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Introduction

Intercellular Communication and DNA Messaging

Intercellular communication between cells in nature allows for coordinated population-level behavior, enabling spatial and temporal organization and complex responses to environmental stimuli.

Synthetic biology is continually expanding the range of programmable cellular characteristics and behaviors, and incorporation of intercellular communication into engineered cell populations has extended programmable behavior to a population level.

Engineered AHL-based intercellular communication

Many bacteria naturally secrete acylated homoserine lactone molecules, or AHLs, which can be detected by other members of the bacterial population. Concentration-sensitive detection of AHL brings about significant qualitative changes in cell behavior via transcription regulation, including feedback on AHL production. Such natural quorum sensing systems are associated with coordinated behaviours such as biofilm formation [1] and bioluminescence [2]. These natural bacterial quorum-sensing systems have been successfully modulated to enable programmed intercellular communication in engineered bacterial populations. In this approach, genes associated with AHL production, detection, and response are “re-wired” such that they correspond to different input stimuli and output behaviors. Coordinated population-level behaviors including two-dimensional pattern formation [3], coordinated oscillations in gene expression [4], and even a system exhibiting predator-prey dynamics [5] have been demonstrated using this technique.

Fig 1. AHL molecules secreted by bacterial cells are detected by receptors in other bacterial cells

Why DNA messaging?

While AHL-based communication is a useful approach for engineering population-level coordination of bacterial cells, the quorum sensing messaging system has some inherent weaknesses that limit the diversity and information content of messages that can be communicated using this method.

Baseballs and Bottles

An analogy is useful in appreciating the expansion of potential for communication afforded by DNA messaging over AHL based messaging. Consider the following absurd but illustrative situation:

Fig 2. DNA messaging would allow DNA to be exchanged between cells as a form of communication.

The Nuts and Bolts of DNA Messaging

Ortiz and Endy [6] designed a system for effective DNA messaging wherein DNA messages are packaged in M13 bacteriophage particles, dispatched from an E. coli “sender population”, and delivered to an E. coli “receiver population” via the M13 infection mechanism.

In nature, M13 proteins package the M13 genome into bacteriophage particles through recognition of the M13 packaging sequence (M13 ori). As it turns out, any circular DNA bearing the M13 packaging sequence will be recognized by the M13 proteins and packaged into a bacteriophage particle. Removing the M13 packaging sequence from the M13 genome and placing it on a plasmid, which is then called a “phagemid”, causes the phagemid DNA to be packaged instead of the M13 DNA. A version of the M13 genome that does not get packaged itself due to a missing packaging sequence is called a “helper plasmid”.

A phagemid carrying a DNA message is referred to as a “messaging phagemid” and will be transmitted from sender cells that carry a helper plasmid

• DNA of arbitrary length can be transmitted: M13 viral packaging occurs at the cell membrane, where the viral particle forms around the DNA as it is packaged and slowly secreted, forming a long filament. This allows DNA of arbitrary length to be packaged [7].

• Sender cells continue to grow: M13 is not a lytic bacteriophage. Since M13 viral particles are secreted through the cell membrane, infected cells are able to continue living and dividing, albeit at ½ to ¾ their normal rate [7]. Because of this, cells sending a DNA message need not commit suicide to transmit their message!

• Only F+ cells can be infected: Infection by M13 bacteriophage requires attachment to the E. coli F pilus. Therefore, only E. coli cells carrying the F plasmid (F+ cells) are susceptible, while F- cells are not.

The basic scheme of DNA messaging is seen in Figure 3. This method was established in 2012 when Ortiz and Endy demonstrated transmission and receipt of a DNA message encoding GFP and ampicillin resistance, as well as a separate message encoding T7 RNA polymerase [6]. Their proof of principle demonstration indicates the viability of DNA messaging and suggests extension of the method to diversify potential communication programs.

Figure 3. Sender cells contain a messaging phagemid and a helper plasmid, which allows them to secrete viral particles. Receiver cells must be F+. When the two cell populations are co-cultured, DNA messaging takes place.

The Idea

We are fascinated by the idea of DNA messaging. Since this intercellular communication method [6] is so new, there is room for advancement of the method. We have identified ways to advance the system. We believe that in advanced DNA messaging, a DNA message should be:

1. Controllable: A DNA message should not necessarily be constitutively transmitted, but should rather be controllable by a particular stimulus.

2. Modifiable: Established methods of DNA recombination should be available for modification of a DNA message by senders or receivers. In particular, recent advances in DNA digital memory and logic [X,X] should be incorporated into DNA messaging.

3. Retransmittable: A receiver cell should be able to retransmit a DNA message following modification.

We developed designs, models, and experiments to approach these goals.

* It should be noted that modification of a DNA message was accomplished by Bonnet et al in a March publication [8]. We were not aware of this until our project was nearly complete. However, our work toward modification of a DNA message was key to our project and did allow us to contribute new BioBricks to the registry.

1. Lewis-Sauer K, Camper A, Ehrlich G, Costerton J, Davies D. Pseudomonas aeruginosa displays multiple phenotypes during development as a biofilm. Journal of Bacteriology, 2002, 184 (4) pp 1140–54.

2. Nealson K, Platt T, Hastings JW. The cellular control of the synthesis and activity of the bacterial luminescent system. Journal of Bacteriology, 1970, 104 (1) pp 313–22.

3. Basu S, Gerchman Y, Collins CH, Arnold FH, Weiss R. A synthetic multicellular system for programmed pattern formation. Nature, 2005, 434 pp 1130–1134.

4. Danino T, Mondragón-Palomino O, Tsimring L, Hasty J. A synchronized quorum of genetic clocks. Nature, 2010, 463 pp 326–330.

5. Balagaddé FK, Song H, Ozaki J, Collins CH, Barnet M, Arnold FH, Quake SR, You L. A synthetic Escherichia coli predator–prey ecosystem. Molecular Systems Biology, 2008, 4, pp 1–8.

6. Ortiz ME, Endy D. Engineered cell-cell communication via DNA messaging. Journal of Biological Engineering, 2012, 6:16.

Control

Design

Controlling Transmission of a DNA Message

In communication systems, messages may need to be sent in response to particular stimuli. We sought to incorporate this control into DNA messaging at the level of M13 viral particle production.

Setting out to control particle production, we had the option of manipulating any of the 11 genes in the M13 genome (see the M13 Bacteriophage page). In our design, viral particle production is regulated by controlling production of M13 protein VIII, which is the major coat protein [1]. Gene VIII is removed from the helper plasmid and is placed on the messaging phagemid under control of an inducible promoter (Figure 1).

When gene VIII is induced on the messaging phagemid, we should have gene VIII complementation and DNA message transmission. When gene VIII is not induced, there will be no gene VIII present and no complementation will take place.

