Evidence map›Paper›PMID 31157778›Full record

ArticleJournal of visualized experiments : JoVE2019

Designing Automated, High-throughput, Continuous Cell Growth Experiments Using eVOLVER.

Zachary J Heins, Christopher P Mancuso, Szilvia Kiriakov, Brandon G Wong, Caleb J Bashor, Ahmad S Khalil

Open access · greenAbstract readVideo-Audio Media
In one paragraph

Article in Journal of visualized experiments : JoVE, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.8field-weighted citation impact, top 29% 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

12 citing papers in PubMed, 22 citations in OpenAlex.

  1. Article
  2. Article
  3. Bioengineering hybrid artificial life.Frontiers in bioinformatics · 2025
    Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Long-term evolution of proliferating cells using the eVOLVER platform.bioRxiv : the preprint server for biology · 2023
    Article
  9. Review
  10. Article
  11. Article
  12. 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

6 authors at 2 institutions in 2 countries.

Zachary J HeinsBiological Design Center, Boston University; Department of Biomedical Engineering, Boston University.
Christopher P MancusoBiological Design Center, Boston University; Department of Biomedical Engineering, Boston University.
Szilvia KiriakovBiological Design Center, Boston University; Program in Molecular Biology, Cell Biology and Biochemistry, Boston University.
Brandon G WongBiological Design Center, Boston University; Department of Biomedical Engineering, Boston University.
Caleb J BashorDepartment of Bioengineering, Rice University; caleb.bashor@rice.edu.
Ahmad S KhalilBiological Design Center, Boston University; Department of Biomedical Engineering, Boston University; Wyss Institute for Biologically Inspired Engineering, Harvard University; khalil@bu.edu.
Boston University · USRice University · US

Funding

TRAINING PROGRAM IN QUANTITATIVE BIOLOGY AND PHYSIOLOGYT32GM008764 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI WHITE, JOHN A. · 2001 to 2021
$5.9M
Combatting antibiotic resistance with synthetic biology technologiesDP2AI131083 · NIAID · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI KHALIL, AHMAD SAMIR · 2016 to 2016
$2.5M
NIAID NIH HHS DP2 AI131083NIGMS NIH HHS T32 GM008764
6 · The paper itself

Abstract

Continuous culture methods enable cells to be grown under quantitatively controlled environmental conditions, and are thus broadly useful for measuring fitness phenotypes and improving our understanding of how genotypes are shaped by selection. Extensive recent efforts to develop and apply niche continuous culture devices have revealed the benefits of conducting new forms of cell culture control. This includes defining custom selection pressures and increasing throughput for studies ranging from long-term experimental evolution to genome-wide library selections and synthetic gene circuit characterization. The eVOLVER platform was recently developed to meet this growing demand: a continuous culture platform with a high degree of scalability, flexibility, and automation. eVOLVER provides a single standardizing platform that can be (re)-configured and scaled with minimal effort to perform many different types of high-throughput or multi-dimensional growth selection experiments. Here, a protocol is presented to provide users of the eVOLVER framework a description for configuring the system to conduct a custom, large-scale continuous growth experiment. Specifically, the protocol guides users on how to program the system to multiplex two selection pressures - temperature and osmolarity - across many eVOLVER vials in order to quantify fitness landscapes of Saccharomyces cerevisiae mutants at fine resolution. We show how the device can be configured both programmatically, through its open-source web-based software, and physically, by arranging fluidic and hardware layouts. The process of physically setting up the device, programming the culture routine, monitoring and interacting with the experiment in real-time over the internet, sampling vials for subsequent offline analysis, and post experiment data analysis are detailed. This should serve as a starting point for researchers across diverse disciplines to apply eVOLVER in the design of their own complex and high-throughput cell growth experiments to study and manipulate biological systems.

Indexed as

SoftwareAutomationCell CycleCell ProliferationCulture TechniquesPhenotypeSaccharomyces cerevisiae

Identifiers

PMID31157778
PMCPMC6738936
OpenAlexW2945641290

What OpenQuestion holds

Textmetadata
LicenceTDM
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.