Evidence map›Paper›PMID 37062598›Full record

ReviewTrends in biotechnology2023

From observational to actionable: rethinking omics in biologics production.

Helen O Masson, Karen Julie la Cour Karottki, Jasmine Tat, Hooman Hefzi, Nathan E Lewis

Abstract readReview
In one paragraph

Review in Trends in biotechnology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Deciphering the determinants of recombinant protein expression across the human secretome.Proceedings of the National Academy of Sciences of the United States of America · 2025
    Article
  5. Review
  6. Review
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

5 authors.

Helen O MassonDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA.
Karen Julie la Cour KarottkiDepartment of Pediatrics, University of California, San Diego, La Jolla, CA, USA.
Jasmine TatDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Amgen Inc., Thousand Oaks, CA, USA.
Hooman HefziBiogen, Inc., Cambridge, MA, USA. Electronic address: hooman.hefzi@biogen.com.
Nathan E LewisDepartment of Bioengineering, University of California, San Diego, La Jolla, CA, USA; Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA. Electronic address: nlewisres@ucsd.edu.

Funding

Unraveling the mammalian secretory pathway through systems biology and algorithm developmentR35GM119850 · NIGMS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH · 2016 to 2025
$4.3M
ImmCellFIE: producing high-resolution snapshots of the functions of immune cellsUH2AI153029 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI LEWIS, NATHAN ENOCH, ROBASKY, KIMBERLY JOAN · 2020 to 2021
$445k
NIAID NIH HHS UH2 AI153029NIGMS NIH HHS R35 GM119850
6 · The paper itself

Abstract

As the era of omics continues to expand with increasing ubiquity and success in both academia and industry, omics-based experiments are becoming commonplace in industrial biotechnology, including efforts to develop novel solutions in bioprocess optimization and cell line development. Omic technologies provide particularly valuable 'observational' insights for discovery science, especially in academic research and industrial R&D; however, biomanufacturing requires a different paradigm to unlock 'actionable' insights from omics. Here, we argue the value of omic experiments in biotechnology can be maximized with deliberate selection of omic approaches and forethought about analysis techniques. We describe important considerations when designing and implementing omic-based experiments and discuss how systems biology analysis strategies can enhance efforts to obtain actionable insights in mammalian-based biologics production.

Indexed as

Biological ProductsAnimalsBiotechnologyCell LineMammalsSystems BiologyBiological Productsbiopharmaceutical protein productionbioprocess optimizationcell line developmentmechanistic modelsomicsstatistical inference

Identifiers

PMID37062598
PMCPMC10524802

What OpenQuestion holds

Textmetadata
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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.