Revision as of 02:35, 18 October 2018

Quorum Activation Model

Introduction

At first, we relied on experts and literature to guide the design of our plasmids for modifying quorum sensing (QS). However, we quickly realized that having a model would help us better understand our system and predict which synthetic edits would best decrease bimodality. We then developed a mass action kinetics model of the interactions between the lsr operon, AI-2, and our Synthetic Quorum Sensing Plasmid (pSQS) system to predict quorum activation. Using this model to simulate a population cell-by-cell in solution, we worked to establish model parameters where none existed in literature. From here, we inserted circuit-altering genes from our Synthetic Quorum Sensing (sQS) related parts into the model to determine how different combinations of genes will affect quorum activation and bimodality. These results provide a platform that future projects can use to effectively engineer optimal activation dynamics and more complex devices using quorum sensing.

Background

Quorum Sensing and the lsr Operon

Quorum sensing (QS) is a system bacteria used to coordinate population based behaviors, such as the release of virulence factors and the formation of biofilms (Zargar et al. 2016). These types of behaviors are called quorum responses, and are only beneficial to a cell if others around it are engaging in the same behavior. For example, a single cell will not accomplish anything, but a population of cells all acting together can make an effective biofilm. Quorum responses are controlled by a class of molecules called autoinducers. Bacteria produce these molecules at low levels and export them into their environment until a threshold extracellular concentration reached. This concentration indicates that the population density is high enough for a quorum response to be appropriate. At this point, the autoinducer importers bring the molecules into the cell where they activate a set of genes associated with promoting quorum responses.

The QS system we are working with is the lsr operon, which is regulated by the autoinducer molecule called autoinducer-2 (AI-2) (Figure 1).

<figure>

   <img src="" alt="Activation">
   <figcaption> Figure 1: The lsr operon feedback loop in a wild type bacterium. (Quan et al 2016) </figcaption>

</figure>

This process has many components that allow it to operate as a QS system. The operon can be broken up into five main sections and a bidirectional AI-2 sensitive promoter, pLsr. One one side of this promoter are the genes LsrR and LsrK. LsrR is a repressor of the operon (Quan et al. 2016). Two dimers of LsrR bind together and associate with an operator sequence to repress both sides of the promoter. LsrK is a kinase which phosphorylates AI-2 to phospho-AI-2 (AI2-P) (Zargar et al. 2016). AI2-P is the form of AI-2 that derepresses the operon by binding to LsrR and causing it to dissociate from pLsr, which permits transcription. On the other side of the operon are the genes LsrACDB, LsrF, and LsrG. LsrACDB is an AI-2 importer complex. LsrFG is a set of enzymes which break down intracellular AI2-P. In the wild type, when the threshold extracellular AI-2 concentration is reached, AI-2 enters the cell and activates the lsr operon. This allows LsrACDB and LsrK to increase the uptake of extracellular AI-2 and phosphorylation of intracellular AI-2 to AI2-P, respectively, which activates the operon even further. This positive feedback loop is the quorum response, and is linked to the expression of quorum regulated genes. Once there is no more AI-2 to import, this activation is eventually shut off by the increased LsrR and LsrFG that repress the operon and take AI2-P out of the system. Three other genes are also important to the dynamics of the system. LuxS is an enzyme that catalyzes cellular metabolites to intracellular AI-2 (Gonzalez et al. 2006). YdgG is a transport protein that exports AI-2 from the cell (Herzberg et al. 2006). PTS is another transport protein that imports AI-2 into the cell.

Previous research has shown that, in natural populations, small population heterogeneity can lead to bimodal activation of cells (Zargar et al. 2016, Quan et al. 2016). Because of the positive feedback dynamic of the operon, early activation of some cells causes them to uptake extracellular AI-2 quickly enough that others never get the chance to activate. It has also been shown that quorum activation can be linked to protein production to provide autonomous induction (Tsao et al. 2010). However, this bimodality means that only about 70% of cells are activated by a quorum response, decreasing the amount of potential protein produced (Zargar 2016). Our project centers on reducing this bimodality and increasing the levels of activation in a quorum response to boost protein yields and provide higher quorum regulated control of genes of interest.

