Skip navigation
Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp015712m916t
Title: Quorum Sensing in Bacterial Biofilms: Regulating Matrix Production through Communication
Authors: Narla, Avaneesh
Advisors: Wingreen, Ned S.
Department: Physics
Certificate Program: Applications of Computing Program
Class Year: 2017
Abstract: Bacteria grow on surfaces in complex communities known as biofilms. Biofilms are composed of cells embedded in extracellular matrix. Within biofilms, bacteria often communicate, cooperate, and compete within their own species and with other species using Quorum Sensing (QS). QS refers to the process by which bacteria produce, secrete, and subsequently detect small molecules called autoinducers (AIs) to assess the local population density of their species, or of other species. QS is known to regulate the production of extracellular matrix. We investigated the benefit of QS in regulating matrix production to gain access to a nutrient that diffuses from a source far from the biofilm. We employed Agent-Based Modeling (ABM), a simulation framework that allows cells to modify their behavior based on local inputs, e.g. nutrient and AI concentrations. We first determined the optimal fixed strategies (that do not use QS) for simulated pairwise competitions between strains, and identified the conditions that favor matrix production. To understand if QS can provide a competitive advantage, we modified our model to include QS with constitutive AI production. We demonstrated that simple QS-based strategies can be superior to any fixed strategy. However, we found that if AI production is not constitutive but rather depends on nutrient intake, then QS-based strategies fail to provide an advantage. We explain this failure of QS using analytic methods. We derive an expression for the biophysically limited dynamic range of AI concentration detection in nutrient limited environments. This expression implies that for QS to provide an advantage in biofilms, production of AI should be privileged and not limited by overall metabolic rates.
URI: http://arks.princeton.edu/ark:/88435/dsp015712m916t
Type of Material: Princeton University Senior Theses
Language: en_US
Appears in Collections:Physics, 1936-2020

Files in This Item:
File SizeFormat 
anarla_final_thesis.pdf5.25 MBAdobe PDF    Request a copy


Items in Dataspace are protected by copyright, with all rights reserved, unless otherwise indicated.