Emergent dynamics of biological collectives
Abstract/Contents
- Abstract
- Across scales, biological systems frequently operate at the edge of chaos where they can balance competing needs for stability and adaptation. In this critical regime, details and variations at lower levels can propagate and drive behaviors at larger scales. Building understanding of how life emerges from these cross-scale interactions requires us to abstract this collective complexity into intuitive representations. But this is challenged our inability to simultaneously observe all the relevant scales driving the system's behavior. While single measurements may fail to capture all generative details of a system, dynamic trajectories can encode key information about the underlying processes. This thesis develops conceptual and technical approaches to address these challenges using dynamics data at two scales of biology. First, I study ecosystems, where species and the environment interact to create interconnected dynamic cycles. Using computational modeling and experimental data, I identify phenomenological evidence and mechanistic underpinnings by which complexity and its statistical regularity emerge from simple inter-species interactions ranging from microbial crossfeeding to the ocean food chain. Second, I study the behavior and nervous system of the planarian Schmidtea Mediterranea and demonstrate that it achieves remarkably robust memory and signal processing even as it regenerates an entire brain. By developing new high-throughput imaging platforms, analysis pipelines, and trainable neural signaling models, I find that planarians achieve their extreme behavioral robustness through overlapping networks of diffusive and synaptic interactions. The incoherent time and length scales of these different molecular signals create competitive control of firing patterns which is robust to massive structural injuries. This demonstration proposes a new principle by which neurons generate multi-scale activity patterns and opens new opportunities to explore the basis of functional neural dynamics in the planarian's structurally plastic nervous system.
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 | 2022; ©2022 |
| Publication date | 2022; 2022 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Bray, Samuel Roland |
|---|---|
| Degree supervisor | Wang, Bo, (Artificial intelligence scientist) |
| Thesis advisor | Wang, Bo, (Artificial intelligence scientist) |
| Thesis advisor | Linderman, Scott |
| Thesis advisor | Prakash, Manu |
| Degree committee member | Linderman, Scott |
| Degree committee member | Prakash, Manu |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Samuel R. Bray. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2022. |
| Location | https://purl.stanford.edu/pq004hk6757 |
Access conditions
- Copyright
- © 2022 by Samuel Roland Bray
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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