A previous paper by Zargar et al. (2016) upregulated LsrACDB and LsrK to decrease bimodality and increase protein production. In this paper, a two plasmid system was used. The first plasmid was single copy, and included the LsrR gene, the pLsr promoter, and a gene for T7 RNA polymerase. This allowed the plasmid to have the same regulatory dynamics as the lsr operon. The second plasmid contained the genes to be upregulated under the control of a pT7 promoter, which requires T7 RNA polymerase for transcription. This second plasmid was a higher copy, allowing for upregulation of the genes it controlled. We built our project off of this paper, and were interested in seeing how upregulating those genes (LsrACDB and LsrK), as well as LuxS and YdgG would impact the bimodality of activation levels in our own synthetic QS plasmid (pSQS). After talking to Dr. Zargar (see Integrated Human Practices) we decided to construct and test multiple combinations of these genes on a similar two plasmid system to what he used.

When first developing our project, we relied on experts and literature to guide the design of our plasmids for modifying QS. However, information on the potential effects of our genetic modifications on QS dynamics was limited. In addition, even though several previous models of the lsr operon existed, most were not accessible or did not go into specific cellular dynamics and could not accommodate the addition of synthetic plasmids (Quan et al. 2016, Hooshangi et al. 2011, Li et al. 2006). To address this, we decided to develop a mass-action model of the interactions of the lsr operon, our synthetic plasmids, and the QS molecule AI-2. This model enables us and future teams to predict and study how QS response with the Lsr operon can be engineered for a designed end.

Development

Identifying the System and Scope of Model

For the first step in building the model, we had to clearly define our system and what the model should include. After some thought, and reviewing how past papers modeled the lsr operon, we decided that the abstract system in which we wanted to model quorum activation was a colony of cells in a Petri dish. This provided the following contexts for our model:

• Cells with fixed location in 2 dimensional space
• Medium considered to act like water for diffusion
• AI-2 free to diffuse within the system
• No growth, fixed number of cells (model starts at stationary phase where reproduction is slowed)
• Bounded or unbounded in space (Dirichlet or Neumann boundary conditions, respectively)
• Cells’ only interaction with the medium and each other is through AI-2 flux

Defining Equations and Relationships

After we clearly defined the system, we were ready to begin developing the mathematical components of the model. There were three main types of interactions we needed to develop equations for:

• Repression/Activation: LsrR repression of pLsr, T7 activation of pT7
• Enzymatic reactions (transport, catalysis, and degradation)
• Standard mass action reactions

For repression of the lsr operon by LsrR, we used the hill function model with cooperativity:

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 1: Hill function model for repression of the lsr operon. </figcaption>

</figure>

where [S] is the concentration of the substrate (LsrR), Vmax is the maximum rate of transcription of the genes repressed by LsrR, n (cooperativity) is the number of repressors needed for repression (4 in the case of LsrR), and K is the repression coefficient (Stan 2014).

For activation of pT7 by T7 RNA polymerase, we also used the hill function model:

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 2: Hill function model for activation of pT7 </figcaption>

</figure>

where [S] is the concentration of the substrate (T7), Vmax is the maximum rate of transcription of the genes activated by T7, n (cooperativity) is the number of activators needed for activation (1 in the case of T7), and K is the activation coefficient (Stan 2014).

For the enzymatic reaction of LsrK, we were able to find better documentation of enzyme kinetics, so we used the standard form of the Michaelis-Menten equation:

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 3: standard form of the Michaelis-Menten equation </figcaption>

</figure>

where [P] is the concentration of the product, kcat is the catalytic rate, [B] is the concentration of the enzyme, [A] is the concentration of the substrate, and KM is the [A] at half the maximum rate of reaction (Stan 2014).

