• Zhang Y.-H.P.
• et al.

Biomanufacturing: history and perspective.

J. Ind. Microbiol. Biotechnol. 2017; 44: 773-784

• Amer B.
• Baidoo E.E.K.

Omics-driven biotechnology for industrial applications.

Front. Bioeng. Biotechnol. 2021; 9613307

• Samoudi M.
• et al.

From omics to cellular mechanisms in mammalian cell factory development.

Curr. Opin. Chem. Eng. 2021; 32100688

• Stolfa G.
• et al.

CHO-omics review: the impact of current and emerging technologies on Chinese hamster ovary based bioproduction.

Biotechnol. J. 2018; 13e1700227

• Hefzi H.
• Lewis N.E.

From random mutagenesis to systems biology in metabolic engineering of mammalian cells.

Pharmaceut. Bioprocess. 2014; 2: 355-358

• Xiong K.
• et al.

An optimized genome-wide, virus-free CRISPR screen for mammalian cells.

Cell Rep. Methods. 2021; 1100062

• Karottki K.J. la C.
• et al.

A metabolic CRISPR-Cas9 screen in Chinese hamster ovary cells identifies glutamine-sensitive genes.

Metab. Eng. 2021; 66: 114-122

• Schmieder V.
• et al.

A pooled CRISPR/AsCpf1 screen using paired gRNAs to induce genomic deletions in Chinese hamster ovary cells.

Biotechnol. Rep. (Amst). 2021; 31e00649

• Bauer N.
• et al.

An arrayed CRISPR screen reveals Myc depletion to increase productivity of difficult-to-express complex antibodies in CHO cells.

Synth. Biol. 2022; 7: ysac026

• Kretzmer C.
• et al.

De novo assembly and annotation of the CHOZN® GS genome supports high-throughput genome-scale screening.

Biotechnol. Bioeng. 2022; 119: 3632-3646

• Athieniti E.
• Spyrou G.M.

A guide to multi-omics data collection and integration for translational medicine.

Comput. Struct. Biotechnol. J. 2023; 21: 134-149

• Klingler F.
• et al.

Unveiling the CHO surfaceome: identification of cell surface proteins reveals cell aggregation-relevant mechanisms.

Biotechnol. Bioeng. 2021; 118: 3015-3028

• Jerabek T.
• et al.

The potential of emerging sub-omics technologies for CHO cell engineering.

Biotechnol. Adv. 2022; 59107978

• Kol S.
• et al.

Multiplex secretome engineering enhances recombinant protein production and purity.

Nat. Commun. 2020; 11: 1908

• Valente K.N.
• et al.

Applications of proteomic methods for CHO host cell protein characterization in biopharmaceutical manufacturing.

Curr. Opin. Biotechnol. 2018; 53: 144-150

• Lewis N.E.
• et al.

Gene expression profiling and the use of genome-scale in silico models of Escherichia coli for analysis: providing context for content.

J. Bacteriol. 2009; 191: 3437-3444

• Gutierrez J.M.
• Lewis N.E.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling.

Biotechnol. J. 2015; 10: 939-949

• Lu J.
• et al.

In silico cell factory design driven by comprehensive genome-scale metabolic models: development and challenges.

Syst. Microbiol. Biomanufact. 2022; (Published online July 22, 2022)

• Lewis N.E.
• et al.

Constraining the metabolic genotype-phenotype relationship using a phylogeny of in silico methods.

Nat. Rev. Microbiol. 2012; 10: 291-305

• Hyduke D.R.
• et al.

Analysis of omics data with genome-scale models of metabolism.

Mol. BioSyst. 2013; 9: 167-174

• Huang Z.
• Yoon S.

Identifying metabolic features and engineering targets for productivity improvement in CHO cells by integrated transcriptomics and genome-scale metabolic model.

Biochem. Eng. J. 2020; 159107624

• Lakshmanan M.
• et al.

Multi-omics profiling of CHO parental hosts reveals cell line-specific variations in bioprocessing traits.

Biotechnol. Bioeng. 2019; 116: 2117-2129

• Huang Z.
• et al.

CHO cell productivity improvement by genome-scale modeling and pathway analysis: application to feed supplements.

Biochem. Eng. J. 2020; 160107638

• Fouladiha H.
• et al.

A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production.

Bioprocess Biosyst. Eng. 2020; 43: 1381-1389

• Hoang D.
• et al.

Modulation of nutrient precursors for controlling metabolic inhibitors by genome-scale flux balance analysis.

Biotechnol. Prog. 2022; (Published online November 11, 2022)

• Opdam S.
• et al.

