In nature, microbial consortia are often resilient and adaptable to environmental challenges and perturbations due to their highly coordinated community-level functions and behaviors, enabled by division of labor and intracellular communication. These features make microbial consortia a powerful chassis for synthetic biology and biotechnology innovations. A critical challenge for designing synthetic consortia is to accurately predict the population dynamics of microbial ecosystems, due to the large number of variables involved and the complexity of the underlying biochemical and ecological networks. This is largely due to our limited understanding of how microbial interactions are shaped by environmental nutrients, and how these interspecies interactions scale to affect community function and stability. Despite numerous computational modeling approaches and high-throughput experimental methods devised to address this knowledge gap, challenges remain in integrating high-throughput experimental techniques such as -omics measurements with dynamic models to both provide a mechanistic understanding on communities at the scale of molecular effectors, and offer reliable predictions at an ecological level. In this thesis work, I combine experimental and computational approaches to study synthetic ecosystem assembly and dynamics, and propose a computational framework to integrate experimental data for predicting and manipulating microbial consortia.
The first chapter of this dissertation is an introduction on the background and motivations of this work, in particular on the challenges of predicting community responses. The second chapter details the development of an experimentally-informed modeling approach to study metabolic interactions and interdependency of a synthetic model system of root-associated microbes, which was then used to guide further design of subcommunities with certain community features. The third chapter describes a computer-aided design (CAD) network partitioning tool that distributes community function in an engineered consortium of microbes, with the goal of overcoming the limitations of performing complicated tasks by a single population. The final chapter lays out future directions to combine -omics data, different modeling approaches, and high-throughput experimental techniques such as droplet microfluidics for the study and design of microbial communities, and how we envision these tools to be connected to generate microbial communities of increasing complexity. / 2026-01-11T00:00:00Z
| Identifer | oai:union.ndltd.org:bu.edu/oai:open.bu.edu:2144/47925 |
| Date | 11 January 2024 |
| Creators | Zhang, Jing |
| Contributors | Segrè, Daniel |
| Source Sets | Boston University |
| Language | en_US |
| Detected Language | English |
| Type | Thesis/Dissertation |
Page generated in 0.0136 seconds