For the enzymatic reaction of LsrFG as well as the transport reactions of LsrACDB and YdgG, we used the following reduction of the Michaelis-Menten equation:

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 4: reduction of the Michaelis-Menten equation </figcaption>

</figure>

where k is the rate of the reaction (Stan 2014). In order to use this reduction, we made two assumptions:
Assumption 1: LsrFG, LsrACDB, and YdgG reactions are at quasi-equilibrium (i.e. B << A)
Assumption 2: [Substrate] for these reactions much less than Michaelis-Menten constant (i.e. A << Km)

For the enzymatic reaction of LuxS, we used the equation

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 5: enzymatic reaction of LuxS </figcaption>

</figure>

To do this, we had to make another assumption.
Assumption 3: The concentration of the substrate (the metabolite S-ribosylhomocysteine) is much greater than the concentration of LuxS (i.e. kcat[A]/(KM+[A]) is some constant)

For all other reactions (translation, AI-2 binding to LsrR, etc.), the standard mass-action kinetic equation is used:

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 4: standard mass-action kinetic equation is used </figcaption>

</figure>

For diffusion of AI-2 through the medium (which is represented as a 2D array), we used a numerical solution to the finite element method of diffusion approximation:

<figure>

   <img src="" alt="Activation">
   <figcaption>Equation 6: finite element method of diffusion approximation </figcaption>

</figure>

where f(x,y) is a function that reports the concentration of AI-2 at that location in the array, and Δt is the change in time between timesteps in the model.

Additional Assumptions
Our model incorporates several more assumptions aside from those already listed:
Assumption 4: All reactions requiring ATP (LsrACDB and YdgG) are assumed to have it in excess (Graff 2013)
Assumption 5: We did not include the AI-2 importer PTS into our model, and instead assumed that the refined import dynamics of LsrACDB and YdgG after sensitivity testing would make PTS unnecessary

A list of all the <a href="https://static.igem.org/mediawiki/2018/8/8d/T--Virginia--2018_ODE.pdf">equations</a> used in our model can be found below.

Parameter Searching

After we had established the set of equations we wanted to use to model our system, we had to start searching through literature to find values for our parameters and initial conditions. While it is always easier to have the correct values at the outset, this is not always possible. Often, not enough research has been done about a system for it to be easy or even possible to determine many of the parameters in any given model. Since we were having trouble finding some of our parameter values, we settled with finding a general order of magnitude for some, and left others to be entirely determined through sensitivity testing. We recommend that future teams begin moving on to set up sensitivity testing sooner rather than later, and prioritize finding parameters and initial conditions within some order of magnitude.

A <a href="https://static.igem.org/mediawiki/2018/b/b3/T--Virginia--2018_Parameter_Table.pdf">table</a> outlining all of the parameter values and initial conditions used can be found below.

Choosing a Platform

As part of iGEM, we were given access to the SimBiology software package from Matlab. We were initially going to use this software to design our model, and kept working on constructing our system within SimBiology for around the first month and a half of summer. As an easy to learn and user-friendly platform, starting with SimBiology helped us conceptualize and visualize our system as well as gain some understanding of what kind of differential equations and interactions we would be using with our final model. Because we wanted to be able to quickly and carefully adjust many parameter values, run trials with different cell counts, and incorporate diffusion of AI-2 between cells, the model we were building required a more customizable platform. Therefore, we decided to choose Matlab as our software and language of choice for this model. We completed the project in Matlab, but used the insights into the system and conceptual idea for our model that we gained from SimBiology along the way.

Structure

After getting within an order of magnitude for most of our parameters, we were ready to construct the actual model and begin sensitivity testing. The structure and code of our model can be separated into five main scripts.

Model_Population.m

• Holds the initial conditions for all variables.
• Initializes a matrix of n cells.
• Initializes a matrix of n “boxes” to store the extracellular AI-2 relevant to each cell. This matrix holds the “AO” parameter for each cell (see the Cellular Equations).
• Sets the boundary conditions for diffusion.
  • Dirilecht - allows AI-2 to diffuse past the boundary of cells. This models an open system.
  • Neumann - keeps AI-2 from diffusing away past the boundary of cells. This models a closed system.
• Calls Structure.m with the matrix of cells and the matrix of extracellular AI-2 as inputs.

