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Revealing Regulatory Network Organization Through Single-Cell Perturbation Profiling and Maximum Entropy Models


Jiang, Jialong (2024) Revealing Regulatory Network Organization Through Single-Cell Perturbation Profiling and Maximum Entropy Models. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/5zta-9818.


Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. In this thesis, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

Item Type:Thesis (Dissertation (Ph.D.))
Subject Keywords:Systems biology; Gene regulatory networks; Single-cell sequencing; Probabilistic graphical models
Degree Grantor:California Institute of Technology
Division:Biology and Biological Engineering
Major Option:Systems Biology
Minor Option:Applied And Computational Mathematics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Thomson, Matthew
Thesis Committee:
  • Elowitz, Michael B. (chair)
  • Phillips, Robert B.
  • Pachter, Lior S.
  • Thomson, Matthew
Defense Date:30 May 2024
Non-Caltech Author Email:jialongjiang2017 (AT)
Funding AgencyGrant Number
Heritage Medical Research InstituteUNSPECIFIED
Charles TrimbleUNSPECIFIED
Shurl and Kay Curci FoundationUNSPECIFIED
Merkin Institute for Translational ResearchUNSPECIFIED
Record Number:CaltechTHESIS:06032024-182223499
Persistent URL:
Related URLs:
URLURL TypeDescription adapted for ch.1-ch.6 DocumentUNSPECIFIED DocumentUNSPECIFIED
Jiang, Jialong0000-0001-8560-8397
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:16486
Deposited By: Jialong Jiang
Deposited On:06 Jun 2024 22:02
Last Modified:14 Jun 2024 21:30

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