Fantastic beasts & ways to probe them : non-linear and collective dynamics in biological systems at population, organism, & cellular scales
Abstract/Contents
- Abstract
- The animal kingdom harbours diverse examples of creatures with intriguing behaviours or phenotypes arising from non-linear, emergent, and collective phenomena. In this dissertation, I combine a variety of techniques to develop integrated experimental and analytical frameworks to highlight the role of complexity in the physiology and ecology of some fascinating organisms. At the population level, the prediction and management of mosquito-borne disease outbreaks highlights a critical need for large-scale, high-resolution data on spatio-temporal ecological dynamics, together with the fundamental limiting challenge of collecting reliable, high-throughput observations from the field. I developed novel engineering tools to probe the ecological dynamics of mosquitoes, using basic mobile phones to record wingbeat sounds for species identification and spatio-temporal mapping. I collected the largest database of sounds from various mosquito species and developed estimation algorithms to classify them, subsequently carrying out field trials to detect circadian activity rhythms, hotspots, and species segregation. With this proof-of-concept, I will outline a citizen science platform for crowdsourcing acoustic data on insects, with applications in monitoring biodiversity, invasive species, and disease outbreaks. At the scale of individual organisms, I studied water-lily beetles to show how dynamical phenomena shape behavioural adaptations through interactions within a habitat. These insects demonstrate flapping-wing flight while remaining attached to the surface of water, when moving between the floating lily pads that they eat in their environmental niche on pond surfaces. Using experimental biomechanics combined with fluid and non-linear dynamical analyses, I showed how surface tension alters this interfacial flight mode in comparison to conventional airborne flight, highlighting the emergence of chaos uncertainty at timescales that are too short to be under active neural control. At the cellular and gene level, I studied placozoans -- basal marine invertebrates comprising only a few cell types arranged in flat layers -- which provide an excellent model system for investigating the evolutionary and developmental origins of animal multicellularity. I sequenced the transcriptome of placozoans in various phenotypic states, to uncover how gene expression shapes their unique and unexplored physiology. Taken together, these phenomena highlight the role of dynamics and multi-scale hierarchical organization in biological systems, with diverse approaches at each scale to understanding their complexities.
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 | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Vaidehi Narayanan, Haripriya |
|---|---|
| Degree supervisor | Dabiri, John O. (John Oluseun) |
| Degree supervisor | Prakash, Manu |
| Thesis advisor | Dabiri, John O. (John Oluseun) |
| Thesis advisor | Prakash, Manu |
| Thesis advisor | LaBeaud, Desiree |
| Thesis advisor | Mordecai, Erin |
| Thesis advisor | Schneider, David (David Samuel) |
| Degree committee member | LaBeaud, Desiree |
| Degree committee member | Mordecai, Erin |
| Degree committee member | Schneider, David (David Samuel) |
| Associated with | Stanford University, Department of Mechanical Engineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Haripriya Vaidehi Narayanan. |
|---|---|
| Note | Submitted to the Department of Mechanical Engineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Haripriya Vaidehi Narayanan
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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