Structure.m

• Calls Cellular_Function.m for all cells at every time step with extracellular AI-2 matrix as input.
• Calls Diffusion.m at every time step to update the extracellular AI-2 matrix.
• At each timestep, logs the values of the parameters in each cell and the state of the extracellular AI-2 matrix. This data will be used later for analysis.

Cellular_Function.m

• Contains the system of 23 interdependent differential equations which define how each of the concentrations of molecules in the cell change from one timestep to another. Each differential equation corresponds to a parameter we have incorporated into our model.

Diffusion.m

• Defines how extracellular AI-2 moves from block to block through each time step

Analyze_Model.m

• Allows the user to produce graphs of the average and standard deviation of each parameter across all cells over time.
• Allows user to produce a heat mat showing AI-2 diffusion and lsr activation of each cell with an interactive slider to observe changes over time

Sensitivity Testing

Engineering design cycle: Identify and demonstrate understanding of the problem

• Gather data (and cite sources) and recognize unknowns and constraints
• Select applicable guiding principles and theories
• List assumptions, approximations and simplifications
• Establish quantifiable measures of success
• Show how the problem was solved
• Validate the results
• Communicate the solution

After finishing the code for our model and populating the Diffusion.m and Cellular_Function.m files with our diffusion equation and cellular equations, respectively, we had a working model. However, when we ran the model using the parameter values we had found by searching through literature, we did not get the results or dynamics we expected. This divergence from previous work (Quan et al. 2016) and experimental data was expected, since some of the parameter values were only broad approximations or completely unknown. To determine the correct value (or order of magnitude for most cases) of the unknown or dubious parameters, we designed an iterative process of sensitivity testing, keeping the engineering design cycle in mind when planning and analyzing each round of tests.

A sensitivity test is an analysis of how much impact a change in a parameter will have the results of a model. All of our tests were done without introducing any synthetic changes to the model. We optimized the model’s parameters using these “wild type” runs because expected results were available for them from previous work (Quan et al. 2016).For a description and graphic of the previous work we were trying to replicate, see see Section I of results.

For our first round of tests, we ran the model with the initial parameter values, studied the dynamics of the runs, and made hypotheses about which parameters would need to be changed, and how, to drive the dynamics of the system toward that of previous work (Quan et al. 2016). Once we identified the parameters we suspected most impacted the results, we designed a set tests to run.

Modeling

Section I: Characterization and Comparison of QS Response Topologies

In a 2016 study by Quan et al, the molecular dynamics of quorum activation in the Lsr operon were modeled in a system of differential equations. The results of this model are depicted in Figure 2 and illustrate the qualitative dynamics of quorum activation with normalized concentration curves of extracellular AI-2, phosphorylated AI-2 (AI2-P) and the gene induction of the lsr operon (lsrACDBFG).

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 2: “Numerical solution to system of ODEs revealing Lsr/AI-2 topology.” (Quan et al, 2016)</figcaption>

</figure>

In order to validate that our model of the sQS plasmid behaves in a way consistent with the dynamics of the lsr operon, we developed a similar simulation. In our simulation, what changed is that instead of the activation of the lsr regulatory sequence inducing expression of lsrACDBFG, the sQS plasmid expresses T7 RNA Polymerase. Figure 3 depicts our attempt to replicate the results of Quan et al in our sQS plasmid with T7RPol replacing lsrACDBFG.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 3: pSQS/lsr/AI-2 Topology</figcaption>

</figure>

In Figure 3, the characteristic curve of extracellular AI-2 is similar to that of Figure 2, with growth up to a time of activation followed by rapid elimination of AI-2 from the extracellular space. T7RPol behaves similarly to lsrACDB as well, growing rapidly at the time of activation and quickly approaching a stable, higher concentration. There is dissimilarity, though. AI2-P in Figure 3a is modal instead of sigmoidal as in Figure 2. The cause of this peaking is unknown, but in Figure 3b, when the values of AI2-P are truncated to eliminate the mode, a curve similar to that in Figure 2 of Quan et al can be seen underlying the mode. Once the peak of AI2-P passes, the concentration of AI2-P approaches a stable new concentration higher than the initial, which is consistent with the prediction.

