Evolution, Evolvability, Expression and Engineering

Thumbnail Image
Download
Name

vaishnav-edv-phd-bio-2022-thesis.pdf

Description
Thesis PDF
Size

22.02 MB

Format

Adobe PDF

Checksum (MD5)

51a9a587e9388a8fff283921091a928f

Thumbnail Image
Download
Name

Disclaimer Sheet-pagination.pdf

Description
Supplementary file
Size

68.18 KB

Format

Adobe PDF

Checksum (MD5)

047314917dc78e1fa3405312ddd08af1

Author(s)
Vaishnav, Eeshit Dhaval
Advisor(s)
Regev, Aviv
Date Issued
September 2022
Publisher
Massachusetts Institute of Technology
Abstract
This thesis describes how to build machines (Engineering) that answer questions about: (a) Evolution & Evolvability and (b) Expression. In the first part of this thesis, I present a framework for understanding and engineering biological sequences, and solving sequence→function problems by building ‘Complete Fitness Landscapes’ in sequence space. This framework for measuring, modelling and designing biological sequences is built around the idea of learning an ‘oracle’ (typically a deep neural network model that takes a sequence as input and predicts its corresponding function) to traverse these ‘Complete Fitness Landscapes’. Here we develop a (promoter sequence)→(gene expression) oracle and use it with our framework to design sequences that demonstrate expression beyond the range of naturally observed sequences. We also show how our framework can be used to detect signatures of selection on a sequence, and to characterize robustness and evolvability. The second part of this thesis describes two frameworks for inferring from single-cell and spatial gene expression measurements: ATLAS (A Tool for Learning from Atlas-scale Single-cell datasets) and insi2vec (a framework for inferring from spatial multi-omic and imaging measurements).
MIT Department
Massachusetts Institute of Technology. Department of Biology
Terms of Use
In Copyright - Educational Use Permitted
Copyright MIT
Persistent DSpace Link
PrivacyPermissionsAccessibilityContact us
Notify us about copyright concerns.