Author

Michael Baym

Department of Biomedical Informatics and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts 02115, USA; Harvard Data Science InitPrimary ORCID profile (0000-0003-1303-5598 · 15 papers)Unclaimed ORCID profile · follow to get notified of new preprints

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Michael BaymiD15 papers · Harvard University

Biology Disciplines

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Evolutionary Biology· 1 Genomics· 1 Synthetic Biology· 1

Signature Assays, Organisms & Methods

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Approach:experimental assay· 3 Organism:bacteria (other)· 1 Organism:E. coli· 1 Organism:mouse· 1 Research Focus:variant calling· 1 Organism:virus· 1 Assay:whole-genome sequencing· 1

Frequent Co-Authors

A. Limdi· 1 shared C. Herren· 1 shared David T Riglar· 1 shared Georg K Gerber· 1 shared Hattie Chung· 1 shared Jeffrey C Way· 1 shared Jonathan W Kotula· 1 shared Matthew J Niederhuber· 1 shared

Activity by Year

2015-2022 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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1 shared paperEvolutionary Biology

A. Limdi

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3 papers

Recent·Most cited

Parallel evolution of mutational fitness effects over 50,000 generations

A. Limdi, S. V. Owen, C. Herren, R. E. Lenski, Michael Baym

The study examines how mutation fitness effects change over 50,000 generations in E. coli, finding parallel evolution of mutation costs and increased essentiality without altering the overall fitness landscape distribution.

bioRxiv · May 17, 2022Evolutionary Biology

Long-term monitoring of inflammation in the mammalian gut using programmable commensal bacteria

David T Riglar, Michael Baym, S Jordan Kerns, Matthew J Niederhuber, Roderick T Bronson, Jonathan W Kotula, Georg K Gerber, Jeffrey C Way, Pamela A Silver

Engineered bacteria record an inflammatory response in an IBD mouse model and are genetically stable during long-term growth in the mouse gut.

bioRxiv · Sep 14, 2016 · PreprintSynthetic Biology

Inexpensive Multiplexed Library Preparation for Megabase-Sized Genomes

Michael Baym, Sergey Kryazhimskiy, Tami D. Lieberman, Hattie Chung, Michael M. Desai, Roy Kishony

This paper presents a cost-effective method for preparing genomic libraries for sequencing, reducing costs to $8 per sample and enabling high-throughput sequencing of microbial and viral genomes.

bioRxiv · Jan 21, 2015Genomics