Design

Designing the Switch

To design a system that is able to play Conway’s Game of Life, one of the components we need is an interval switch. The function of this switch is to detect whether there are ‘too few’, ‘the right amount’ or ‘too many’ neighboring cells in the ON state, so we need a switch that is activated when the concentration of our signalling species, HSL, is in-between two thresholds. In order to be able to easily test our system we use Green Fluorescent Protein (GFP), as the protein that should be expressed when the switch is ON. We would ideally want to engineer a cell that starts to express GFP as soon as the concentration of HSL exceeds a certain threshold (indicating that there are ‘enough’ neighbors in the ON state) and stops expressing GFP as soon as the HSL concentration exceeds a second threshold (indicating too many neighbors are in the ON state). Of course this kind of discrete behavior is not feasible in a living cell, so we aim to approximate this behavior, hoping to get a response as shown in figure 2.1-B. In this figure, GFP expression exceeds a certain value (at which we define the switch to be ON) when the lower threshold is reached (having the ‘right amount’ of neighbors), and drops below this value when the second threshold is reached (having ‘too many’ neighbors).

Figure 2.1: A: Response curves of the hybrid promoter and the promoter with weakened operator binding sites (OR). B: combined effect of promoter activation and inhibition on the hybrid promoter. This is the response we would like to get from our interval switch; when HSL concentration exceeds the first threshold and enough neighbors are ON, GFP is expressed, when HSL concentration passes the second threshold and too many neighbors are ON, GFP expression stops. Note that we define the switch to be ON when GFP expression reaches a certain limit, this is indicated by the vertical red lines and represents the boundaries between ON and OFF states.

An abstract version of the network we designed is shown in Figure 2.2. Here, we use a promoter that is inducible by HSL and facilitates GFP expression when HSL reached a level high enough to activate the promoter, this gets the system going into the ON state. To get the system to switch off again after the HSL concentration reaches the second threshold, we add a weaker HSL inducible promoter that needs a higher concentration of HSL to be activated and facilitates the expression of an inhibitor that blocks GFP expression. Figure 2.1-A shows the response curves we would like these promoters to have. The blue curve shows the activation of the promoter that facilitates GFP expression, the red curve shows the activation of the promoter that facilitates expression of the inhibitor. When we combine the activating effect of the HSL inducible promoter and the inhibiting effect of the weaker promoter, we should get the result shown in Figure 2.1-B.

Figure 2.2: a schematic representation of our proposed system.

2.1 Implementation

For our cellular automaton in vivo, we want to implement a system in which a bacterium (here: E. coli) is able to estimate the number of active neighboring bacteria by measuring the concentration of a certain quorum sensing molecule. For this quorum sensing molecule, we decided to make use of 3-hexanoyl homoserine lactone (3OC6-HSL). The system consists of two parts; a sender and a receiver device. Both are inserted into one cell, which is a novel approach in this kind of research. Due to the receiver device, the cell is able to determine the concentration of HSL in its direct surroundings. When the concentration is low, there are few active cells in the proximity of the considered cell. Based on the signal ‘low concentration’, the cell will be in its OFF state, meaning no production of HSL. Whenever the concentration in the surroundings is higher, the cell switches to a state in which it will produce HSL itself. To distinguish this state from the latter, we call this the ON state. Whenever the concentration is too high, meaning there are too many cells in the ON state in the proximity of our cell, the cell switches to the OFF state again. Linked to the production of HSL is the production of Green Fluorescent Protein (GFP), which is the signal we use to determine the state a cell is in at a given moment (figure 2.2 and 2.1-B).

2.2 Weakened operator

To obtain this system, we make use of three different promoters. The first (pTet) is constitutively on, promoting the expression of LuxR, a protein that forms the complexes with HSL that are needed for the induction of the second and third promoters. The second promoter (pLux) is inducible by low concentrations of HSL and promotes the expression of new HSL and GFP. Thus, when there are some neighbors producing HSL (the neighbors are in the ON state), this can diffuse freely over the cell membrane, resulting in the induction of HSL production by the new cell (the new cell switches to the ON state as well). When the concentration of HSL is high enough, the third promoter (DpLux) is also induced, leading to the expression of CIl. This protein represses the mechanism of pLux, thus stopping the production of HSL and GFP. Due to the changing levels of induction of the different promoters, the interval shaped response curve is created (figue 2.1-B). The simplified mechanism based on the presence of HSL in the direct surrounding of a cell is depicted in figure 2.3.

