Exploiting noise, non-linearity, and feedback to differentially control multiple different cells using a single optogenetic input
| dc.contributor.author | May, Michael P., author | |
| dc.contributor.author | Munsky, Brian, advisor | |
| dc.contributor.author | Stasevich, Tim, advisor | |
| dc.contributor.author | Krapf, Diego, committee member | |
| dc.contributor.author | Shipman, Patrick, committee member | |
| dc.date.accessioned | 2024-01-01T11:25:26Z | |
| dc.date.available | 2024-01-01T11:25:26Z | |
| dc.date.issued | 2023 | |
| dc.description.abstract | Motivated by Maxwells-Demon, we propose and solve a cellular control problem in which the exploitation of stochastic noise can break symmetry between two cells and allow for specific control of multiple cells using a single input signal. We find that a new type of noise-exploiting controllers are effective and can remain effective despite coarse approximations to the model's scale or extrinsic noise in key model parameters, and that these controllers can retain performance under substantial observer-actuator time delays. We also demonstrate how SIMO controllers could drive two-cell systems to follow different trajectories with different phases and frequencies by using a noise-exploiting controller. Together, these findings suggest that noise-exploiting control should be possible even in the case where models are approximate, and where parameters are uncertain. Having demonstrated the potential of noise-enhanced feedback control through computational modeling, we have also begun the next steps toward automating microscopy to implement this potential in experimental practice. Specifically, we demonstrate a new integrated pipeline to automate the image collection including: (i) quickly search in two-dimensions to find fields of view with cells of desired phenotypes, (ii) targeted collection of three-dimensional image data for these chosen fields of view, and (iii) streamlined processing of the collected images for rapid segmentation, spot detection and tracking, and cell/spot phenotype quantification. | |
| dc.format.medium | born digital | |
| dc.format.medium | doctoral dissertations | |
| dc.identifier | May_colostate_0053A_18155.pdf | |
| dc.identifier.uri | https://hdl.handle.net/10217/237475 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation.ispartof | 2020- | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject | stochastic | |
| dc.subject | gene regulation | |
| dc.title | Exploiting noise, non-linearity, and feedback to differentially control multiple different cells using a single optogenetic input | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Biomedical Engineering | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Doctoral | |
| thesis.degree.name | Doctor of Philosophy (Ph.D.) |
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