Ying Zhang


Dr. Ying Zhang obtained undergraduate training in Computer Sciences, graduate training in Computational Biology, and postdoc trainings in Microbial Diversity and Molecular Ecology. These diverse experiences have provided her with unique perspectives in solving problems cross disciplinary boundaries. She is interested in using metabolic modeling, (Meta)omics, and other data-intensive approaches in answering research questions in bioengineering and environmental microbiology. Her research group develops open-source software and models for the simulation of biological systems to achieve mechanistic understandings at molecular, organismal, and ecosystems scales. Recent applications include genome-scale modeling of non-model bacteria and archaea, genomics and transcriptomics of marine protists, metagenomics of free-living and host-associated microbiomes, and taxonomic classification of sequencing reads using deep learning neural networks.


Dr. Zhang’s research program takes an interdisciplinary approach into studying the metabolism and evolution of microorganisms. Her research has broad applications in decoding the genotype-to phenotype connections, predicting metabolic engineering design strategies, assessing ecosystem health, and estimating organismal responses to environmental anomaly. Some recent projects are described below.

Model-guided bioengineering designs for bio-based chemical production.
The bio-based production of organic chemicals from metabolically engineered microbes provides a sustainable alternative to fossil-based production in the face of today’s climate challenges. To identify optimized bioengineering strategies, her group is currently developing computational tools for the model-guided metabolic engineering designs. They established models for hyperthermophilic bacteria and archaea and are currently implementing new approaches for the bio-based production of economically important chemicals, such as ethanol.

Building deep learning tools for the rapid characterization of microbial community.
The rapid development of deep learning techniques has inspired new applications that leverage the massive molecular data collected from a variety of environmental samples. Here they demonstrated the application of deep learning in metagenomics, a technique frequently used for genome-wide profiling of microbiomes. Their recent release of a deep learning model, DL-TODA, supports the rapid taxonomic classification of large-scale metagenomic reads with high accuracy.

Diversity, function, and dynamics of microbiomes in aquatic and sedimentary environments.
Microbes are among the most abundant organisms on Earth and play important roles in primary production, respiration, and nutrient cycling. Besides their contributions to global biogeochemical cycles, microbes respond very quickly to environmental changes. Understanding of the genotype-to-phenotype association of microbes can provide significant insights into how they respond to environment stress and enable the identification of specific metabolic interactions. The group’s research led to the molecular characterization of host-associated microbiomes and marine protists using omics approaches. Examples of recent applications included characterizing metabolic versatility of marine protists in oxygen-depleted environments and identifying the metabolism and evolution of marine invertebrate-associated bacteria using community sequencing and metagenomics.


  • Postdoc, Woods Hole Oceanographic Institution, 2011-2013
  • Ph.D., Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, 2011
  • B.Sc., Beijing Normal University, Beijing, China, 2004

Selected Publications

ORCID: https://orcid.org/0000-0001-8759-0194

Esser SP, Rahlff J, Zhao W, Predl M, Plewka J, Sures K, Wimmer F, Lee J, Adam PS, McGonigle J, Turzynski V, Banas I, Schwank K, Krupovic M, Bornemann TLV, Figueroa-Gonzalez PA, Jarett J, Rattei T, Amano Y, Blaby IK, Cheng J, Brazelton WJ, Beisel CL, Woyke T, Zhang Y, and Probst AJ. (2023) A predicted CRISPR-mediated symbiosis between uncultivated archaea. Nature Microbiology, Under review.

Vailionis J, Zhao W, Zhang K., Rodionov D., Lipscomb GL, Tanwee TNN, O’Quinn HC, Kelly RM, Adams MWW, and Zhang Y. (2023). Optimizing strategies for bio-based ethanol production using genome-scale metabolic modeling of the hyperthermophilic archaeon, Pyrococcus furiosus. Applied and Environmental Microbiology, Accepted.

Lipscomb GL, Crowley AT, Nguyen DMN, Keller MW, Vailionis J, Zhang K, Zhang Y, Kelly RM, and Adams, MWW. (2023) Manipulating Fermentation Pathways in the Hyperthermophilic Archaeon Pyrococcus furious for Ethanol Production up to 95°C Driven by Carbon Monoxide Oxidation. Applied and Environmental Microbiology, e00012-23. doi: 10.1128/aem.00012-23.

Cres CM, Tritt A, Bouchard KE, Zhang Y. (2023) DL-TODA: A Deep Learning Tool for Omics Data Analysis. Biomolecules, 13(4):585. doi: 10.3390/biom13040585.

Powers C, Gomaa F, Billings EB, Utter DR, Beaudoin DJ, Edgcomb VP, Hansel CM, Wankel SD, Filipsson HL, Zhang Y, Bernhard JM. (2022) Two canonically aerobic foraminifera express distinct peroxisomal and mitochondrial metabolisms. Frontiers in Marine Science, doi: 10.3389/fmars.2022.1010319.

Pimentel ZT, Thibodeau PS, Song B, Zhang Y. (2022) A Mollicutes Metagenome-Assembled Genome from the Gut of the Pteropod Limacina rangii. Microbiology, doi: 10.1128/mra.00752-22.

Crosby JR, Laemthong T, Bing RG, Zhang K, Tanwee TNN, Lipscomb GL, Rodionov DA, Zhang Y, Adams MWW, Kelly RM. (2022) Biochemical and Regulatory Analyses of Xylanolytic Regulons in Caldicellulosiruptor bescii Reveal Genus-Wide Features of Hemicellulose Utilization. Appl Environ Microbiol. 88(21):e0130222. doi: 10.1128/aem.01302-22.

Liu F, Meamardoost S, Gunawan R, Komiyama T, Mewes C, Zhang Y, Hwang E, Wang L. (2022) Deep learning for neural decoding in motor cortex. Journal of Neural Engineering. 19(5). doi: 10.1088/1741-2552/ac8fb5.

Dufault-Thompson K, Nie C, Jian H, Wang F, and Zhang Y. (2022) Reconstruction and analysis of thermodynamically constrained metabolic models reveal mechanisms of metabolic remodeling under temperature perturbations of a deep-sea bacterium. mSystems, DOI: https://doi.org/10.1128/msystems.00588-22.

Hwang Y, Schulze-Makuch D, Arens FL, Saenz JS, Adam PS, Sager C, Bornemann TLV, Zhao W, Zhang Y, Airo A, Schloter M, Probst AJ. (2021) Leave no stone unturned: individually adapted xerotolerant Thaumarchaeota sheltered below the boulders of the Atacama Desert hyperarid core. Microbiome 9, 234. https://doi.org/10.1186/s40168-021-01177-9