Author

Diveena Becker

Ginkgo Bioworks, Inc. (United States)

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 1 Synthetic Biology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Approach:deep learning· 1 Organism:E. coli· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1 Organism:yeast· 1

Frequent Co-Authors

Joshua A. UrrutiaiD· 3 shared Amin Espah BorujeniiD· 2 shared Christopher A. VoigtiD· 2 shared Enoch YeungiD· 2 shared George Zheng· 2 shared Katie J. ClowersiD· 2 shared Mark Weston· 2 shared Matthew W. VaughniD· 2 shared

Activity by Year

2021-2023 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersSystems Biology

Joshua A. UrrutiaORCID

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Author profile →

3 papers

Recent·Most cited

Disentangling gene expression burden identifies generalizable phenotypes induced by synthetic gene networks

Aqib Hasnain, Amin Espah Borujeni, Yongjin Park, Diveena Becker, Paul Maschhoff, Joshua A. Urrutia, Linus Rydell, Shara Balakrishnan, Yuval Dorfan, Christopher A. Voigt, Enoch Yeung

The paper identifies synthetic gene network-induced phenotypes by analyzing gene expression in E. coli and validates them through antibiotic resistance experiments.

bioRxiv · Jun 29, 2023 · PreprintSystems Biology

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

Bree Cummins, Robert C. Moseley, Anastasia Deckard, Mark Weston, George Zheng, Daniel Bryce, Joshua Nowak, Marcio Gameiro, Tomáš Gedeon, Konstantin Mischaikow, Jacob Beal, Tessa Johnson, Matthew W. Vaughn, Niall I. Gaffney, Shweta Gopaulakrishnan, , Steven B. Haase

The paper develops a computational method to predict synthetic circuit robustness across growth conditions and validates it with experimental results in budding yeast.

bioRxiv · Jun 13, 2022 · PreprintSynthetic Biology

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Mohammed Eslami, Amin Espah Borujeni, Hamed Eramian, Mark Weston, George Zheng, Joshua A. Urrutia, Carolyn Corbet, Diveena Becker, Paul Maschhoff, Katie J. Clowers, Alexander W. Cristofaro, Hamid Doost Hosseini, David Benjamin Gordon, Yuval Dorfan, Jedediah M. Singer, , Enoch Yeung

The paper introduces a machine learning model to predict whole-cell transcriptional responses to combinations of perturbations using data from single inducers.

Bioinformatics · Sep 22, 2021Peer-reviewedBioinformatics