2.3 Modeling

One of the benefits of using an engineering approach in the design of a genetic network is that we can construct a model in which we can predict the behavior of our system. A detailed report of our modeling work can be found in chapter 6 but we will list some of the results here so we can get a clearer view of how we expect the network to behave in a biological environment.

Our first approach was constructing a single cell model in SimBiology [10]

where each of the BioBricks is modeled as a ‘module’. These modules are then connected and allow us to simulate the system as a whole. Besides this ‘single cell approach’ we also constructed a less detailed model in MATLAB [11]

Matlab. The Language of Technical Computing, The Mathworks Inc., version 7.6.0.324 (R2008a).

which can be used to run spatial simulation on a grid of cells (for more details we refer to the modeling chapter).

Figure 2.4 shows the GFP expression plotted against time. We can see here that our network produces a pulse before going to a stable state. The size of this pulse is dependent on the HSL concentration as well as the speed at which it increases. It could in theory be circumvented by introducing a feed-forward loop in the GFP expression (e.g. adding a promoter which responds to HSL and activates the hybrid promoter), giving the inhibitor time to block the hybrid promoter before GFP is expressed. However, as the size of the pulse also depends on HSL concentrations and the rate at which they change, we chose to first see how our system responds in a real test setting and see how this pulse expressed itself.

Figure 2.4: GFP expression plotted against time.

Figure 2.5 shows a graph of GFP expression plotted against different HSL concentrations. The GFP values are ‘measurements’ taken 1 hour after induction. We can see that indeed it shows the kind of behavior we are trying to create, where GFP is expressed only at a certain interval of HSL concentrations. It is not as clean as we would like though, is shows a leakiness at high HSL levels when maximum CI expression is reached and not all promoters are fully inhibited. Again, this might show a different response in a real life test setup.

Figure 2.5: GFP expression plotted against HSL concentration (measurements taken after 60 minutes).

Figure 2.6: Expected GFP expression in a spatial setting. The red square depicts a cell-population that expresses a constant amount of HSL. The white cells indicate active GFP expression of the receiver cells (interval switches).

The last image displayed, figure 2.6, shows a spatial model where sender cell (the red square), which expresses a constant amount of HSL, is placed in a grid of receiver cells (interval switches). This image shows us that receiver cells that are too close to the sender get too much HSL and do not express GFP and cells that are too far from the sender cells do not get enough HSL and also do not express GFP. The active cells show where our interval is situated and the HSL concentration is ‘just right’. This is also a setup which could be easily tested in the lab to see if our system works.

Genetic Circuit

In the previous chapters we described how bacterial cells could be able to play the Conway’s Game of Life. Cells are able to sense and send signals by means of the quorum sensing mechanism of Vibrio fischerii (of which parts are available). These signals can be measured and interpreted by the interval switch which controls the response by expressing proteins that switch the state between ON and OFF. This chapter will explain in more detail how these functionalities will work together in one system. In the system, 3 different promoters are present (Bba_K077200 (adjusted BBa_R0062), BBa_R0065 and BBa_R0040). The adjusted promoter should have a lower affinity for LuxR.HSL complex than BBa_R0065. This part is not available from the registry. In the database BBa_R0065 was present which is inducible by the LuxR-HSL complex and repressed by CIl. This suits our system well since it can be induced from an external signal and could also be turned off again.

The genes aiia, gfp and luxI in the genetic circuit have a LVA tag, which effectively decrease the live-span of the protein. The luxR gene does not have an LVA tag since this protein should always be available in the cell and resetting the system does not rely on the breakdown of LuxR. The other proteins are degraded faster in the cell, which means that the whole system itself would have a more dynamic response time and each step of the game should be faster. All the genes have the same ribosome binding sites (RBS) in front of them. From simulations of the system we learned that varying the RBS efficiency did not influence the response significantly; therefore we chose to use ribosome binding sites with the same efficiency in the whole system. The terminators are all the double BBa_B0015 terminators. Since most of the biobricks that were needed to build our system were also present as a gene with a RBS already attached in the database, we used the parts that had an efficiency of 1.