When gene VIII is induced on the messaging phagemid, we should have gene VIII complementation and DNA message transmission. When gene VIII is not induced, there will be no gene VIII present and no message transmission will take place.

The choice to control gene VIII was made based on several factors:

• Control over bacteriophage ϕX174 particle production was accomplished using the same approach [2].
• Gene VIII is the last gene in an operon on the M13 genome [1], so its removal is unlikely to affect transcription of other genes.
• Gene VIII is not known to have regulatory function, so regulation of other M13 genes should not be disturbed.
• Monica Ortiz (co-author on the original DNA messaging paper [3]) suggested the choice of gene VIII over other genes.

Using this design, we expect DNA messaging to be controllable through regulation of the M13 major coat protein, which is required for message transmission.

1. Sambrook J, Russell DW. Molecular Cloning: A Laboratory Manual. 3rd edition. Cold Spring Harbor Laboratory Press, Cold Spring Harbour, NY. 2001.

2. Jaschke P, Lieberman E, Rodruiguez J, Sierra A, Endy D. A fully decompressed synthetic bacteriophage øX174 genome assembled and archived in yeast.Virology, 2012, 434(2) pp 278-84.

3. Ortiz ME, Endy D. Engineered cell-cell communication via DNA messaging. Journal of Biological Engineering, 2012, 6:16.

Experiment

Regulating transmission of a DNA message.

To accomplish our goal of controlling DNA messaging, we sought to control M13 viral particle production by removing M13 gene VIII from the helper plasmid and placing it under an inducible promoter on a separate construct (See Figure 0.1). M13 protein VIII, the product of gene VIII, is the major coat protein of the M13 bacteriophage, so viral particle production and DNA messaging should be possible only when gene VIII is expressed and protein VIII is present (refer to the Design page).

As a step toward this goal, we first sought to demonstrate that viral particles could be produced when a helper plasmid missing gene VIII is complemented by a copy of gene VIII constitutively expressed on a separate construct; we chose to place gene VIII on the messaging phagemid.

Figure 0.1: Experimental design for gVIII complementation test

1.Constructs

1a. Experimental constructs

To test the complementation of gene VIII, we used E. coli βDH10B strain containing the messaging phagemid and helper plasmid as a sender population, and we used E. coli JM109 strain containing a constitutively expressed GFP expression cassette (BBa_I20260) in pSB3K3 (Kanamycin-resistant) as a receiver population. The senders, E. coli βDH10B, require diaminopimelic acid (DAP) as supplement for growth in LB media. The receivers, E. coli JM109 are F+ and do not require this supplement. (Recall that receivers must be F+ for delivery of a DNA message by an M13 particle.) This allows counter-selection against sender cells by growing in LB media with no DAP supplementation. Receiver cells that have received the message can be selected using chloramphenicol and RFP expression.

In order to perform the gene VIII complementation test, we made the following constructs using standard assembly and PCR methods, and then transformed them sequentially into E. coli βDH10B (F-) strain (Figure 1.1).

• M13 ori – RFP cassette - Constitutive Promoter + RBS – M13 gVIII (pSB1C3) (BBa_K314110- BBa_J04450- BBa_K1039020)
• HPdO Δ gVIII (BBa_K1039017)

Figure 1.1: A messaging phagemid containing M13 gVIII would complement a modified helper plasmid with gVIII deleted and create M13 viral particles. The messaging phagemid would then be packaged into an M13 viral particle due to the presence of the M13 origin on the phagemid and could then be transferred through viral infection.

An indicator plasmid containing a GFP expression cassette was transformed into E. coli JM109 (F+) receiver cells (Figure 1.2).

• GFP cassette (pSB3K3) (BBa_I20260)

Figure 1.2: Receiver cells that contain the indicator plasmid could be distinguished from sender cells due to the presence of the GFP.

1b. Control constructs

As a positive control, we used senders carrying a complete helper plasmid (gene VIII present) and a messaging phagemid carrying the M13 origin of replication and an RFP expression cassette (BBa_K314110- BBa_J04450). With the helper plasmid intact and all phage proteins expressed, we expect the phage machinery to recognize the M13 origin and package the messaging phagemid to be sent to the receiver population.

• M13 ori- RFP cassette (pSB1C3) (BBa_K314110- BBa_J04450)
• HPdO (BBa_K1039016)

As a negative control to confirm the necessity of the M13 origin, we performed an experiment identical to the positive control using a messaging phagemid that is missing the M13 origin. Without the M13 origin, we expect no transmission of the messaging phagemid. We also performed a negative control for the gene VIII complementation experiment wherein the messaging phagemid was missing the M13 ori.

• RFP cassette (pSB1C3) (BBa_J04450)
• HPdO (BBa_K1039016)

AND

• RFP- Constitutive promoter + RBS - gVIII (pSB1C3) (BBa_J04450- BBa_K1039020)
• HPdO Δ gVIII (BBa_K1039017)

To demonstrate the need of gene VIII for viral packaging, we added a negative control to the experiment that is identical to the gene VIII complementation experiment, but with the messaging phagemid lacking gene VIII (ie no gene VIII was present in the system).

• M13 - RFP cassette (pSB1C3) (BBa_K314110- BBa_J04450)
• HPdO Δ pVIII (BBa_K1039017)

In order to demonstrate the necessity of having both a helper plasmid and a messaging phagemid for successful transmission, we also included experiments wherein senders contained only a messaging phagemid or only a helper plasmid. In these cases we expected no message transmission.

• HPdO (BBa_K1039016)
• HPdO Δ gVIII (BBa_K1039017)
• M13 ori - RFP- Constitutive promoter + RBS - gVIII (pSB1C3) (BBa_K314110- BBa_J04450- BBa_K1039020)
• RFP - Constitutive promoter + RBS - gVIII (pSB1C3) (BBa_J04450- BBa_K1039020)
• M13 - RFP cassette (pSB1C3) (BBa_K314110- BBa_J04450)
• RFP cassette (pSB1C3) (BBa_J04450)

As a negative control to demonstrate the necessity for F+ receivers, we transformed the indicator plasmid used in the receiver cells into E. coli DH5α (F- strain) and used it as a receiver population. We expected no successful messaging with F- receivers because the F pilus, which is only present in F+ strains, is required for the mechanism of message delivery using M13 particles.

In addition, we plated senders and receivers individually on our selection plates to check for contamination.

1c. Biobricks submitted

We submitted the following Biobricks to the Standard Registry of Biological Parts.

