unknown quantitative behaviors.

Purpose of the Study:

To discuss promising computational tool developments for microbial strain engineering.
To identify key research frontiers that are advancing biological design capabilities.

Main Methods:

Exploration of five key research frontiers: constraint-based modeling, kinetics and thermodynamic modeling, protein structure analysis, genome sequence analysis, and regulatory network analysis.
Highlighting the impact of experimental data and machine learning in improving the scope and accuracy of these computational methods.

Main Results:

Significant advancements in computational tools are enabling more sophisticated microbial strain design.
These tools are improving in both scope and accuracy, driven by experimental and machine learning innovations.

Conclusions:

Modern strain engineering requires comprehensive application and integration of these computational tools within a unified workflow.
The identified frontiers, powered by big data science, are expected to drive the development of more advanced and potent strain engineering strategies.