Quantifying the optimal strategy of population control of quorum sensing network in Escherichia coli

Abstract

Biological functions of bacteria can be regulated by monitoring their own population density induced by the quorum sensing system. However, quantitative insight into the system’s dynamics and regulatory mechanism remain challenging. Here, we construct a comprehensive mathematical model of the synthetic quorum sensing circuit that controls population density in Escherichia coli. Simulations agree well with experimental results obtained under different ribosome-binding site (RBS) efficiencies. We present a quantitative description of the component dynamics and show how the components respond to isopropyl-β-D-1-thiogalactopyranoside (IPTG) induction. The optimal IPTG-induction range for efficiently controlling population density is quantified. The controllable area of population density by acyl-homoserine lactone (AHL) permeability is quantified as well, indicating that high AHL permeability should be treated with a high dose of IPTG, while low AHL permeability should be induced with low dose for efficiently controlling. Unexpectedly, an oscillatory behavior of the growth curve is observed with proper RBS-binding strengths and the oscillation is greatly restricted by the bacterial death induced by toxic metabolic by-products. Moreover, we identify that the mechanism underlying the emergence of oscillation is determined by the negative feedback loop structure within the signaling. Bifurcation analysis and landscape theory are further employed to study the stochastic dynamic and global stability of the system, revealing two faces of toxic metabolic by-products in controlling oscillatory behavior. Overall, our study presents a quantitative basis for understanding and new insights into the control mechanism of quorum sensing system, providing possible clues to guide the development of more rational control strategy.

Continuous, Non-Destructive Detection of Microorganism Growth at Buried Interfaces with Vascularized Polymers

“…The governing equations used were adapted from previous work developing quantitative models for bacterial substrate utilization 52 and the production of low-molecularweight compounds. 51 The results from finite element simulation for both nutrients and a bacterial compound are shown in Figure 4B. The resulting curves for both matched very closely with the experimental results for HPLC analysis of fluid from E.coli-colonized surfaces shown in Figure 3C.…”

Caspase-1 and Gasdermin D Afford the Optimal Targets with Distinct Switching Strategies in NLRP1b Inflammasome-Induced Cell Death

“…All the corresponding reactions in Figure S2 and reaction rates in model are elaborated in Table S1. Using the wellestablished kinetic approaches [32,33], these biochemical processes can be subsequently translated into a set of ODEs (ordinary differential equations) to describe the time evolution of amount for each molecular species (Table S2). Initial amount of the molecular species is estimated from experimental studies [34][35][36].…”

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“…Through providing a more physical description of the stochastic dynamic and global stability of the biological systems, recently developed potential landscape theory is a powerful approach for identifying new functional states or unknown regulatory mechanisms [ 33 , 37 ]. Most recently, a new cell-aging fate induced by overexpression of the lysine deacetylase Sir2 was found by using this approach [ 51 ].…”

Intertemporal trade-off between population growth rate and carrying capacity during public good production

“…Over the years, several studies have captured the important role of QS and non-QS regulatory mechanisms in maximizing the benefits collected from public good molecules. Several studies have captured the importance of quorum sensing to tune public good production with cell density ( Bruger and Waters, 2016 , 2018 ; Heilmann et al., 2015 ; Li et al., 2021 ; Pai et al., 2012 ; Schluter et al., 2016 ; Schuster et al., 2017 ; Brockhurst et al., 2008 ; Kümmerli and Brown, 2010 ). Prominent examples include QS-mediated tuning of public good production to increase population fitness in Vibrio harveyi ( Bruger and Waters, 2016 ), or robustness of quorum sensing pathway itself at all initial densities ensuring population survival in long term ( Pai et al., 2012 ).…”