1. BBa_K1039016- HPdO Helper Plasmid

2. BBa_K1039017- HPdO with deleted gVIII Helper Plasmid

3. BBa_K1039018- pVIII of M13 virus

4. BBa_K1039019- pVIII of M13 virus with strong RBS (B0034)

5. BBa_K1039020- pVIII of M13 virus with strong constitutive promoter (J23104) and RBS (B0034)

2. Experimental design for Gene VIII complementation

After making our constructs, we set out to determine if our helper plasmid with gene VIII knockout (BBa_K1039017) could be complemented with a messaging phagemid consisting of M13 origin (BBa_K314110) - RFP expression cassette (BBa_J04450) - Constitutive Promoter and Ribosome Binding site + M13 gene VIII (BBa_K1039020) in pSB1C3. We expected the expression of gene VIII from the messaging phagemid to produce a viral coat protein so it can complement the helper plasmid with gene VIII knockout. With a complete set of functional proteins, the virus should be able to assemble itself and package the messaging phagemid through recognition of the M13 ori to successfully deliver the message to the receiver population. Chloramphenicol resistance (from the pSB1C3 backbone) and RFP expression should be detectable in receiver cells that have received the DNA message.

2a. Co-culture experiment of sender and receiver populations

Message transmission was tested by co-culturing sender and receiver populations, then selecting for successful transmission. During the co-culture, M13 particles containing the DNA message should be secreted by sender cells and should deliver the message to receiver cells.

2b. Exposure of receiver population to filtrate from sender culture

Since the message is transmitted through viral particles, sender cells should not need to be in contact with receivers for message transmission; presence of message-carrying viral particles should be enough. We therefore also tested message transmission by exposing receiver cells to culture filtrate from sender cells, which should contain viral particles secreted by the senders that can deliver messages to receivers.

3. Experimental protocol

3a. Co-culture experiment (Figure 3.1)

• Grow sender cells and receiver cells individually in liquid LB media with appropriate antibiotics and supplements overnight at 37 °C with shaking.
• Subculture a 1:100 dilution into fresh media containing the appropriate antibiotics and supplements and grow at 37 °C with shaking until O.D ≈ 0.6 (± 0.1).
• Remove the chloramphenicol antibiotic from the media of sender cells by centrifuging the cultures and washing the pellet 3 times with saline.
• Resuspend the sender cells in fresh LB liquid media containing no chloramphenicol
• Co-culture sender cells with receiver cells at a 2:1 volume ratio and incubate at room temperature for ~ 5 h with shaking.
• Subculture the co-culture at a 1:100 dilution into fresh LB media with no DAP and the appropriate antibiotics to select for receiver cells containing the messaging phagemid. Incubate overnight at 37 °C with shaking.
• Dilute cultures 10-6 in saline and plate 100 μL of the diluted culture onto LB Cm 20 Km 25 agar.

Figure 3.1 PhiC31 or Bxb1 invertible promoter switch (PB state). The invertible promoter switch consists of a strong or medium promoter (J23119 or J23118) and an upstream transcription terminator, flanked by either PhiC31 or Bxb1 attP and attB sites (PB state). These constructs were standardized by adding a standard Prefix/Suffix and submitted as BioBricks.

• Grow sender cells and receiver cells individually in liquid LB media with appropriate antibiotics and supplements overnight at 37 °C with shaking.
• Subculture a 1:100 dilution into fresh media containing the appropriate antibiotics and supplements and grow at 37 °C with shaking until O.D ≈ 0.6 (± 0.1).
• Remove the chloramphenicol antibiotic from the media of sender cells by centrifuging the cultures and washing the pellet 3 times with saline.
• Resuspend the sender cells in fresh LB liquid media containing no chloramphenicol
• Co-culture sender cells with receiver cells at a 2:1 volume ratio and incubate at room temperature for ~ 5 h with shaking.

Results

Table 4.1: Results for M13 complementation co-culture experiment in liquid LB Cm Km

TABLE 1 Here

In many of the test samples, growth in liquid culture was detected when the sender cells contained the messaging phagemid, regardless of whether the M13 ori was present or if the helper plasmid was present. Since the receiver cells were selected on the grounds of containing chloramphenicol resistance, transfer of the messaging phagemid was very likely due to conjugation between sender cells and receiver cells, rather than infection by the M13 bacteriophage. When the sender population contained a helper plasmid along with the messaging phagemid, the selection for receiver cells with chloramphenicol yielded much less growth. This could suggest that if M13 bacteriophage were being produced, it would slow the growth of the receiver cells. It is also likely that the message was transmitted via M13 bacteriophage but the results are inconclusive.

Monica Ortiz from the Endy Lab proposed the use of F+ receivers and senders, which may decrease conjugation. The new design of the experiment would use the E. coli JM109 F+ strain as the sender population as well as the receiver population. The sender populations would have the same plasmids as was mentioned in the previous experiments but the receiver population would contain a GFP indicator in the pSB4A5 backbone so that receiver cells could be selected using ampicillin. Currently, the sender and receiver cell populations are in the process of being established so that the co-culturing experiment can be performed.

The filtrate experiment was the second experiment proposed by our team. Conjugation requires cell-cell contact so by filtering the sender cells out of the media and using the filtrate to infect receiver cells, message transfer would only occur through M13 viral infection. The filtrate experiment was performed but no colonies were observed on the plates. It is believed that the cells were too dilute when plating on agar so the experiment will be repeated and optimized, possibly using E. coli JM109 F+ strain as the sender population.

In addition to the experiments proposed above, we devised a plaque assay that allows us to visualize M13 infection of the receiver cells. Although M13 virus does not undergo the lytic cycle, its infection of a cell population slows its growthreference. If plated onto agar, an area of diminished growth would be observed at the site of M13 viral infection, creating a turbid plaque. We proposed to use E. coli JM109 F+ receiver cells containing a helper plasmid and infecting them with the filtrate of E. coli JM109 F+ sender cells as described above. Receiver cells that become infected by the M13 virus would gain the messaging phagemid and then produce more virus (since they contain the helper plasmid), which will then infect neighbouring cells, thus creating a zone of diminished growth. This experiment is still in its preliminary stages but we hope to achieve positive results.

5. Supplemental information

5a. Antibiotics and Supplements

Table 5.1: Concentrations of antibiotics and supplements used in liquid and solid media

TABLE 2 here

DH5α

F- endA1 glnV44 thi-1 recA1 relA1 gyrA96 deoR nupG Φ80dlacZΔM15 Δ(lacZYA-argF)U169, hsdR17(rK- mK+), λ–

JM109

endA1 glnV44 thi-1 relA1 gyrA96 recA1 mcrB+ Δ(lac-proAB) e14- [F' traD36 proAB+ lacIq lacZΔM15] hsdR17(rK-mK+)

βDH10B

F- endA1 recA1 galE15 galK16 nupG rpsL ΔlacX74 Φ80lacZΔM15 araD139 Δ(ara,leu)7697 mcrA Δ(mrr-hsdRMS-mcrBC) λ-

Modify

DESIGN

Modifying a DNA Message

Recent work with serine integrases (see Appendix on Serine Integrases) has demonstrated their utility in implementing digital memory [1] and Boolean logic [2,3] in DNA through inversion of DNA sequences. We sought to incorporate an invertible promoter switch into a DNA message, consisting of a promoter whose orientation can be flipped and restored through action of a serine integrase and a corresponding recombination directionality factor (RDF). The switch would allow different gene expression based on its state, could function in digital memory storage, and could be flipped in sender cells before transmission or in receiver cells after receipt of the message.