A systematic evaluation of methods for tailoring genome-scale metabolic models.

Cell Syst. 2017; 4: 318-329.e6

• Gopalakrishnan S.
• et al.

Guidelines for extracting biologically relevant context-specific metabolic models using gene expression data.

Metab. Eng. 2023; 75: 181-191

• Zhang C.
• Hua Q.

Applications of genome-scale metabolic models in biotechnology and systems medicine.

Front. Physiol. 2015; 6: 413

• Lerman J.A.
• et al.

In silico method for modelling metabolism and gene product expression at genome scale.

Nat. Commun. 2012; 3: 929

• Dahal S.
• et al.

Recent advances in genome-scale modeling of proteome allocation.

Curr. Opin. Syst. Biol. 2021; 26: 39-45

• Gutierrez J.M.
• et al.

Genome-scale reconstructions of the mammalian secretory pathway predict metabolic costs and limitations of protein secretion.

Nat. Commun. 2020; 11: 68

• Feizi A.
• et al.

Genome-scale modeling of the protein secretory machinery in yeast.

PLoS One. 2013; 8e63284

• Schinn S.-M.
• et al.

A genome-scale metabolic network model and machine learning predict amino acid concentrations in Chinese hamster ovary cell cultures.

Biotechnol. Bioeng. 2021; 118: 2118-2123

• Kyriakopoulos S.
• et al.

Kinetic modeling of mammalian cell culture bioprocessing: the quest to advance biomanufacturing.

Biotechnol. J. 2018; 13e1700229

• Park S.-Y.
• et al.

Bioprocess digital twins of mammalian cell culture for advanced biomanufacturing.

Curr. Opin. Chem. Eng. 2021; 33100702

• Almquist J.
• et al.

Kinetic models in industrial biotechnology – Improving cell factory performance.

Metab. Eng. 2014; 24: 38-60

• Hossler P.
• et al.

Systems analysis of N-glycan processing in mammalian cells.

PLoS One. 2007; 2e713

• Stach C.S.
• et al.

Model-driven engineering of N-linked glycosylation in Chinese hamster ovary cells.

ACS Synth. Biol. 2019; 8: 2524-2535

• Xu J.
• et al.

Systematic development of temperature shift strategies for Chinese hamster ovary cells based on short duration cultures and kinetic modeling.

MAbs. 2019; 11: 191-204

• Ben Yahia B.
• et al.

Predictive macroscopic modeling of cell growth, metabolism and monoclonal antibody production: case study of a CHO fed-batch production.

Metab. Eng. 2021; 66: 204-216

• Clarke C.
• et al.

Statistical methods for mining Chinese hamster ovary cell ‘omics data: from differential expression to integrated multilevel analysis of the biological system.

Pharmaceut. Bioprocess. 2014; 2: 469-481

• Mowbray M.
• et al.

Machine learning for biochemical engineering: a review.

Biochem. Eng. J. 2021; 172108054

• Antonakoudis A.
• et al.

The era of big data: genome-scale modelling meets machine learning.

Comput. Struct. Biotechnol. J. 2020; 18: 3287-3300

• Alden N.
• et al.

Using metabolomics to identify cell line-independent indicators of growth inhibition for Chinese hamster ovary cell-based bioprocesses.

Metabolites. 2020; 10: 199

• Barberi G.
• et al.

Anticipated cell lines selection in bioprocess scale-up through machine learning on metabolomics dynamics.

IFAC-PapersOnLine. 2021; 54: 85-90

• Barberi G.
• et al.

Integrating metabolome dynamics and process data to guide cell line selection in biopharmaceutical process development.

Metab. Eng. 2022; 72: 353-364

• Heffner K.
• et al.

Expanded Chinese hamster organ and cell line proteomics profiling reveals tissue-specific functionalities.

Sci. Rep. 2020; 10: 15841

• Budge J.D.
• et al.

Engineering of Chinese hamster ovary cell lipid metabolism results in an expanded ER and enhanced recombinant biotherapeutic protein production.

Metab. Eng. 2020; 57: 203-216

• Torres M.
• et al.

Metabolic profiling of Chinese hamster ovary cell cultures at different working volumes and agitation speeds using spin tube reactors.

Biotechnol. Prog. 2021; 37e3099

• Lin D.
• et al.

CHOmics: a web-based tool for multi-omics data analysis and interactive visualization in CHO cell lines.

PLoS Comput. Biol. 2020; 16e1008498

• Dhiman H.
• et al.

