Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters.

Biotechnology journal - 1 Nov 2021

Khaleghi Mohammad Karim, Savizi Iman Shahidi Pour, Lewis Nathan E, Shojaosadati Seyed Abbas

Abstract excerpt

Recent noteworthy advances in developing high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Biofuels
Share to Fermentation

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Genotype

Share to Machine Learning

Share to Models, Biological

Source

Identifiers

PMID: 34390201
DOI: 10.1002/biot.202100212

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 1 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters.PMID 34390201Read ↗

Constraint-based models predict metabolic and associated cellular functions.Semantic proximity Read ↗

Constraint-based modeling in microbial food biotechnology.Semantic proximity Read ↗

CBFA: phenotype prediction integrating metabolic models with constraints derived from experimental data.Semantic proximity Read ↗

Emerging ensembles of kinetic parameters to characterize observed metabolic phenotypes.Semantic proximity Read ↗

Constraint-based modeling identifies new putative targets to fight colistin-resistant A. baumannii infections.Semantic proximity Read ↗

Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.Semantic proximity Read ↗

Protein constraints in genome-scale metabolic models: Data integration, parameter estimation, and prediction of metabolic phenotypes.Semantic proximity Read ↗

Constraint-based modeling identifies new putative targets to fight colistin-resistant <i>A. baumannii</i> infectionsSemantic proximity Read ↗

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.Semantic proximity Read ↗

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.Semantic proximity Read ↗

OM-FBA: Integrate Transcriptomics Data with Flux Balance Analysis to Decipher the Cell Metabolism.Semantic proximity Read ↗

Genome-scale metabolic network models for industrial microorganisms metabolic engineering: Current advances and future prospects.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.