As part of this goal, we designed four invertible promoter switches, corresponding to combinations of two different integrase systems, Bxb1 and Phi C31, with two different promoters, J23119 and J23118 (see the BioBricks page). Our design is directly inspired by and closely mimics the recombinase addressable data (RAD) module designed by Bonnet et al [1], which uses the Bxb1 integrase system. The invertible promoter switch consists of a promoter flanked by att sites, with a transcription terminator upstream of the promoter. Expression of integrase alone or in concert with RDF inverts the switch through recombination of the att sites. The terminator guards against transcription of genes upstream of the promoter that should only be transcribed when the switch is in its opposite state (Figure 1). See the Experiment and Results pages for information on our implementation of invertible promoter switches and the integrase/RDF BioBricks we produced for working with the switches.

When the switch is in “PB” state, the promoter is flanked by attP and attB sites, and in “RL” state the promoter is flanked by attR and attL sites. Flipping from PB to RL is catalyzed by integrase and is irreversible in the presence of integrase alone. Flipping from RL to PB is catalyzed by integrase in conjunction with RDF, and is irreversible in the presence of these proteins together. For more on the functioning of integrase and RDF in site-specific recombination of att sites, see the appendix on Serine Integrsaes.

Since the switch in the RAD module was demonstrated to be functional [1], we replicated its architecture exactly in our switches. Intervening spacer sequences between functional elements are also the same in our switches.

Figure 1. The invertible promoter switch flips from PB to RL state in the presence of integrase, and from RL to PB state in the presence of integrase and RDF. Difference genes are expressed depending on the state of the switch.

It is important to note that any genes added to the prefix side of the switch must be inverted relative to the BioBrick convention, i.e. they are transcribed from suffix to prefix, rather than prefix to suffix. These inverted genes are expressed when the switch is in PB state.

These switches may be used as a form of passive memory storage; it holds its state after the stimulus (Int or Int+RDF) has passed. It does not require active gene expression to hold state, in contrast to switches that function through regulation of transcription (eg a mutual inhibition toggle switch [4]).

It should be noted that modification of a DNA message was accomplished by Bonnet et al in their March publication [2]. We were not aware of this until our project was nearly complete. However, our work toward modification of a DNA message was key to our project and did allow us to contribute new BioBricks to the registry.

It should also be noted that our BioBrick switch constructs contain an NheI restriction site outside of the functional switch portion of the BioBrick close to the prefix. This NheI site is an artifact of a design idea we had early in our project’s design phase and does not have a functional role. It is not expected to affect the functioning of the switch.

References

1. Bonnet J, Subsoontorn P, Endy D. Rewritable digital data storage in live cells via engineered control of recombination directionality. Proceedings of the National Acadamy of Science USA, 2012, 109(23) pp 8884-8889.

2. Bonnet J, Yin P, Ortiz ME, Subsoontorn P, Endy D. Amplifying genetic logic gates. Science, 2013, 340 (6132) pp 599-603.

3. Siuti P, Yazbek J, Lu TK. Synthetic circuits integrating logic and memory in living cells. Nature Biotechnology, 2013, 31 pp 448–452.

4. Gardner TS, Cantor CR, Collins JJ. Construction of a genetic toggle switch in Escherichia coli. Nature, 2000, 403(6767) pp 339-342.

PhiC31 and Bxb1 invertible promoter switches

EXPERIMENT

1. Constructs

1a. Multi-copy test constructs:

Multi-copy test constructs were constructed for the following promoter switches in their PB state (promoter switches flanked by attP and attB sites):

1. BBa_K1039001: Bxb1 Invertible Promoter Switch (Promoter J23119) – PB State

2. BBa_K1039008: PhiC31 Invertible Promoter Switch (Promoter J23119) – PB State

3. BBa_K1039009: PhiC31 Invertible Promoter Switch (Promoter J23118) – PB State

The gene for green fluorescent protein (GFP) was placed downstream of each promoter switch in its inverted orientation (BBa_K1039015). This allows for the promoter switch to transcribe GFP in its PB state. The gene for red fluorescent protein (RFP) (BBa_J04450) was placed upstream of each promoter switch. RFP was therefore only transcribed upon a directionally regulated site-specific recombination event on the attP and attB sites flanking the promoter switch. As mentioned before, this event is mediated by the corresponding PhiC31 or Bxb1 integrases and thereby results in the reversed orientation of the promoter (Fig. 1).

The multi-copy test constructs were assembled via 3A assembly (Parts Registry Assembly Protocol) and sub-cloned into a Parts Registry plasmid, pSB4A5. Each test construct is flanked by the standard iGEM suffix and prefix. The multi-test constructs made include:

1) Bba_K1039025: Test construct for Bxb 1 Invertible Promoter switch (Promoter J23119).

2) Bba_K1039027: Test construct for PhiC31 Invertible Promoter switch (Promoter J23119).

3) Bba_K1039028: Test construct for PhiC31 Invertible Promoter switch (Promoter J23118).

Figure 1. Invertible promoter switch test construct (PB state): The figure above shows the test constructs made for the PhiC31 and Bxb 1 invertible promoter switches in their PB state. The gene for GFP (with its ORF in a reverse orientation) was cloned downstream of each promoter switch. The gene for RFP was cloned upstream of each promoter switch. The invertible promoter switch is flanked by attP and attB sites (as represented by the triangles). The promoter within each promoter switch would drive the expression of GFP in the PB state. Test constructs for all Invertible promoter switches are flanked with iGEM Prefix and Suffix (as indicated by the box labeled “P” and “S”).

2b. Multi-copy Non-inducible Integrase-expressing constructs:

Two constructs for the constitutive expression of PhiC31 and Bxb1 integrases respectively, were assembled via Standard Assembly (Parts Registry Assembly Protocol). Genes encoding the PhiC31 and Bxb1 integrases (BBa_K1039012 and BBa_K1039003) were separately cloned downstream of a constitutive promoter and its corresponding ribosome binding site (K608002) in pSB1C3 (Parts Registry Standard vector) (Fig. 2). Each test construct is flanked by the standard iGEM suffix and prefix. The multi-test non-inducible integrase-expressing constructs include the following:

1) Bba_K1039030: PhiC31 integrase-expressing construct with a Promoter and RBS

2) Bba_K1039029: Bxb1 integrase-expressing construct with a promoter and RBS.