Genetic and epigenetic variation across genes involved in energy metabolism and mitochondria of Chinese hamster ovary cell lines.

Biotechnol. J. 2019; 14e1800681

• Choudhary K.S.
• et al.

Elucidation of regulatory modes for five two-component systems in Escherichia coli reveals novel relationships.

mSystems. 2020; 5e00980-20

• Huttlin E.L.
• et al.

Architecture of the human interactome defines protein communities and disease networks.

Nature. 2017; 545: 505-509

• Yeo H.C.
• et al.

Combined multivariate statistical and flux balance analyses uncover media bottlenecks to the growth and productivity of Chinese hamster ovary cell cultures.

Biotechnol. Bioeng. 2022; 119: 1740-1754

• Yao G.
• et al.

A metabolomics approach to increasing Chinese hamster ovary (CHO) cell productivity.

Metabolites. 2021; 11: 823

• Ding M.Q.
• et al.

Precision oncology beyond targeted therapy: combining omics data with machine learning matches the majority of cancer cells to effective therapeutics.

Mol. Cancer Res. 2018; 16: 269-278

• Tan K.
• et al.

A multi-omics supervised autoencoder for pan-cancer clinical outcome endpoints prediction.

BMC Med. Inform. Decis. Mak. 2020; 20: 129

• Lee A.P.
• et al.

Multi-omics profiling of a CHO cell culture system unravels the effect of culture pH on cell growth, antibody titer, and product quality.

Biotechnol. Bioeng. 2021; 118: 4305-4316

• Tzani I.
• et al.

Understanding the transcriptional response to ER stress in Chinese hamster ovary cells using multiplexed single cell RNA-seq.

bioRxiv. 2022; (Published online March 31, 2022)

• Aibar S.
• et al.

SCENIC: single-cell regulatory network inference and clustering.

Nat. Methods. 2017; 14: 1083-1086

• Jin T.
• et al.

scGRNom: a computational pipeline of integrative multi-omics analyses for predicting cell-type disease genes and regulatory networks.

Genome Med. 2021; 13: 95

• Keenan A.B.
• et al.

ChEA3: transcription factor enrichment analysis by orthogonal omics integration.

Nucleic Acids Res. 2019; 47: W212-W224

• Krämer A.
• et al.

Causal analysis approaches in Ingenuity Pathway Analysis.

Bioinformatics. 2014; 30: 523-530

• Szklarczyk D.
• et al.

The STRING database in 2021: customizable protein-protein networks, and functional characterization of user-uploaded gene/measurement sets.

Nucleic Acids Res. 2021; 49: D605-D612

• Huang J.K.
• et al.

Systematic evaluation of molecular networks for discovery of disease genes.

Cell Syst. 2018; 6: 484-495.e5

• Zhang Q.C.
• et al.

PrePPI: a structure-informed database of protein-protein interactions.

Nucleic Acids Res. 2013; 41: D828-D833

• Chiang A.W.T.
• et al.

Combating viral contaminants in CHO cells by engineering innate immunity.

Sci. Rep. 2019; 9: 8827

• Kuo C.-C.
• et al.

Dysregulation of the secretory pathway connects Alzheimer’s disease genetics to aggregate formation.

Cell Syst. 2021; 12: 873-884.e4

• Sriyudthsak K.
• et al.

Mathematical modeling and dynamic simulation of metabolic reaction systems using metabolome time series data.

Front. Mol. Biosci. 2016; 3: 15

• Saa P.A.
• Nielsen L.K.

Construction of feasible and accurate kinetic models of metabolism: A Bayesian approach.

Sci. Rep. 2016; 6: 29635

• Girbig D.
• et al.

Systematic analysis of stability patterns in plant primary metabolism.

PLoS One. 2012; 7e34686

• Andreozzi S.
• et al.

iSCHRUNK–In silico approach to characterization and reduction of uncertainty in the kinetic models of genome-scale metabolic networks.

Metab. Eng. 2016; 33: 158-168

• Sridhara V.
• et al.

Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli.

PLoS One. 2014; 9e114608

• Zhang J.
• et al.

Combining mechanistic and machine learning models for predictive engineering and optimization of tryptophan metabolism.

Nat. Commun. 2020; 11: 4880

• Shaked I.
• et al.

Metabolic network prediction of drug side effects.

Cell Syst. 2016; 2: 209-213

• Narayanan H.
• et al.

Hybrid models based on machine learning and an increasing degree of process knowledge: application to cell culture processes.

Ind. Eng. Chem. Res. 2022; 61: 8658-8672

• Tsopanoglou A.
• del Val I.J.