One other area of inconsistency between our model and that of Quan et al is the timing of activation. In our model the point activation, here defined as the point of peak extracellular AI-2, is at 190 minutes. In Figure 2, activation occurs at 275 minutes. This discrepancy could be attributed to the presence of additional lsr regulatory components from pSQS. However, when the copy number of pSQS is increased from single copy (n=1) to low copy (n=5), this point of activation shifts to almost exactly match that of Quan et al (See Figure 4).

While for the development of this model we were unable to obtain the code that produced Figure 2, our Quorum Activation Model would benefit from a comparison of the equations used for the model of Quan et al. The positive curvature of extracellular AI-2 concentration for time less than 275 min indicates that a different set of constants or equations were used for some AI-2 dynamics. While this test verifies that our model replicates the dynamics of another lsr-based QS model, there are particular disparities in the simulation topologies that warrant future investigation and refinement of our model design.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 4: pSQS/lsr/AI-2 Topology with Higher Copy Number</figcaption>

</figure>

In the simulation shown in Figure 4, the dynamics of AI2-P and extracellular AI-2 are nearly identical to in Figure 3. However, the time of activation is shifted to 280 minutes, within 2% of the time Quan et al predicted. However, at this copy number, T7RPol overshoots a stable concentration and begins to fall around 350 min. In this way, the single copy pSQS test remains more consistent with predicted results. This change in topology as a result of pSQS copy number has important implications for the exploration of activation by Quorus because a delay in activation can correspond to a change in sensitivity of sQS to population density.

Section II: Validation of Quorum-Sensitive Activation

To validate that activation in our model relied on external AI-2 accumulation and cellular cooperativity, and not individual self-activation, we simulated QS dynamics in a single cell. The cells were placed in a small virtual medium with the bounds one unit from the edges of the cell. Two different boundary conditions (BC) for AI-2 diffusion in the medium were simulated in compared for this test. These two BC were designed to simulate the behavior of a cell in a group of other quorum sensing cells versus when isolated in an AI-2 deficient solution.

The first applied Neumann BC, where the edges of the medium are insulated to AI-2 diffusion. This allows for the cooperative activation of an entire population to be simplified to a single cell, as AI-2 can accumulate in the isolated compartment as it would in a large colony.

The second applied Dirichlet BC, where the edges of the medium are kept at zero concentration, and AI-2 is eliminated from the system as it diffuses away from the cell. This simulates how a cell would behave when in a large compartment with no other cells. Quorum activation should not be possible in this situation due to the inability of AI-2 to accumulate.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 5: Comparison of Single Cell Activation Dynamics</figcaption>

</figure>

From the simulation of a single cell in isolation (Dirichlet BC) and in a group of other quorum sensing cells (Neumann BC), it is evident there is a significant decrease in measures of quorum response when the cell is in Isolation. Both the concentration of AI2-P and the total production of GFP, our protein of interest, are consistently higher in Group than Isolation.

In this simulation, activation is measured by the concentration of AI2-P in the cell, as it directly correlates with the activity of the lsr promoter region on both the operon and the sQS plasmid. The time of activation corresponds to the time of peak AI2-P concentration, as this is the point at which the cell responds with a proportional amount of protein expression. This can be seen by comparing the concentration of AI2-P in Figure 2 to the rate of change of GFP concentration in Figure 3 around the 300 minute mark.

The steady-state, post-activation levels of AI2-P found at time greater than 400 min correspond to the total amount of GFP that will be produced before its synthesis rate equilibrates with its degradation rate. This maximal protein production is crucial in manufacturing because it corresponds to retrievable yield. When the cell is in Group, this steady-state concentration is approximately two times larger than when in Isolation. The total GFP expression has a similar relationship, further supporting the characteristic cooperativity found in our model.