2c. Multi-copy Non-inducible Integrase and Recombination directionality factor- expressing constructs: Constructs were assembled to allow constitutive expression of both integrase and recombination directionality factor simultaneously from a single promoter. This construct was assembled via Standard Assembly (Parts Registry Assembly Protocol) for both PhiC31 and Bxb1 integrases/RDFs respectively (Fig. 2). Each construct is flanked by the standard iGEM suffix and prefix. The multi-test non-inducible integrase and RDF producing constructs include:

1) Bba_K1039014: PhiC31 integrase and RDF expressing construct from a single promoter.

2) Bba_K1039007: Bxb1 integrase and RDF expressing construct from a single promoter.

Figure 2. Multi-test non-inducible constructs for PhiC31 and Bxb1 integrase/RDF: (A.) Construct for non-inducible expression of integrase driven from a constitutive promoter and associated RBS for both PhiC31 and Bxb1 integrase (B.) Construct for non-inducible simultaneous expression of integrase and RDF from a constitutive promoter. Both genes have an associated RBS. All constructs are flanked with iGEM Prefix and Suffix (as indicated by the boxes labeled as “P” and “S”).

1. Experimental design for demonstrating the functionality of Bxb1 Invertible Promoter switch (Promoter J23119: PB state)

To demonstrate the functionality of the Bxb1 invertible promoter switch in its PB state, ~500 ng of the test construct was co-transformed into a competent E. coli strain (DH5α) with its corresponding constitutive Bxb1 integrase-expressing construct (~500 ng). For our negative control, ~500 ng of the Bxb1 test construct was transformed into E. coli in the absence of the integrase expressing construct The cells were subsequently plated on LB agar (with appropriate antibiotics to maintain both plasmids) and allowed to grow for two days at 37 degrees (~36h).The growth period was chosen to allow adequate expression of the integrase which would consequently mediate a recombination event resulting in the reversed orientation of the promoter (RL state) within each switch and thereby drive the expression of RFP.

Figure 3. Testing PhiC31 and Bxb1 Invertible promoter switches (PB state): To test the invertible promoter switches in their PB state (flanked by attP and attB), they were independently co-transformed with their corresponding non-inducible integrase-expressing constructs and consequently plated on LB agar with appropriate antibiotics to select for both plasmids. The resulting bacterial colonies were then analyzed for RFP expression.

2. Flow cytometry to demonstrate functionality of “inverted” GFP

The Bxb1 invertible promoter switch in its PB state drives the expression of GFP. A single GFP-expressing colony (from the Bxb1 invertible promoter switch test construct transformation plate) was selected and inoculated in 5mL of LB (with appropriate antibiotics) and grown overnight at 37°C. Since a modified version of GFP (ORF was modified to be in the opposite orientation with respect to convention) was used for these constructs, bacterial cells producing GFP (BBa_I20260) was used as one of the controls. Flow cytometry was then used to confirm GFP expression from all cultures

RESULTS AND ANALYSIS

Functionality of the Bxb1 Invertible promoter switch

Once the Bxb1 Invertible promoter switch test construct was co-transformed with its corresponding Bxb1 integrase-expressing construct, the resulting colonies were screened for RFP and GFP expression for both the co-transfomations and the negative control respectively. Figure 5b. represents the resulting colonies screened for RFP expression after the co-transfomation. All bacterial colonies were expressing detectable amounts of RFP. These colonies were also screened for GFP expression (Fig 5a.) and showed no fluorescent protein expression, thus confirming the integrase-mediated inversion of the Bxb1 promoter switch.

The negative control consisting of our single transformation of the Bxb1 promoter switch test construct in the absence of integrase. Figure 5c. and 5d. represent the resulting colonies when screened for GFP and RFP expression repectively. As expected, fig. 5d. confirmed no expression of RFP, however, GFP expression was also undetectable. To detect GFP from our control plates, a bacterial colony from the negative control transformation plate (fig. 5e.) was inoculated in culture for ~16 hours and centrifuged at 13,000 rpm for 1 minute. The allowed the bacterial cells to be concentrated into a pellet, which was consequently screened for GFP expression. As shown in Fig. 5f., the cell pellet showed detectable GFP expression when compared to its corresponding controls (RFP expressing bacterial cells and bacterial cells expressing no fluorescent protein). The observed GFP expression confirmed the proper functioning of our switch in its PB state.

Figure 5: Characterization of PhiC31 and Bxb 1 invertible promoter switches (PB state ): Bxb1 promoter switch test construct co-transformed with Bxb1 integrase expressing construct and viewed under (a) GFP filter (b) RFP filter; The Bxb1 test construct was transformed in the absence of Bxb1 integrase and viewed under (c) GFP filter (d) RFP filter (e) no filter; RFP expression was only seen in plate(b). (e) (f) shows a pellet of cells containing the Bxb1 test construct without the integrase expressing construct. When viewed under the GFP filter, the pellet fluoresced green when compared to its corresponding controls (cells not expressing a fluorescent protein, and cells expressing RFP)

Flow cytometry

The results for flow cytometry are shown in Figure 6. GFP expression from both cultures (Figure 6b. and 6c.) result in a shift in fluorescent populations when compared to control cells expressing no fluorescent protein (Figure 6a.). These results confirm the proper functioning of our modified GFP gene and furthermore, demonstrate the functionality of the promoter switches in their PB state.

Figure 6: Flow cytometry for confirming expression of inverted GFP (A) negative control: bacterial cells expressing no fluorescent protein (B) GFP (BBa_I20260) expressing bacterial cells (C) Bacterial cells expressing inverted GFP from the invertible promoter switch test construct. GFP expression from cultures (B) and (C) result in a shift in fluorescent populations when compared to control cells expressing no fluorescent protein (A). These results confirm the proper functioning of our modified GFP gene and furthermore, characterize the promoter switches in their PB state

MODELLING

Modelling the Dynamics of Bxb1-style and PhiC31-style Invertible Promoter Switch Systems

Invertible Promoter Switch “Styles”

We made invertible promoter switches based on the Bxb1 and PhiC31 integrase systems. In both of these systems Int dimerizes before attaching to att sites, and RDF modifies Int by binding it stoichiometrically (ie two RDF molecules bind each Int dimer). However, the details of the interaction between Int and RDF in these two systems is thought to differ. Notably, the Bxb1 RDF is thought to interact with Bxb1 Int only when Int is bound to DNA at the Bxb1 att sites [1] while PhiC31 RDF is also able to complex with Int in the cytosol independently of DNA binding [2] (Figure 1).

