FluxRETAP: a REaction TArget Prioritization genome-scale modeling technique for selecting genetic targets.

ABSTRACT:

Motivation

Metabolic engineering is rapidly evolving as a result of new advances in synthetic biology tools and automation platforms that enable high throughput strain construction, as well as the development of machine learning tools (ML) for biology. However, selecting genetic engineering targets that effectively guide the metabolic engineering process is still challenging. ML can provide predictive power for synthetic biology, but current technical limitations prevent the independent use of ML approaches without previous biological knowledge.

Results

Here, we present FluxRETAP, a simple and computationally inexpensive method that leverages the prior mechanistic knowledge embedded in genome-scale models for suggesting targets for genetic overexpression, downregulation or d

SUBMITTER: Czajka JJ 

PROVIDER: S-EPMC12417073 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature