Towards synthetic ecology : genetically programmable 4-module population control system in yeast

Communities of microorganisms are found nearly ubiquitously on earth. They survive and proliferate through interactions within and between microbial species, which are mediated by the exchange of small signaling modules. Understanding how they regulate the interactions is both crucial and challenging, with applications including industrial biotechnology, human health and environmental sustainability. In microbial ecology, researchers have been trying to culture pure and mixed species in different conditions to elucidate the rules behind the interactions. However, the studies have been complicated by multiple variables at both the genotype and phenotype levels. To address these challenges, I demonstrate a synthetic ecological system as a proof of principle to observe microbial population level behaviors. Using a formalized design process and engineering principles, I design and construct a synthetic multi-module ecological system for population homeostasis. The synthetic ecological system consists of four functionally distinct modules - quorum sensing, high threshold killing, low threshold killing, and intermediate rescuing modules. The system is able to maintain the yeast population within a programmable range in liquid culture. However, when the same system is studied in solid medium, heterogeneity in growth rate and population size is observed. To further study the heterogeneity issue in solid medium, I develop a cell deposition platform to evaluate sub-population level or even single-cell level behavior. With a commercial Nano eNabler machine, cells with pre-defined patterns are deposited on agarose surface. This technique can be used to study microbial communities in a spatially distributed fashion.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Biological Engineering.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2014

Author dc:creator

• Sun, Jingjing, Ph. D. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Ron Weiss.

• Biological Engineering.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/90678

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/90678