Figure 1. Bxb1 RDF can only complex with Int dimers that are bound to att sites, while PhiC31 RDF can complex with cytosolic Int dimers as well.

We refer to a switch system where RDF binds Int only when Int is already complexed with an att site as a “Bxb1-style” system, and we refer to a switch system where RDF can complex with Int independently of DNA as a “PhiC31-style” system. We sought to investigate the differences in the dynamics of these systems.

We first modeled switch systems where Int and RDF are driven by separate promoters (Figure 2). We should note that Bonnet et al did simulations describing the same switch systems in their original design of the RAD module [3].

Figure 2. We modeled the dynamics of Bxb1- and PhiC31-style promoter switch systems with Int and RDF driven by separate promoters.

Questioning the Model to Inform Model-based Design

We wanted to know the ranges of integrase and RDF expression that would flip switches of each style to identify potential differences in the ways they should be applied.

Model Construction

State variables (all time-dependent):

I = Integrase

R = RDF

mx = mRNA corresponding to protein X.

SPB = Switch in “PB” state, where we have attP and attB sites flanking the promoter

SRL = Switch in “RL” state, where we have attR and attL sites flanking the promoter

I2 = integrase dimer

I2SPB = integrase dimer bound to one att site in a PB state switch

I2RXSPB = integrase dimer bound to one att site in a PB state switch, with X RDF proteins also bound (X = 1 or 2)

I4SPB = one integrase dimer bound to each att site in the PB state switch

I4RXSPB = one integrase dimer bound to each att site in the PB state switch, with X RDF proteins also bound (X = 1 – 4)

In the PhiC31 style switch but NOT the Bxb1 style switch, the following complexes are also included:

I2Rx = X RDF proteins bound to an integrase dimer, X = 1 or 2

Parameters:

αI = transcription rate of Int

αR = transcription rate of RDF

δx = degradation rate of mRNA corresponding to protein X

βx = translation rate of protein X from mx

ρx = degradation rate of protein X

k1 = forward rate constant for association of a protein to a complex

k-1 = reverse rate constant for dissociation of a protein to a complex

krec = rate of switch flipping through recombination when bound to appropriate complex

Note that rate constants k1 and k-1 are assumed to be the same for formation of all complexes, since rate constants for the complexes are not known. krec is assumed to apply to flipping of the switch from PB to LR state and also from LR to PB state, when DNA is bound in the appropriate complexes.

Differential Equations: The differential equation for the abundance of an mRNA species is determined by transcription and degradation rates of mRNA:

The differential equation for the abundance of a complex X of I, R, and S accounts for the following factors:

If X is a monomer of I or R, it can be produced through translation and we add the term,

If X is formed from association of any complexes A and B in a reaction,

then we add the terms,

for association and dissociation.

If X can associate with a complex Y to form a complex Z in a reaction,

then we add the terms,

for association and dissociation.

If X is a complex that allows flipping of the switch (ie X is SPBI4 or SRLI4R4) then we add the term,

for production of X through switch flipping

Protein degradation in complexes is assumed such that individual proteins degrade at the same rate as they would outside of a complex. Degradation rate ρ is assumed to be equal for all monomers, so we assume that a complex X with N subunits degrades at a rate proportional to 1/N, since all subunits degrade together in the complex. For degradation of complexes, we add the term,

DNA is assumed to be conserved, without production or degradation. To allow protein complexes to degrade without losing DNA, degradation of any complex involving DNA is assumed to apply only to the protein; the DNA is left behind. For free DNA involved in complexes Ci, we add the term,

where Ni is the number of proteins in complex Ci.

Thus the general differential equation for abundance of a protein complex X is:

where n different protein pairs Ai and Bi can complex to form X, and X can complex with m different other complexes Yj to form m complexes Zj, and there are N proteins in complex X.

If X is a monomer of I or R then we also add the translation term, if X can be consumed or formed through switch flipping we add the appropriate production or consumption terms, and if X is naked DNA then we add terms for its formation through degradation of complexes.

Note that the only reactions involving switch flipping are:

Example:

For abundance of integrase dimer, which for a PhiC31 style system takes part in the interactions,

the differential equation is:

Estimation of Parameter Values

Promoter J32101 was reported to drive transcription at a rate of 0.03 PoPS (polymerases per second) [3]. We ranged our promoter strengths from 10-12 to 0.1 PoPS, as this was the range in which we saw interesting dynamics.

A previous study found that the average half-life of an mRNA is approximately 5 minutes [4]. We used this parameter for mRNA degradation in our model.

To find a reasonable range for translation rate, we referred to a study in which lacZ translation was found to range from roughly 15 to 50 translation initiation events per minute per mRNA [5]. We assumed translation occurred at a rate of 20 initiation events per minute per mRNA, and varied only transcription rate to change protein concentrations.

Based on a study of protein degradation rates [6], we produced protein degradation constants by assuming the half-life of all proteins to be 60 minutes.

For the forward and reverse rate constants, we did not have any data. We assumed k1, k-1, and krec all to be 1.

For all simulations discussed below, we ran the simulations with varying rate constant values to check if our assumptions had a significant impact on the results. We found that results were qualitatively similar.

Sensitivity Analysis of Bxb1- and PhiC31-style Switch Systems

We simulated the behavior of each switch with varying integrase and RDF promoter strengths. We used 50 switches in the simulations, and kept track of the percentage of switches that had flipped from PB to RL state at the system’s steady state (Figure 3). We also ran the simulations with different switch copy numbers and obtained qualitatively similar results.

Figure 3. Comparison of a) Bxb1-style and b) PhiC31-style switches flipping with various Int and RDF promoter strengths.

These graphs are qualitatively similar to those obtained by Bonnet et al using their simulation [3].

We note that the dynamics for very low Int and RDF promoter strengths (10-12 – 10-9 PoPS) are similar for the two switches, but that for higher promoter strengths they differ. In the Bxb1-style system RDF promoter strength above 10-9 PoPS rules out flipping of the switch, regardless of integrase promoter strength, while in the PhiC31-style system high RDF expression can be matched with high Int expression and the switch can still flip. Since the PhiC31-style system allows for RDF to complex with Int in solution, RDF can be sequestered by Int in solution. This allows for they dynamics we see in Figure 3.

To investigate the effects of stochastic processes, we implemented our differential equations model for the switch system stochastically using the Gillespie algorithm and varied Int and RDF promoter strengths. The shape of the parameter regions where Bxb1 and PhiC31 switches flipped was qualitatively similar to those found in the continuous model (Figure 4).

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Figure 4. Comparison of a) Bxb1-style and b) PhiC31-style switches flipping with various Int and RDF promoter strengths using a stochastic simulation.

