skip to main content
DOE PAGES title logo U.S. Department of Energy
Office of Scientific and Technical Information

This content will become publicly available on June 1, 2020

Title: Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning

Abstract

Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore sizemore » and distribution with changes in the genotype.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [2]; ORCiD logo [1];  [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. ORNL
  2. University of California, San Diego, Scripps Institution of Oceanography
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1550758
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
npj Computational Materials
Additional Journal Information:
Journal Volume: 5; Journal Issue: 1
Country of Publication:
United States
Language:
English

Citation Formats

Trofimov, Artem, Pawlicki, Alison A., Borodinov, Nikolay, Mandal, Shovon, Mathews, Teresa J., Hildebrand, Mark, Ziatdinov, Maxim A., Hausladen, Katherine, Urbanowicz, Paulina, Steed, Chad A., Ievlev, Anton, Belianinov, Alex, Michener, Josh, Vasudevan, Rama K., and Ovchinnikova, Olga S. Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning. United States: N. p., 2019. Web. doi:10.1038/s41524-019-0202-3.
Trofimov, Artem, Pawlicki, Alison A., Borodinov, Nikolay, Mandal, Shovon, Mathews, Teresa J., Hildebrand, Mark, Ziatdinov, Maxim A., Hausladen, Katherine, Urbanowicz, Paulina, Steed, Chad A., Ievlev, Anton, Belianinov, Alex, Michener, Josh, Vasudevan, Rama K., & Ovchinnikova, Olga S. Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning. United States. doi:10.1038/s41524-019-0202-3.
Trofimov, Artem, Pawlicki, Alison A., Borodinov, Nikolay, Mandal, Shovon, Mathews, Teresa J., Hildebrand, Mark, Ziatdinov, Maxim A., Hausladen, Katherine, Urbanowicz, Paulina, Steed, Chad A., Ievlev, Anton, Belianinov, Alex, Michener, Josh, Vasudevan, Rama K., and Ovchinnikova, Olga S. Sat . "Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning". United States. doi:10.1038/s41524-019-0202-3.
@article{osti_1550758,
title = {Deep data analytics for genetic engineering of diatoms linking genotype to phenotype via machine learning},
author = {Trofimov, Artem and Pawlicki, Alison A. and Borodinov, Nikolay and Mandal, Shovon and Mathews, Teresa J. and Hildebrand, Mark and Ziatdinov, Maxim A. and Hausladen, Katherine and Urbanowicz, Paulina and Steed, Chad A. and Ievlev, Anton and Belianinov, Alex and Michener, Josh and Vasudevan, Rama K. and Ovchinnikova, Olga S.},
abstractNote = {Genome engineering for materials synthesis is a promising avenue for manufacturing materials with unique properties under ambient conditions. Biomineralization in diatoms, unicellular algae that use silica to construct micron-scale cell walls with nanoscale features, is an attractive candidate for functional synthesis of materials for applications including photonics, sensing, filtration, and drug delivery. Therefore, controllably modifying diatom structure through targeted genetic modifications for these applications is a very promising field. In this work, we used gene knockdown in Thalassiosira pseudonana diatoms to create modified strains with changes to structural morphology and linked genotype to phenotype using supervised machine learning. An artificial neural network (NN) was developed to distinguish wild and modified diatoms based on the SEM images of frustules exhibiting phenotypic changes caused by a specific protein (Thaps3_21880), resulting in 94% detection accuracy. Class activation maps visualized physical changes that allowed the NNs to separate diatom strains, subsequently establishing a specific gene that controls pores. A further NN was created to batch process image data, automatically recognize pores, and extract pore-related parameters. Class interrelationship of the extracted paraments was visualized using a multivariate data visualization tool, called CrossVis, and allowed to directly link changes in morphological diatom phenotype of pore size and distribution with changes in the genotype.},
doi = {10.1038/s41524-019-0202-3},
journal = {npj Computational Materials},
number = 1,
volume = 5,
place = {United States},
year = {2019},
month = {6}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on June 1, 2020
Publisher's Version of Record

Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

Save / Share:

Works referenced in this record:

Prospects of Manipulating Diatom Silica Nanostructure
journal, January 2005

  • Hildebrand, Mark
  • Journal of Nanoscience and Nanotechnology, Vol. 5, Issue 1, p. 146-157
  • DOI: 10.1166/jnn.2005.013