Evidence map›Paper›PMID 36245012›Full record

ArticleBioprocess and biosystems engineering2022

Genome-scale modeling of Chinese hamster ovary cells by hybrid semi-parametric flux balance analysis.

João R C Ramos, Gil P Oliveira, Patrick Dumas, Rui Oliveira

Open access · hybridAbstract read
In one paragraph

Article in Bioprocess and biosystems engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
2.3field-weighted citation impact, top 10% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 28 citations in OpenAlex.

  1. Review
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  3. A Hybrid Modeling Framework for Predictive Digital Twins of CHO Cell Culture.Computational and structural biotechnology journal · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 2 countries.

João R C RamosLAQV REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Campus Caparica, 2829-516, Caparica, Portugal.
Gil P OliveiraLAQV REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Campus Caparica, 2829-516, Caparica, Portugal.
Patrick DumasGSK, 89 rue de l'Institut, 1330, Rixensart, Belgium.
Rui OliveiraLAQV REQUIMTE, Department of Chemistry, NOVA School of Science and Technology, NOVA University Lisbon, Campus Caparica, 2829-516, Caparica, Portugal. rmo@fct.unl.pt.
Universidade Nova de Lisboa · PTGlaxoSmithKline (Belgium) · BE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Flux balance analysis (FBA) is currently the standard method to compute metabolic fluxes in genome-scale networks. Several FBA extensions employing diverse objective functions and/or constraints have been published. Here we propose a hybrid semi-parametric FBA extension that combines mechanistic-level constraints (parametric) with empirical constraints (non-parametric) in the same linear program. A CHO dataset with 27 measured exchange fluxes obtained from 21 reactor experiments served to evaluate the method. The mechanistic constraints were deduced from a reduced CHO-K1 genome-scale network with 686 metabolites, 788 reactions and 210 degrees of freedom. The non-parametric constraints were obtained by principal component analysis of the flux dataset. The two types of constraints were integrated in the same linear program showing comparable computational cost to standard FBA. The hybrid FBA is shown to significantly improve the specific growth rate prediction under different constraints scenarios. A metabolically efficient cell growth feed targeting minimal byproducts accumulation was designed by hybrid FBA. It is concluded that integrating parametric and nonparametric constraints in the same linear program may be an efficient approach to reduce the solution space and to improve the predictive power of FBA methods when critical mechanistic information is missing.

Indexed as

Metabolic Networks and PathwaysModels, BiologicalAnimalsCHO CellsCricetinaeCricetulusCHO-K1 cellsCulture media designFlux balance analysisGenome-scale modelingHybrid semi-parametric systemsMachine learning

Identifiers

PMID36245012
PMCPMC9616788
OpenAlexW4306360081

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.