We note that stochastic variation seems to have a particularly strong effect at low Int and RDF expression levels, as expected. In these simulations, it was particularly clear that the PhiC31-style switch was reliable over a more flexible range of expression levels.

Figure 4. Comparison of a) Bxb1-style and b) PhiC31-style switches flipping with various Int and RDF promoter strengths using a stochastic simulation.

Two-switch Systems

A promoter switch system may be subject to noise, where the switch temporarily flips accidentally due to a burst of integrase expression. If there are multiple copies of the switch present in the cell, there is a region of Int and RDF expression where some copies of the switch are flipped while others are not (the range of colours between blue and red in Figure 3). This behvaiour is undesirable, as it is not “switch-like”; since a switch is an all-or-nothing device, we would like our switches to function this way even when they are present in multiple copies.

We wanted to see if use of a two-switch system, wherein the first switch controls the second switch, could improve the “switch-like nature” of a multi-copy set of switches. If the first switch must flip to completion before the second switch flips, then the expression range where switching is only partial may be reduced in the second switch.

We modeled a two-switch system where the first switch controls expression of the Int and RDF corresponding to the second switch (RDF2 and Int2). RDF2 is expressed when switch 1 is in its initial PB state, and Int2 is expressed when switch 1 is flipped to RL state. The Int and RDF corresponding to switch 1 are now referred to as Int1 and Int2 (Figure 4).

Figure 4. A two-switch system where the second switch is controlled by the first switch.

Modelling the Two-switch System

In this system, we have state variables for Int2 and RDF 2 (I2 and R2), their mRNAs, and complexes involving them and the second switch. The two-switch system was modelled using the same principles described for the one-switch system, with one extra detail: the transcription rate of Int2 and RDF2 genes depends on the state of switch 1. We introduce the parameter,

αS1,

the transcription rate from the promoter in switch 1. The differential equations for Int2 and RDF2 mRNAs are given by:

where Pi are complexes involving S1 in PB state, and Rj are complexes involving switch 1 in RL state.

Sensitivity Analysis of a Two-switch System

We simulated the two-switch system with varying Int1 and RDF1 promoter strengths. We used 50 switches in the simulations, and kept track of the percentage of switches that had flipped from PB to RL state at the system’s steady state for each of the two switches. The strength of the switch 1 promoter was chosen such that the sum of expression from all the switch 1 promoters was equal to the expression from the Int1 promoter. This way, similar amounts of integrase are in the system for both switches. Figure 5 displays a two-switch system using two PhiC31-style switches. We selected the PhiC31-style switch because it offers control over switching at a wider range of parameter values than the Bxb1-style switch.

We also ran the simulations with different switch copy numbers, different switch 1 promoter strengths, and different combinations of switch 1 and switch 2 styles, and obtained qualitatively similar results.

Figure 5. Percentages of promoters flipped for a) Switch 1 and b) Switch 2 in a two-switch system. Both switches were PhiC31-style in this case.

We see that the ranges of Int1 and RDF1 promoter strengths corresponding to incomplete switch flipping (the colours between blue and red in Figure 5) are narrower for switch 2 (Figure 5b) than for switch 1 (Figure 5a). This simulation suggests that controlling one switch with another helps make a multi-switch system more switch-like, with flipping of all copies at once.

To investigate the effects of stochastic processes, we implemented our differential equations model for the two-switch system stochastically using the Gillespie algorithm and varied Int and RDF promoter strengths. The shape of the parameter regions where each switch flipped switches flipped were qualitatively similar to those found in the continuous model (Figure 7).

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Figure 7. Percentages of promoters flipped for a) Switch 1 and b) Switch 2 in a two-switch system simulated stocahstically. Both switches were PhiC31-style in this case.

We note a clear reduction in the variability of the percentage of switches flipped in switch 2 compared to switch 1. By controlling switch 2 with switch 1, we have reduced the stochastic nature of switch 2 to give it more robust switch-like behavior. This complements the finding using the continuous model, which showed that a two-switch system narrowed the parameter range corresponding to partial flipping of switch 2.

Conclusions

Our goal is to use modelling to inform design decisions in a model-based design approach. The following points are relevant to the application of these invertible promoter switches:

• Our single-switch model indicates that a Bxb1-style switch has a threshold of RDF expression above which no flipping can occur, regardless of Int expression, while a PhiC31-style switch can be flipped at any RDF expression level as long as Int expression is high enough.

  • o The PhiC31-style switch is thus more flexible in different situations that may require performance at different ranges of Int and RDF expression levels.
  • o The Bxb1-style switch has the advantage that switching can generally be controlled by varying one parameter, RDF expression, past a specific threshold.
• • Our two-switch model shows that switch-like behavior of a switch present in multiple copies is improved if it is controlled by a second switch; the two-switch system corresponds to a narrower range of parameter values that permit partial switching.

References

1. Ghosh P, Wasil LR, Hatfull GF. (2006) Control of phage Bxb1 excision by a novel recombination directionality factor. PLoS Biology, 2006, 4:e186.

2. Khaleel T, Younger E, McEwan AR, Varghese AS, Smith MCM. A phage protein that binds PhiC31 integrase to switch its directionality. Molecular microbiology, 2011, 80(6), 1450-63.

3. Bonnet J, Subsoontorn P, Endy D. Rewritable digital data storage in live cells via engineered control of recombination directionality. Proceedings of the National Acadamy of Science USA, 2012, 109(23) pp 8884-8889.

Retransmit

Retransmitting a Modified DNA Message

Retransmitting a Modified DNA Message

DESIGN

In intercellular communication systems, it may be useful for receiver cells to be able to retransmit a message, especially if they are able to modify the message.

We sought to combine control over transmission and modification of a DNA message to allow receiver cells to modify an original DNA message (Mori) to produce a modified DNA message (Mmod) and then retransmit it.

In the scheme we developed (Figure 1), three populations of E. coli are co-cultured (Figure 1):

• 1.The sender population transmits the original DNA message, Mori.
  • Mori includes an invertible promoter switch. When flipped, it leads to production of M13 gene VIII and T7 RNA polymerase (RNAP) from Mmod
• 2.The F+ primary receiver population receives Mori, and in the presence of a particular stimulus modifies and retransmits it.
  • Primary receivers carry a helper plasmid with gene VIII knockout, as well as inducible integrase expression
  • Upon induction of integrase, the switch on the Mori messaging phagemid flips and gene VIII expression complements the helper plasmid, allowing retransmission of Mmod.
• 3.The F+ secondary receiver population exhibits a detectable behavior (ie fluorescent protein production) upon receipt of Mmod.
  • T7 RNAP expression from Mmod causes expression of a fluorescent protein in secondary receivers.

Note that secondary receivers may also receive Mori, since all three populations are present in co-culture, and that primary receivers can also receive M¬mod.