Moving towards an era of hybrid modelling: advantages and challenges of coupling mechanistic and data-driven models for upstream pharmaceutical bioprocesses.

Curr. Opin. Chem. Eng. 2021; 32100691

• Kotidis P.
• Kontoravdi C.

Harnessing the potential of artificial neural networks for predicting protein glycosylation.

Metab. Eng. Commun. 2020; 10e00131

• Kotidis P.
• et al.

Model-based optimization of antibody galactosylation in CHO cell culture.

Biotechnol. Bioeng. 2019; 116: 1612-1626

• Zampieri G.
• et al.

Machine and deep learning meet genome-scale metabolic modeling.

PLoS Comput. Biol. 2019; 15e1007084

• Richelle A.
• et al.

Model-based assessment of mammalian cell metabolic functionalities using omics data.

Cell Rep. Methods. 2021; 1100040

• Richelle A.
• et al.

Assessing key decisions for transcriptomic data integration in biochemical networks.

PLoS Comput. Biol. 2019; 15e1007185

• Marx N.
• et al.

How to train your cell – Towards controlling phenotypes by harnessing the epigenome of Chinese hamster ovary production cell lines.

Biotechnol. Adv. 2022; 56107924

• Tomczak K.
• et al.

The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge.

Contemp. Oncol. 2015; 19: A68-A77

• International Cancer Genome Consortium
• et al.

International network of cancer genome projects.

Nature. 2010; 464: 993-998

• ENCODE Project Consortium

An integrated encyclopedia of DNA elements in the human genome.

Nature. 2012; 489: 57-74

• Luo Y.
• et al.

New developments on the Encyclopedia of DNA Elements (ENCODE) data portal.

Nucleic Acids Res. 2020; 48: D882-D889

• Golabgir A.
• et al.

Quantitative feature extraction from the Chinese hamster ovary bioprocess bibliome using a novel meta-analysis workflow.

Biotechnol. Adv. 2016; 34: 621-633

• Kim H.U.
• et al.

Metabolic flux analysis and metabolic engineering of microorganisms.

Mol. BioSyst. 2008; 4: 113-120

• Thiele I.
• Palsson B.Ø.

A protocol for generating a high-quality genome-scale metabolic reconstruction.

Nat. Protoc. 2010; 5: 93-121

• Gu C.
• et al.

Current status and applications of genome-scale metabolic models.

Genome Biol. 2019; 20: 121

• Edwards J.S.
• Palsson B.O.

Systems properties of the Haemophilus influenzae Rd metabolic genotype.

J. Biol. Chem. 1999; 274: 17410-17416

• Edwards J.S.
• Palsson B.O.

The Escherichia coli MG1655 in silico metabolic genotype: its definition, characteristics, and capabilities.

Proc. Natl. Acad. Sci. U. S. A. 2000; 97: 5528-5533

• Förster J.
• et al.

Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network.

Genome Res. 2003; 13: 244-253

• Henry C.S.
• et al.

iBsu1103: a new genome-scale metabolic model of Bacillus subtilis based on SEED annotations.

Genome Biol. 2009; 10: R69

• He M.
• et al.

Metabolic engineering of based on genome-scale metabolic model to promote fengycin production.

3 Biotech. 2021; 11: 448

• Theron C.W.
• et al.

Integrating metabolic modeling and population heterogeneity analysis into optimizing recombinant protein production by Komagataella (Pichia) pastoris.

Appl. Microbiol. Biotechnol. 2018; 102: 63-80

• Duarte N.C.
• et al.

Global reconstruction of the human metabolic network based on genomic and bibliomic data.

Proc. Natl. Acad. Sci. U. S. A. 2007; 104: 1777-1782

• Hefzi H.
• et al.

A consensus genome-scale reconstruction of Chinese hamster ovary cell metabolism.

Cell Syst. 2016; 3: 434-443.e8

• Norsigian C.J.
• et al.

BiGG Models 2020: multi-strain genome-scale models and expansion across the phylogenetic tree.

Nucleic Acids Res. 2020; 48: D402-D406

• Fang X.
• et al.

Reconstructing organisms in silico: genome-scale models and their emerging applications.

Nat. Rev. Microbiol. 2020; 18: 731-743

• Islam M.M.
• et al.

Kinetic modeling of metabolism: Present and future.

Curr. Opin. Syst. Biol. 2021; 26: 72-78

• Shannon P.
• et al.

Cytoscape: a software environment for integrated models of biomolecular interaction networks.

Genome Res. 2003; 13: 2498-2504