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dc.contributor.authorKuznetsov, Gleb
dc.date.accessioned2019-05-20T12:21:49Z
dc.date.created2018-05
dc.date.issued2018-05-11
dc.date.submitted2018
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:40049979*
dc.description.abstractEvolution has shown that mutation and selection over billions of years can produce complex molecules and organisms that thrive in a diverse range of environments. As biological engineers, we would like to systematize the navigation of genetic landscapes to find solutions to urgent health and technological needs. In this thesis, I approach the engineering of biological systems from the perspective of design. I illustrate the view of design as an iterative framework of satisfying engineering constraints while discovering and testing degrees of freedom in biological systems. Beginning at the genome scale, I describe a software framework for encoding design rules for recoding genomes and its application to the design of an E. coli strain using only 57 of 64 codons. The genome is being assembled and tested in 50-kilobase segments and we have verified that over 50% of the recoded genome design can functionally complement. Where design rules break down, we leverage DNA synthesis and genome editing to generate targeted diversity and update the design rules. Next, I describe how a model-guided approach that prioritizes mutations to test can augment adaptive laboratory evolution. A 63-codon genomically recoded organism that we previously engineered suffered from impaired fitness and we used our approach to discover a minimal set of high-impact edits that recover 59% of the fitness defect. Finally, I discuss ongoing work to augment design and evolution of proteins by training machine learning models that learn from and guide high-throughput mapping of fitness landscapes. I describe lessons learned in a proof-of-concept study mapping the fitness landscape of the green fluorescent protein and implications for engineering of other proteins. The unifying contribution of this dissertation is a demonstration at multiple scales of how to systematically integrate DNA synthesis, sequencing, high-throughput assays, and computational methods to interrogate biological systems and learn design principles that expand our engineering capabilities.
dc.description.sponsorshipBiophysics
dc.format.mimetypeapplication/pdf
dc.language.isoen
dash.licenseLAA
dc.subjectBiology, Molecular
dc.subjectEngineering, Biomedical
dc.subjectBiology, Bioinformatics
dc.titleMachine-guided design and evolution of biological systems: from the protein to the genome scale
dc.typeThesis or Dissertation
dash.depositing.authorKuznetsov, Gleb
dc.date.available2019-05-20T12:21:49Z
thesis.degree.date2018
thesis.degree.grantorGraduate School of Arts & Sciences
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy
dc.contributor.committeeMemberBaym, Michael
dc.contributor.committeeMemberMirny, Leonid A.
dc.contributor.committeeMemberSpringer, Michael
dc.contributor.committeeMemberHogle, James M.
dc.type.materialtext
thesis.degree.departmentBiophysics
dash.identifier.vireohttp://etds.lib.harvard.edu/gsas/admin/view/1985
dc.description.keywordsbiological sciences;synthetic biology;genome engineering;machine learning;evolution;protein engineering;biodesign
dc.identifier.orcid0000-0001-9317-0639
dash.author.emailgleb.kuznetsov@gmail.com


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