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Alexander Garruss

B.S., Management Information Systems, Kansas State University
M.S., Computer Science, University of Kansas
Ph.D., Bioinformatics and Integrative Genomics, Harvard University

I have failed so many times that I am starting to like it – now working hard at failing faster and more often.

Research Areas

Genetics and Genomics, Molecular and Cell Biology

Honors

Alexander Garruss, Ph.D., is an inventor and biotechnology researcher, and most recently served as the Principal Scientist of Artificial Intelligence and Machine Learning (AI/ML) at Sail Biomedicines, a Flagship Pioneering Company. Prior to Sail Biomedicines, Garruss was a Senior Scientist of Machine Learning at Exact Sciences. He returned to the Stowers Institute in Spring 2024. 

Garruss earned a Ph.D. from Harvard University in the Bioinformatics and Integrative Genomics (BIG) program, conducting his research in the laboratory of George Church, Ph.D., Garruss utilized tools from large-scale DNA synthesis, high-throughput DNA sequencing, and advanced AI/ML algorithms toward understanding both protein-DNA interactions and RNA-RNA interactions in living cells.

Prior to Harvard, Garruss was a bioinformatics programmer/analyst for the Stowers Institute conducting studies about regulatory genomics and chromatin biology. He holds a bachelor’s degree in business administration from Kansas State University and a master’s degree in computer science from the University of Kansas.

His academic contributions in computational biology and machine learning total more than 20 peer-reviewed publications and 3,000 citations, appearing in journals such as Cell, Nature Methods, and Nature Genetics. Garruss is an inventor on several patents covering methods for polynucleotide synthesis, enzyme discovery, and the use of ribo-regulators for medical diagnostics.

Garruss has also served as scientific advisor and consultant to several biotechnology organizations. He is an emerging leader in the application of machine learning combined with massively multiplexed experimentation for fundamental scientific and medical breakthroughs.

Featured Publications

Deep representation learning improves prediction of LacI-mediated transcriptional repression

Garruss AS, Collins KM, Church GM. Proc Natl Acad Sci U S A. 2021 Jul 6;118(27):e2022838118.

A deep learning approach to programmable RNA switches

Angenent-Mari NM*, Garruss AS*, Soenksen LR*, Church G, Collins JJ. Nat Commun. 2020 Oct 7;11(1):5057. *Co-first authors.

Riboregulators and methods of use thereof

Soenksen, L., Angenent-Mari, N., Garruss, A.S., Collins, J.J., Church, G.M., Collins, K., Camacho, D. WO2021194580A9. 2021

Engineering an allosteric transcription factor to respond to new ligands

Taylor ND, Garruss AS, Moretti R, Chan S, Arbing MA, Cascio D, Rogers JK, Isaacs FJ, Kosuri S, Baker D, Fields S, Church GM, Raman S. Nat Methods. 2016 Feb;13(2):177-83.

Processive template independent DNA polymerase variants

Griswold, K., Turczyk, B,. Wiegand, D., Church, G.M., Garruss, A.S. US20190360013A1. 2019

Methods and systems for cell-free enzyme discovery and optimization.

Church, G.M., Rogers, J.K., Guell, M., Garruss, A.S. WO2017155945A1. 2017

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