CRI-SPA: a high-throughput method for systematic genetic editing of yeast libraries

Cachera, Paul; Olsson, Helén; Coumou, Hilde C.; Jensen, Mads L; Sánchez, Benjamín J.; Strucko, Tomas; Broek, Marcel van den; Daran, Jean‐Marc; Jensen, Michael K.; Sonnenschein, Nikolaus; Lisby, Michael; Mortensen, Uffe Hasbro

doi:10.1093/nar/gkad656

Cited by 5 publications

(1 citation statement)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, our method can serve as a tool for genome-scale knockout screens to identify genomes with improved production phenotypes. While host-pathway interactions are normally seen as deleterious for production, it is well known that genetic knockouts can cause unexpected effects on cell physiology; for example, a large-scale study showed that a small fraction of double yeast knockouts can improve fitness 49 , while multiplexed CRISPR screens have found combinations of knockouts with improved metabolic phenotypes 50,51 . Such findings pave the way for the use of such synergistic genetic interactions as a novel strategy to improve production.…”

Section: Discussionmentioning

confidence: 99%

Modelling dynamic host-pathway interactions at the genome scale

Merzbacher,

Aodha,

Oyarzún

2024

Preprint

View full text Add to dashboard Cite

Pathway engineering offers a promising avenue for sustainable chemical production. The design of efficient production systems requires understanding complex host-pathway interactions that shape the metabolic phenotype. While genome-scale metabolic models are widespread tools for studying static host-pathway interactions, it remains a challenge to predict dynamic effects such as metabolite accumulation or enzyme overexpression during the course of fermentation. Here, we propose a novel strategy to integrate kinetic pathway models with genome-scale metabolic models of the production host. Our method enables the simulation of the local nonlinear dynamics of pathway enzymes and metabolites, informed by the global metabolic state of the host as predicted by Flux Balance Analysis (FBA). To reduce computational costs, we make extensive use of surrogate machine learning models to replace FBA calculations, achieving simulation speed-ups of at least two orders of magnitude. Through case studies on two production pathways inEscherichia coli, we demonstrate the consistency of our simulations and the ability to predict metabolite dynamics under genetic perturbations and various carbon sources. We showcase the utility of our method for screening dynamic control circuits through large-scale parameter sampling and mixed-integer optimization. Our work links together genome-scale and kinetic models into a comprehensive framework for computational strain design.

show abstract

Section: Discussionmentioning

confidence: 99%