Evidence map›Paper›PMID 30165868›Full record

ReviewMicrobial cell factories2018

An estimate is worth about a thousand experiments: using order-of-magnitude estimates to identify cellular engineering targets.

Kevin James Metcalf, Marilyn F Slininger Lee, Christopher Matthew Jakobson, Danielle Tullman-Ercek

Abstract readReview
In one paragraph

Review in Microbial cell factories, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Advances in Machine Learning Models for Predicting Enzyme Kinetic Parameters.Journal of chemical information and modeling · 2026
    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

4 authors.

Kevin James MetcalfDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA. kevin.metcalf@northwestern.edu.ORCID http://orcid.org/0000-0002-2721-3378
Marilyn F Slininger LeeDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA.
Christopher Matthew JakobsonDepartment of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, 94720, USA.
Danielle Tullman-ErcekDepartment of Chemical and Biological Engineering, Northwestern University, Evanston, IL, 60208, USA.

Funding

Army Research Office W911NF-15-1-0144Division of Graduate Education DGE 1106400Division of Molecular and Cellular Biosciences MCB1150567
6 · The paper itself

Abstract

Biotechnological processes use microbes to convert abundant molecules, such as glucose, into high-value products, such as pharmaceuticals, commodity and fine chemicals, and energy. However, from the outset of the development of a new bioprocess, it is difficult to determine the feasibility, expected yields, and targets for engineering. In this review, we describe a methodology that uses rough estimates to assess the feasibility of a process, approximate the expected product titer of a biological system, and identify variables to manipulate in order to achieve the desired performance. This methodology uses estimates from literature and biological intuition, and can be applied in the early stages of a project to help plan future engineering. We highlight recent literature examples, as well as two case studies from our own work, to demonstrate the use and power of rough estimates. Describing and predicting biological function using estimates guides the research and development phase of new bioprocesses and is a useful first step to understand and build a new microbial factory.

Indexed as

Cell EngineeringSynthetic BiologyBioprocessMetabolic engineeringSynthetic biology

Identifiers

PMID30165868
PMCPMC6117934

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.