Research graph
References from Machine learning for metabolic pathway optimization: A review. Local targets link to admitted publications; unresolved targets remain external evidence.
10.1016/j.copbio.2019.05.010
10.1016/j.copbio.2019.05.010 · External reference
10.1016/j.cell.2016.02.004
10.1016/j.cell.2016.02.004 · External reference
Highly efficient biosynthesis of astaxanthin in Saccharomyces cerevisiae by integration and tuning of algal crtZ and bkt
10.1007/s00253-015-6791-y · 2015 · External reference
De novo biosynthesis of rubusoside and rebaudiosides in engineered yeasts
10.1038/s41467-022-30826-2 · 2022 · External reference
10.1073/pnas.1206346109
10.1073/pnas.1206346109 · External reference
Recent advances in machine learning applications in metabolic engineering
10.1016/j.biotechadv.2022.108069 · 2023 · External reference
10.1016/j.ymben.2011.09.002
10.1016/j.ymben.2011.09.002 · External reference
10.1038/s41579-019-0255-9
10.1038/s41579-019-0255-9 · External reference
Publisher Correction: Morphology and mechanics of fungal mycelium
10.1038/s41598-018-20637-1 · 2018 · External reference
10.1111/j.1461-0248.2006.00997.x
10.1111/j.1461-0248.2006.00997.x · External reference
10.1021/acscatal.1c02786
10.1021/acscatal.1c02786 · External reference
10.1109/imcet.2016.7777453
10.1109/imcet.2016.7777453 · External reference
Machine learning predicts new anti-CRISPR proteins
10.1093/nar/gkaa219 · 2020 · External reference
10.1016/j.ymben.2018.03.020
10.1016/j.ymben.2018.03.020 · External reference
Machine learning-guided acyl-ACP reductase engineering for improved in vivo fatty alcohol production
10.1038/s41467-021-25831-w · 2021 · External reference
10.1016/j.cell.2018.05.015
10.1016/j.cell.2018.05.015 · External reference
Recent advances on constraint-based models by integrating machine learning
10.1016/j.copbio.2019.11.007 · 2020 · External reference
10.1038/s41579-020-00440-4
10.1038/s41579-020-00440-4 · External reference
What is flux balance analysis
10.1038/nbt.1614 · 2010 · External reference
Machine and deep learning meet genome-scale metabolic modeling
10.1371/journal.pcbi.1007084 · 2019 · External reference
Improving the phenotype predictions of a yeast genome‐scale metabolic model by incorporating enzymatic constraints
10.15252/msb.20167411 · 2017 · External reference
10.1016/j.tibtech.2021.06.010
10.1016/j.tibtech.2021.06.010 · External reference
10.1186/s13059-019-1730-3
10.1186/s13059-019-1730-3 · External reference
Analyses of extracellular protein production in Bacillus subtilis – I: Genome-scale metabolic model reconstruction based on updated gene-enzyme-reaction data
10.1016/j.bej.2017.07.005 · 2017 · External reference
High-Quality Genome-Scale Reconstruction of Corynebacterium glutamicum ATCC 13032
2021 · External reference
10.1186/s13068-017-0856-3
10.1186/s13068-017-0856-3 · External reference
From zero to hero—Design-based systems metabolic engineering of Corynebacterium glutamicum for l-lysine production
10.1016/j.ymben.2011.01.003 · 2011 · External reference
Improving NADPH availability for natural product biosynthesis in Escherichia coli by metabolic engineering
10.1016/j.ymben.2009.07.003 · 2010 · External reference
Metabolic engineering of Escherichia coli for the production of cadaverine: A five carbon diamine
10.1002/bit.22918 · 2011 · External reference
Machine learning applications in systems metabolic engineering
10.1016/j.copbio.2019.08.010 · 2020 · External reference
10.1016/j.ymben.2020.10.005
10.1016/j.ymben.2020.10.005 · External reference
10.1038/nprot.2009.203
10.1038/nprot.2009.203 · External reference
10.1093/nar/gkz061
10.1093/nar/gkz061 · External reference
10.1073/pnas.1821905116
10.1073/pnas.1821905116 · External reference
10.1016/j.copbio.2017.12.012
10.1016/j.copbio.2017.12.012 · External reference
Machine learning methods for metabolic pathway prediction
10.1186/1471-2105-11-15 · 2010 · External reference
10.1093/nar/gky537
10.1093/nar/gky537 · External reference
Unresolved reference
2018 · External reference
10.1038/nbt.1672
10.1038/nbt.1672 · External reference
10.1093/bib/bbv079
10.1093/bib/bbv079 · External reference
gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models
10.1186/s13059-021-02295-1 · 2021 · External reference
10.1093/nar/gky537
10.1093/nar/gky537 · External reference
Managing uncertainty in metabolic network structure and improving predictions using EnsembleFBA
10.1371/journal.pcbi.1005413 · 2017 · External reference
A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics
10.1038/s41467-021-25158-6 · 2021 · External reference
10.1371/journal.pcbi.1008891
10.1371/journal.pcbi.1008891 · External reference
10.1371/journal.pcbi.1005489
10.1371/journal.pcbi.1005489 · External reference
Competitive fragmentation modeling of ESI-MS/MS spectra for putative metabolite identification
10.1007/s11306-014-0676-4 · 2015 · External reference
