Computational and experimental approaches to understanding mammalian gene regulation with synthetic biology
Abstract/Contents
- Abstract
- Transcriptional regulation is a key pathway cells use to regulate gene expression in response to temporal signaling, and is becoming widely used as a platform for synthetic biology applications. Transcription in mammalian cells is in large part driven by the actions of transcription factors, which consist of DNA-binding domains that recognize and bind to specific genomic locations and effector domains that recruit transcriptional machinery and chromatin regulators to activate or repress target genes. This work uses mathematical modeling and high-throughput experimental approaches to dissect the temporal and combinatorial logic of gene regulation in mammalian cells. First, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations, elaborating on prior models in which individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We analyze classical gene regulatory motifs such as feedforward and autoregulatory loops in the context of duration-dependent signaling, and find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Second, we use a high-throughput approach to dissecting how distinct effector domains within a single transcription factor can be combined to regulate gene expression. We measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. We find that weak and moderate activation domains synergize to drive strong gene expression, while combining strong activators often results in weaker activation. In contrast, weaker repressor domains tend to average each other out, and moderate to strong repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small molecule drug. Altogether, this work helps to advance our understanding of how to build synthetic transcription factors, and how to design and model chromatin regulation-based circuits. Our hope is that these efforts will help advance mammalian synthetic biology as a tool for biological research and a reliable strategy for developing medical therapies.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Mukund, Aditya |
|---|---|
| Degree supervisor | Bintu, Lacramioara |
| Thesis advisor | Bintu, Lacramioara |
| Thesis advisor | Bassik, Michael |
| Thesis advisor | Kundaje, Anshul, 1980- |
| Degree committee member | Bassik, Michael |
| Degree committee member | Kundaje, Anshul, 1980- |
| Associated with | Stanford University, School of Humanities and Sciences |
| Associated with | Stanford University, Biophysics Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Adi Mukund. |
|---|---|
| Note | Submitted to the Biophysics Program. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/qd520mz2708 |
Access conditions
- Copyright
- © 2023 by Aditya Mukund
- License
- This work is licensed under a Creative Commons Attribution Non Commercial Share Alike 3.0 Unported license (CC BY-NC-SA).
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