Seed2LP: seed inference in metabolic networks for reverse ecology applications.

ABSTRACT:

Motivation

A challenging problem in microbiology is to determine nutritional requirements of microorganisms and culture them, especially for the microbial dark matter detected solely with culture-independent methods. The latter foster an increasing amount of genomic sequences that can be explored with reverse ecology approaches to raise hypotheses on the corresponding populations. Building upon genome-scale metabolic networks (GSMNs) obtained from genome annotations, metabolic models predict contextualized phenotypes using nutrient information.

Results

We developed the tool Seed2LP, addressing the inverse problem of predicting source nutrients, or seeds, from a GSMN and a metabolic objective. The originality of Seed2LP is its hybrid model, combining a scalable and discrete B

SUBMITTER: Ghassemi Nedjad C 

PROVIDER: S-EPMC12007882 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature