Leal-Alves, Chiara (2025) Engineering Droplet Microfluidic Platforms for Microbial Strain Improvement. PhD thesis, Concordia University.
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Abstract
Advancing microbial strain improvement is essential for industrial biotechnology, enabling organisms with enhanced productivity, stress tolerance, and metabolic efficiency. Traditional improvement methods—both genetic and non-GMO approaches like UV mutagenesis or adaptive laboratory evolution—generate high phenotypic diversity but require the screening of vast microbial libraries. Existing screening tools such as FACS and microtiter assays struggle with cost, resolution, and compatibility, especially for secreted or label-free phenotypes and complex morphologies like filamentous fungi.
This thesis presents a novel microfluidic electrostatic droplet sorting (EDS) platform designed to overcome these limitations. The EDS system encapsulates individual cells in nano- to picoliter droplets, creating isolated microreactors for high-throughput phenotypic screening. Unlike conventional dielectrophoretic sorting, EDS operates at lower voltages, enhancing biocompatibility and versatility, and is better suited to heterogeneous droplet populations and morphologically complex organisms.
The thesis details device design, fabrication, and integration with optical detection systems, supporting both binary and multiplexed sorting. The platform was validated with various industrial microbes, including Clonostachys rosea, Aspergillus oryzae, Trichoderma reesei, and Saccharomyces cerevisiae var. diastaticus, targeting enzymes like chitinase, amylase, and cellulase. Furthermore, a novel method was developed to mimic solid-state fermentation in droplets using colloidal suspensions, enabling screening under industry-relevant conditions.
These integrated EDS platforms address critical screening challenges, offering scalable, low-cost, and high-throughput solutions that support the development of next-generation strains for sustainable biomanufacturing.
| Divisions: | Concordia University > Gina Cody School of Engineering and Computer Science > Electrical and Computer Engineering |
|---|---|
| Item Type: | Thesis (PhD) |
| Authors: | Leal-Alves, Chiara |
| Institution: | Concordia University |
| Degree Name: | Ph. D. |
| Program: | Electrical and Computer Engineering |
| Date: | 13 May 2025 |
| Thesis Supervisor(s): | Shih, Steve |
| ID Code: | 995849 |
| Deposited By: | Ph.D Chiara Leal Alves |
| Deposited On: | 04 Nov 2025 16:12 |
| Last Modified: | 04 Nov 2025 16:12 |
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