Literature DB >> 24484878

Fluidic and microfluidic tools for quantitative systems biology.

Burak Okumus1, Sadik Yildiz2, Erdal Toprak3.   

Abstract

Understanding genes and their functions is a daunting task due to the level of complexity in biological organisms. For discovering how genotype and phenotype are linked to each other, it is essential to carry out systematic studies with maximum sensitivity and high-throughput. Recent developments in fluid-handling technologies, both at the macro and micro scale, are now allowing us to apply engineering approaches to achieve this goal. With these newly developed tools, it is now possible to identify genetic factors that are responsible for particular phenotypes, perturb and monitor cells at the single-cell level, evaluate cell-to-cell variability, detect very rare phenotypes, and construct faithful in vitro disease models.
Copyright © 2013 Elsevier Ltd. All rights reserved.

Mesh:

Year:  2013        PMID: 24484878     DOI: 10.1016/j.copbio.2013.08.016

Source DB:  PubMed          Journal:  Curr Opin Biotechnol        ISSN: 0958-1669            Impact factor:   9.740


  7 in total

1.  A nanoliter microfluidic serial dilution bioreactor.

Authors:  Guo-Yue Gu; Yi-Wei Lee; Chih-Chung Chiang; Ya-Tang Yang
Journal:  Biomicrofluidics       Date:  2015-08-31       Impact factor: 2.800

2.  Design and Use of a Low Cost, Automated Morbidostat for Adaptive Evolution of Bacteria Under Antibiotic Drug Selection.

Authors:  Po C Liu; Yi T Lee; Chun Y Wang; Ya-Tang Yang
Journal:  J Vis Exp       Date:  2016-09-27       Impact factor: 1.355

3.  Single-cell microscopy of suspension cultures using a microfluidics-assisted cell screening platform.

Authors:  Burak Okumus; Charles J Baker; Juan Carlos Arias-Castro; Ghee Chuan Lai; Emanuele Leoncini; Somenath Bakshi; Scott Luro; Dirk Landgraf; Johan Paulsson
Journal:  Nat Protoc       Date:  2017-12-21       Impact factor: 13.491

4.  MMHelper: An automated framework for the analysis of microscopy images acquired with the mother machine.

Authors:  Ashley Smith; Jeremy Metz; Stefano Pagliara
Journal:  Sci Rep       Date:  2019-07-12       Impact factor: 4.379

5.  Quantifying the Determinants of Evolutionary Dynamics Leading to Drug Resistance.

Authors:  Guillaume Chevereau; Marta Dravecká; Tugce Batur; Aysegul Guvenek; Dilay Hazal Ayhan; Erdal Toprak; Tobias Bollenbach
Journal:  PLoS Biol       Date:  2015-11-18       Impact factor: 8.029

6.  Image-Based Single Cell Profiling: High-Throughput Processing of Mother Machine Experiments.

Authors:  Christian Carsten Sachs; Alexander Grünberger; Stefan Helfrich; Christopher Probst; Wolfgang Wiechert; Dietrich Kohlheyer; Katharina Nöh
Journal:  PLoS One       Date:  2016-09-23       Impact factor: 3.240

7.  Rationally designing antisense therapy to keep up with evolving bacterial resistance.

Authors:  Seyfullah Kotil; Eric Jakobsson
Journal:  PLoS One       Date:  2019-01-15       Impact factor: 3.240

  7 in total

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