We designed the system on two separate plasmids (K077557 and K077668, figure 3.1). In total, the system contains around 5000 base pairs and consists of 19 biobricks. K077557 harbours the parts of the system which define the lower limit of the interval switch. K077557 produces GFP and LuxI if the HSL concentration rises above the lower limit, meaning the cell is turned ON. This information is subsequently sent to the neighbors using LuxI, which produces HSL. Next to this, K077557 contains S03119 which produces LuxR constitutively, enabling response to HSL. K077668 defines the higher limit of the interval switch. It needs a higher concentration of HSL compared to K077557 to enable transcription. If the HSL concentration rises above this higher limit, K077668 produces CI and AiiA. CI inhibits K077557, stopping production of GFP and LuxI, in game terms the cell is going to the OFF state and stops sending a signal. AiiA breaks down the intracellular HSL and thus resets the system. As long as CI is present K077557 is not susceptible to HSL, because HSL is broken down by AiiA the production of AiiA and CI will cease. When CI and AiiA are broken down the system is back to its initial state and ready to receive new signals.

Figure 3.1 Design of genetic circuit. Legend: 1. LuxR production, constitutively on. 2. HSL diffusion through the cell membrane. 3. LuxR-HSL complex formation. 4. LuxR-HSL binding (low threshold). 5. HSL production. 6. Green Fluorescent Protein (GFP) synthesis. 7. LuxR-HSL binding (high threshold). 8. CIl binding, repression. 9. HSL degradation by AiiA.

Physical System

5.1 Design requirements

To implement a cellular automaton in a real physical environment, a suitable set up should be designed. This set-up must contain some basic requirements:

• Each ‘grid-cell’ is represented by what we will call a compartment containing one or more biological cells (note that this does not have to be a physical compartment, in could just be a colony in a fixed position).
• Each compartment must remain in a fixed position, cells should not be able to travel to other compartments.
• Each compartment must be connected to each of his direct neighbors, meaning that a signal must be able to travel between them.
• The signalling species should ideally only travel to the direct neighbor and no further so that only direct neighbors can signal each other.

5.2 Possible designs

Besides meeting the design requirements listed above, another characteristic that might pose a problem with an implementation of a cellular automaton is that when we use Conway’s Game of Life rules, each compartment on a regular grid would have 8 neighbors and it should react in the same way to their signals, be it a diagonal or an orthogonal neighbor. However, because diagonal neighbors are in fact further away than orthogonal neighbors, diagonal neighbors might actually have less influence on the state of a compartment. A way to avoid this would be to use a hexagonal grid, where each compartment would have six neighbors at an equal distance.

Using an approach that satisfies there conditions is using a grid of compartments where each compartment is a semipermeable membrane that allows signal molecules (in our case HSL) to travel to other compartments while keeping the cells in place. Cells can be grown within the compartments in liquid media, but also on a solidified media with agar. To tweak the set up in order to limit the range of the signal to only the direct neighbors, media with different densities could be used, changing the diffusion speed. The combination of diffusion speed and rate of decay define how far the signal travels.

Another, more advanced approach one might think of, is using small bioreactors as compartments (fig. 5.1). This will retain grid-cells to deliver the HSL molecules to compartments other then their direct neighbors, when compared with the use of colonies on agar plates. Diffusion, and thereby communication, between the bioreactors is facilitated by semipermeable membranes between the compartments. A continuous supply of nutrients ensures a situation where every cell is active in the exponential growth phase. In this phase, cells will react quickly on signal molecules from adjacent bioreactors and lowers the time needed for one generation in the game. Furthermore, by draining cells and depleted media, the amount of cells and quality of media should be constant at an adjusted level.

Figure 5.1: A possible hexagonal set up where signalling molecules diffuse between compartments.

5.3 Experimental results

During this project, we only tested a system where colonies are placed in a grid structure on an EZ-agar plate. In these tests a functional sender device, which constantly produced LuxI, was surrounded by receiver colonies that expressed the GFP protein upon HSL-induction. From these experiments we could see that, when induced by HSL, the cells that live on the edge of the colonies started to express GFP, followed by the cells that were closer to the centre of the colony. This might not be favorable to our system because this means that the living cells in a compartment are not necessarily in the same state.

5.4 Future implementations

The exact requirements of our physical system will stay unclear, because until now, a working version of our designed genetic network is lacking. By extensive testing of this genetic system one can see the reactions of the system seen in different physical arrangements. Combining these experimental data with our basic requirements (depicted in 5.1) and the rules of Conway’s Game of Life, will suggest the ideal physical set up to be used.