There is still noticeably a problem with the model. Though significantly less than in true quorum response, the cell in Isolation still produces protein at 30% of Group due to the accumulation of AI2-P. This indicates that while our Quorum Activation Model captures some of the cooperative behavior found in quorum sensing, some fraction of the observed quorum response will simply be due to the self-induction of a single cell. Potential causes of this problem and ways future iterations of this model can be improved to address it will be discussed in “Limitations and Future Work.”

Section III: Investigation of sQS Plasmid Copy Number and Leaky Expression

When first designing our Synthetic Quorum Response plasmid, there was concern over the potential leaky expression if it were put in a mid to high copy backbone. In previous designs of plasmids similar to the sQS plasmid (pSQS), chiefly its predecessor pCT6, the backbone has been single copy to avoid AI-2 independent self-activation (Zargar). This reasoning arises from the logic that fewer pLsr binding sites, from less pCT6, mean a higher ratio of LsrR protein to its operator binding site and an overall more reliable OFF state. The justification for avoiding a higher copy number of pSQS is that it would cause activation to occur earlier and less cooperatively.

Because the iGEM Registry does not contain a single copy backbone, our system had to be designed using low to mid copy vectors. To predict potential effects of sQS plasmid copy number, the unenhanced pSQS-GFP system was simulated for three different copy numbers, single (n = 1), low (n = 5) and high (n = 20). The results of these simulations are as follows.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 6: Effect of pSQS Copy Number on Two Measures of Activation</figcaption>

</figure>

From the comparative AI2-P accumulation and the T7 RNA Polymerase (T7RPol) expression it induces, it is evident that the copy number of pSQS affects dynamics of activation. Specifically, the level and time of activation increase as the copy number increases. From the single copy pSQS to the high copy pSQS, there is a 100 minute increase in the time of activation, as measured by the time of peak AI2-P concentration. In Figure 3a there is also a noticeable difference in the concentration of AI2-P in the steady state (time greater than 400 min). This correlates to the results in Figure 3b, where the relative levels of AI2-P are mirrored in the relative levels of T7RPol in steady state.

The results of this simulation call into question whether a high copy number indeed leads to increased leakage of pSQS activation and subsequent T7 RNA Polymerase expression. In fact, due to the higher peak levels of AI2-P and 100 minute delay in activation, there is evidence that a higher copy number increases synchrony and magnitude of activation in the population. Due to the delay, higher copy number could potentially reinforce OFF state behavior for higher levels of AI2-P outside the cell.

This begs for experimental validation. A simple set of pSQS + pT7-sfGFP transformed cells, with pSQS copy number as the independent variable and pT7-sfGFP on a high copy backbone could be analyzed with flow cytometry. The results from an experiment of this kind would further validate the conclusions of this simulation.

Section IV: Exploring the Quorus Enhancement System

In this section, a combinatorial approach is taken to the design of the Quorus Enhancement System. When we discussed our project with Dr. Amin Zargar, whose work we used as the foundation of our project, he suggested performing these tests experimentally. Due to time constraints, not all of these combinations could be constructed and tested. As a result, this model provides insight into the future design of the Quorus Enhancement and Response System.

The key population characteristics we used as bases for comparison between combinations were the average GFP accumulation per cell after 600 minutes, the standard deviation of activation as measured by AI2-P concentration, and the time to activation as measured by the time of peak AI2-P concentration.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 7: Mean GFP Accumulation per Cell at 600 minutes (in μM of GFP)</figcaption>

</figure>

Figure 7 above is a table that details the total GFP expression for different combinations of AI-2 regulating genes on the Enhancement System (ES) plasmid. The color of each block reflects its value versus the control system (None,None). The simulation suggests that LuxS is the most beneficial to protein production, as it more than doubles the expression of the empty Enhancement System (None and None). Combining LuxS and LsrK genes in the ES not only increases the rate of AI-2 production in the cytoplasm but increases the concentration of LsrK protein available to convert that AI-2 to AI2-P.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 8: Standard Deviation of Peak Activation Among Cells (in μM of AI2-P)</figcaption>

</figure>

Figure 8 above is a table that details the peak standard deviation of activation. As in other parts of this project, activation is operationalized in the concentration of AI2-P, so the units of each block are micromolar of AI2-P in the cell. A red color shift in a block indicates the standard deviation increase from control. Green color shift indicates a decrease. YdgG significantly decreases activation variance whenever present. However, LuxS significantly increases variance of activation. As predicted in a study by Zargar et al. (2016), the inclusion of LsrK decreases variance compared to unmodified quorum sensing, though not as much as by the inclusion of YdgG.

