Towards causality in gene regulatory network inference
Understanding the coordination of biomolecules that underlies gene regulation is key to gaining mechanistic insights into cellular functions, phenotypes, and diseases. Advances in single-cell technologies promise to unveil mechanisms of gene regulation at unprecedented resolution by enabling measure...
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| Natura: | Tesi |
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Massachusetts Institute of Technology
2023
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| Accesso online: | https://hdl.handle.net/1721.1/151203 |
| _version_ | 1826201849268535296 |
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| author | Wu, Alexander Po-Yen |
| author2 | Berger, Bonnie A. |
| author_facet | Berger, Bonnie A. Wu, Alexander Po-Yen |
| author_sort | Wu, Alexander Po-Yen |
| collection | MIT |
| description | Understanding the coordination of biomolecules that underlies gene regulation is key to gaining mechanistic insights into cellular functions, phenotypes, and diseases. Advances in single-cell technologies promise to unveil mechanisms of gene regulation at unprecedented resolution by enabling measurements of genomic and/or epigenetic features for individual cells. However, unlocking insights from single-cell data requires algorithmic innovations.
This thesis introduces a series of methods for uncovering gene regulatory relationships underlying cellular identity and function from single-cell data. Firstly, we present a framework for enhancing the detection of statistical associations in small sample size settings for gene regulatory network inference. We then describe the use of single-cell genetic perturbation screens for determining the causal roles of critical regulatory complexes, focusing specifically on its applications for revealing mechanistic insights about the mammalian SWI/SNF family of chromatin remodeling complexes.
To bridge the gap between methods that identify statistical associations from observational data and those that infer causal relationships using interventions, we also introduce a new category of techniques that extends the econometric concept of Granger causality to complex graph-based dynamical systems, such as those found in single-cell trajectories. In particular, we describe a graph neural network-based generalization of Granger causality for single-cell multimodal data that enables the detection of noncoding genomic loci implicated in the regulation of specific genes. We then demonstrate how we use this approach to link genetic variants to gene dysregulation in disease, focusing on its applications to schizophrenia etiology. Lastly, we present an extension of this graph-based Granger causal framework that leverages RNA velocity dynamics for causal gene regulatory network inference and enables inquiries into the role of temporal control in gene regulatory function and disease. |
| first_indexed | 2024-09-23T11:57:48Z |
| format | Thesis |
| id | mit-1721.1/151203 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T11:57:48Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1512032023-08-01T03:49:47Z Towards causality in gene regulatory network inference Wu, Alexander Po-Yen Berger, Bonnie A. Massachusetts Institute of Technology. Computational and Systems Biology Program Understanding the coordination of biomolecules that underlies gene regulation is key to gaining mechanistic insights into cellular functions, phenotypes, and diseases. Advances in single-cell technologies promise to unveil mechanisms of gene regulation at unprecedented resolution by enabling measurements of genomic and/or epigenetic features for individual cells. However, unlocking insights from single-cell data requires algorithmic innovations. This thesis introduces a series of methods for uncovering gene regulatory relationships underlying cellular identity and function from single-cell data. Firstly, we present a framework for enhancing the detection of statistical associations in small sample size settings for gene regulatory network inference. We then describe the use of single-cell genetic perturbation screens for determining the causal roles of critical regulatory complexes, focusing specifically on its applications for revealing mechanistic insights about the mammalian SWI/SNF family of chromatin remodeling complexes. To bridge the gap between methods that identify statistical associations from observational data and those that infer causal relationships using interventions, we also introduce a new category of techniques that extends the econometric concept of Granger causality to complex graph-based dynamical systems, such as those found in single-cell trajectories. In particular, we describe a graph neural network-based generalization of Granger causality for single-cell multimodal data that enables the detection of noncoding genomic loci implicated in the regulation of specific genes. We then demonstrate how we use this approach to link genetic variants to gene dysregulation in disease, focusing on its applications to schizophrenia etiology. Lastly, we present an extension of this graph-based Granger causal framework that leverages RNA velocity dynamics for causal gene regulatory network inference and enables inquiries into the role of temporal control in gene regulatory function and disease. Ph.D. 2023-07-31T19:22:21Z 2023-07-31T19:22:21Z 2023-06 2023-07-13T20:23:25.645Z Thesis https://hdl.handle.net/1721.1/151203 In Copyright - Educational Use Permitted Copyright retained by author(s) https://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Wu, Alexander Po-Yen Towards causality in gene regulatory network inference |
| title | Towards causality in gene regulatory network inference |
| title_full | Towards causality in gene regulatory network inference |
| title_fullStr | Towards causality in gene regulatory network inference |
| title_full_unstemmed | Towards causality in gene regulatory network inference |
| title_short | Towards causality in gene regulatory network inference |
| title_sort | towards causality in gene regulatory network inference |
| url | https://hdl.handle.net/1721.1/151203 |
| work_keys_str_mv | AT wualexanderpoyen towardscausalityingeneregulatorynetworkinference |