Recent advances and prospects of computational methods for metabolite identification: a review with emphasis on machine learning approaches
10.1093/bib/bby066 · 2019 · External reference
10.3390/biom12050721
10.3390/biom12050721 · External reference
Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models
10.1038/s41467-018-07652-6 · 2018 · External reference
10.1016/j.coisb.2021.03.003
10.1016/j.coisb.2021.03.003 · External reference
10.1016/j.cels.2017.11.013
10.1016/j.cels.2017.11.013 · External reference
10.1073/pnas.1514240113
10.1073/pnas.1514240113 · External reference
Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers
10.1073/pnas.2001562117 · 2020 · External reference
10.1038/s41929-022-00798-z
10.1038/s41929-022-00798-z · External reference
10.1093/nar/gkr874
10.1093/nar/gkr874 · External reference
Metabolite identification and molecular fingerprint prediction through machine learning
10.1093/bioinformatics/bts437 · 2012 · External reference
Searching molecular structure databases with tandem mass spectra using CSI:FingerID
10.1073/pnas.1509788112 · 2015 · External reference
SIMPLE: Sparse Interaction Model over Peaks of moLEcules for fast, interpretable metabolite identification from tandem mass spectra
10.1093/bioinformatics/bty252 · 2018 · External reference
Modeling regulatory networks using machine learning for systems metabolic engineering
10.1016/j.copbio.2020.02.014 · 2020 · External reference
A hybrid deep learning framework for gene regulatory network inference from single-cell transcriptomic data
2022 · External reference
10.1073/pnas.1911536116
10.1073/pnas.1911536116 · External reference
Chemical genomic guided engineering of gamma-valerolactone tolerant yeast
10.1186/s12934-017-0848-9 · 2018 · External reference
Dissecting a complex chemical stress: chemogenomic profiling of plant hydrolysates
10.1038/msb.2013.30 · 2013 · External reference
10.1016/j.ymben.2021.06.009
10.1016/j.ymben.2021.06.009 · External reference
10.1073/pnas.1005139107
10.1073/pnas.1005139107 · External reference
Interpretable machine learning to model biomass and waste gasification
10.1016/j.biortech.2022.128062 · 2022 · External reference
10.1007/s11705-017-1621-7
10.1007/s11705-017-1621-7 · External reference
10.1021/acssynbio.9b00020
10.1021/acssynbio.9b00020 · External reference
10.1115/1.3653121
10.1115/1.3653121 · External reference
Towards a fully automated algorithm driven platform for biosystems design
10.1038/s41467-019-13189-z · 2019 · External reference
A machine learning Automated Recommendation Tool for synthetic biology
10.1038/s41467-020-18008-4 · 2020 · External reference
Protein engineering via Bayesian optimization-guided evolutionary algorithm and robotic experiments
2023 · External reference
Machine learning in enzyme engineering
10.1021/acscatal.9b04321 · 2020 · External reference
10.1038/s41586-021-03819-2
10.1038/s41586-021-03819-2 · External reference
10.1038/s41592-019-0496-6
10.1038/s41592-019-0496-6 · External reference
10.1038/s41467-022-33729-4
10.1038/s41467-022-33729-4 · External reference
10.1016/j.trechm.2022.03.001
10.1016/j.trechm.2022.03.001 · External reference
10.1038/s41586-022-04456-z
10.1038/s41586-022-04456-z · External reference
10.1073/pnas.1901979116
10.1073/pnas.1901979116 · External reference
10.1038/nmeth.1492
10.1038/nmeth.1492 · External reference
10.1016/j.sbi.2017.09.002
10.1016/j.sbi.2017.09.002 · External reference
10.1038/nrm2805
10.1038/nrm2805 · External reference
10.1038/s41467-020-15798-5
10.1038/s41467-020-15798-5 · External reference
10.1016/j.cels.2021.07.008
10.1016/j.cels.2021.07.008 · External reference
Navigating the protein fitness landscape with Gaussian processes
2013 · External reference
Adaptation in protein fitness landscapes is facilitated by indirect paths
2016 · External reference
Interpretable numerical descriptors of amino acid space
10.1089/cmb.2008.0173 · 2009 · External reference
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
2021 · External reference
10.1038/s41592-021-01100-y
10.1038/s41592-021-01100-y · External reference
Unresolved reference
2004 · External reference
10.1093/nar/gkaa786
10.1093/nar/gkaa786 · External reference
Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping
2020 · External reference
10.1021/acssynbio.9b00061
10.1021/acssynbio.9b00061 · External reference
Precise Prediction of Promoter Strength Based on a De Novo Synthetic Promoter Library Coupled with Machine Learning
10.1021/acssynbio.1c00117 · 2022 · External reference
10.1145/2939672.2939785
10.1145/2939672.2939785 · External reference
10.1109/msp.2017.2765202
10.1109/msp.2017.2765202 · External reference
10.1093/nar/gkaa325
10.1093/nar/gkaa325 · External reference
10.1093/nar/gkz772
10.1093/nar/gkz772 · External reference
Tuning the performance of synthetic riboswitches using machine learning
10.1021/acssynbio.8b00207 · 2019 · External reference
10.1021/acssynbio.2c00015
10.1021/acssynbio.2c00015 · External reference