Comparing the results from Figures 7 and 8, and balancing the costs of low expression and activation heterogeneity, the model suggests that LuxS and YdgG are the most effective combination to be incorporated into the Enhancement System.

However, another important aspect of the Enhancement System is the population density at which activation occurs. Manipulating how cells respond to different concentrations of extracellular AI-2 affects how they will respond to growth and AI-2 accumulation. As shown in Results, modifying the Enhancement System results in a shift of the timing, and population density, of initial quorum activation. As a result, the following table enumerates the time of activation relative to unmodified sQS response.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 9: Shift of Activation Time from pSQS Quorum Response (in Minutes)</figcaption>

</figure>

Orange block coloring corresponds to a negative change, where activation happens earlier. Green activation corresponds to positive change and a delay in activation. YdgG delays quorum activation in both the case of being alone and with LuxS. However, when YdgG is combined with LsrK, the activation is significantly earlier. In fact, LsrK causes quorum activation to be significantly earlier whenever it is incorporated into the Enhancement System. This likely means the incorporation of LsrK enables activation at lower extracellular AI-2 concentrations and corresponding lower population densities.

This exploration of the dynamics of the Quorus Enhancement System elucidate the dimensions of control that can be exerted over a quorum sensing population. In Figures 7 and 8, the factors directly related to manufacturing, mean and homogeneity of induction, are found to be manipulable by selecting certain pairs of genes from the Modular T7-Regulated Library of parts. Additionally, the sensitivity of quorum response can be modulated from the natural value by similar pairings, as shown in Figure 9.

Section V: Comparison to Experimental Results

Our model of quorum-sensing based induction produced many predictions of experimental behavior. Figures 7 through 9 from Section IV contain The strongest agreement between experimental results and our model predictions is in our predictions from Figure 8: Standard Deviation of Peak Activation Among Cells. Using our population model of an array of quorum sensing cells, we computed the variance of activation levels among the cells. These measures of activation variance, and consequently standard deviation, provided comparative predictions for how different Enhancement System variants might affect population activation distributions.

<figure>

   <img src="" alt="Activation">
   <figcaption>Figure 10: Activation Distributions of Enhancement System Variants</figcaption>

</figure>

Additional Resources

<a href="https://static.igem.org/mediawiki/2018/2/2d/T--Virginia--2018_ModelingFiles.zip">Click here</a> to view our MATLAB code, sensitivity tests, and figures for final runs.

<a href="https://static.igem.org/mediawiki/2018/b/b3/T--Virginia--2018_Parameter_Table.pdf">Table of Parameters and Initial Values</a>

<a href="https://static.igem.org/mediawiki/2018/8/8d/T--Virginia--2018_ODE.pdf">System of Equations for Cellular Quorum Sensing</a>

References

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G.-B. Stan. (2014). Modelling in Biology. [Online]. Available: <a href="http://www.bg.ic.ac.uk/research/g.stan/2010_Course_MiB_handouts.pdf">http://www.bg.ic.ac.uk/research/g.stan/2010_Course_MiB_handouts.pdf</a>

Gonzalez J, Neshavan N (2006). Messing with Bacterial Quorum Sensing. Microbiology and Molecular Biology Reviews 70, 859-875.

Graff, Steven. “A Mathematical Model to Study the Role of the Lsr Intergenic Region in Mediation of Autoinducer-2 Quorum Sensing in Escherichia Coli” University of Maryland, 2013.

Graff, Steven M., and William E. Bentley. “Mathematical Model of LsrR-Binding and Derepression in Escherichia Coli K12.” Journal of Bioinformatics and Computational Biology, vol. 15, no. 01, 2017, p. 1650039., doi:10.1142/s0219720016500396.

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