Figure 1. The sender population transmits a DNA message, which is modified and retransmitted by primary receivers in the presence of a particular stimulus. The secondary receiver population exhibits a detectable behavior (eg fluorescent protein expression) when it receives the modified message.

The dynamics and potential modes of failure of this system are explored through mathematical modeling.

MODELLING

We sought to combine controlled transmission and modification of a DNA message to design a system wherein receiver cells are able to modify a DNA message by flipping an invertible promoter switch and then retransmit the modified message. The system is summarized in Figure 1 (see the Design section for more details).

Figure 1. The sender population transmits a DNA message, which is modified and retransmitted by primary receivers in the presence of a particular stimulus. The secondary receiver population exhibits a detectable behavior (eg fluorescent protein expression) when it receives the modified message.

The key output of this system is secondary receiver cells that have received a modified message and are fluorescing. This output can be evaluated in two different scenarios, where modification and retransmission are:

Induced: We want as many secondary receivers as possible carrying the modified message.
Uninduced: We want as few secondary receivers as possible carrying the modified message.

This system may be prone to several undesired outcomes (Figure 2) outlined below:

1. False negative: Since populations are co-cultured, secondary receivers can also receive the original unmodified message (M¬ori) that is transmitted by sender cells. When modification and retransmission are induced, there is a risk that secondary receivers might still be overwhelmed with Mori, preventing them from receiving the modified message (M-mod¬). This could result from the following modes of failure:

• The majority of secondary receivers become infected with Mori during the lag time between Mori and Mmod production.
  • M¬ori is present in the system first, since Mmod must be produced from Mori.
  • There is an additional lag in Mmod production caused by the “turnaround time” between receipt of a DNA message and accumulation of copy number and protein concentration that can fuel packaging and transmission of the modified message
• Inefficiency of production of Mmod vs Mori due to genomic tinkering keeps concentration of Mmod low compare to Mori.
  • We have picked apart the M13 genome in order to allow control over production of viral particles in primary receivers. This may result in lowered efficiency of particle production in primary receivers compared to senders.

2. False positive: When retransmission and modification are not induced, basal expression of integrase due to promoter leakiness may cause unwanted flipping of the switch, producing Mmod and leading to retransmission. Since a switch holds state after flipping, such an error would be irreversible. This problem is exacerbated by the fact that the resulting unwanted message transmission would spread to other cells. Such amplification of error could make the system effectively constitutive, even though an inducible promoter is employed.

Questioning our Model

Our goal in modeling is model-based design. We would like to use our model to guide design choices and direct lab efforts. Before producing the model, we made the following list of questions on which we hoped the model would shed light. In each case, the measure of system performance is the ratio of secondary receivers that receive Mmod vs Mori:

• What is the sensitivity to reduced viral particle production efficiency in primary receivers, leading to weaker transmission of Mmod?
• How does the ratio of initial populations (senders : primary receivers : secondary receivers) affect the output of the system?
• What is the sensitivity to basal (uninduced) expression of integrase in primary receivers, leading to unwanted modification and retransmission, especially given amplification of error through retransmission?

To cut to the chase, skip to the “conclusions” section! If you like details, keep reading here.

Model Construction

We used the following set of state variables and parameters to model the spread of M¬ori¬ and Mmod¬ through the co-cultured populations:

State variables:

Ps(t) = population of sender cells

P1 uninfected(t) = population of primary receiver cells that have not received a DNA message

P2 uninfected(t) = population of secondary receiver cells that have not received a DNA message

P1 ori(t) = population of primary receiver cells carrying the original DNA message

P2 ori (t) = population of secondary receiver cells carrying the modified DNA message

P1 mod (t,a) = population of primary receiver cells of age a at time t. Age a corresponds to how long the cell has been a P1 mod cell. This is important to know because of the turnaround time between receipt of a DNA message and accumulation of phagemid and protein to fuel viral particle production.

P2 mod(t) = population of secondary receiver cells carrying the modified DNA message

Mori (t) = concentration of M13 viral particles carrying the original unmodified DNA message

M¬mod (t) = concentration of M13 viral particles carrying the modified DNA message

Parameters:

c = carrying capacity of the liquid growth medium

g = maximum growth rate of cells that are not producing viral particles in absence of pressure due to carrying capacity

r = maximum growth rate of cells that are producing viral particles as a fraction of g

k = adsorption rate of viral particles to cells

i = induction constant. i = 1 when message modification and retransmission is induced, and i = 0 when it is not induced

S(i) = rate of switch flipping per primary receiver carrying Mori. Depends on induction.

p = rate of viral particle production through intact M13 cistrons on the helper plasmid

b = the percent rate, relative to p, of viral particle production in primary receiver cells through complemented helper plasmid with gene VIII knockout. Assumed between 0 and 1.

j1 = age at which viral particle production begins in P1 mod cells. Production starts at zero at age j1 and increases.

j2 = age at which viral particle production reaches its maximum and continues to maintain a steady rate

When modeling the growth of each population over the course of the experiment, the carrying capacity of the media had to be considered. To simplify our equations, we define:

We built a differential equations (DEs) model of the system using these state variables and parameters, which is described below.

The DE for senders is a simple logistic growth equation:

where the growth rate G is modified by r since senders produce viral particles and therefore grow more slowly. The DEs for P1 uninfected and P2 uninfected incorporate loss due to receipt of DNA messages, and the DE for P¬2 ori incorporates production due to receipt of DNA message:

Since there is a turnaround time associated with receipt, modification, and retransmission of a DNA message in primary receivers, the “age” of P1 mod¬ cells – the time since they became capable of producing viral particles – is an important aspect of the system state for determining the rate of production of M¬mod. We are therefore interested in P1 mod (t,a), where a is age, and we must use a partial differential equation to keep track of this state variable. The boundary condition for the PDE accounts for P1 mod(t,0), the production of P1 mod through flipping of the switch in P1 ori cells as well as direct infection by M1 mod.

Production of Mori by senders occurs through intact helper plasmid at rate p,

The rate of production of Mmod by P1 mod cells requires consideration of the age of the cells. The rate of production of Mmod by cells of a given age is defined by the function

where viral particle production by a P1 mod cell begins at age j1 and increases with age to maximal production rate bp at age j2, where 0 < b < 1. Production in these cells is likely slower than the rate in senders, p, because one of the M13 cistrons has been picked apart to allow complementation.

The production rate of M¬mod at time t is determined from the distribution of P1 mod cells over all ages and the rate of particle production at each age through a convolution:

Estimation of Model Parameter Values

In the absence of precise parameter values, a literature review provided reasonable ranges for the parameters that allowed us to make qualitative statements about the behavior of the system.

• • Carrying capacity c was estimated as 1.5*109 cells/ml [1].
• Maximal growth rate g was estimated as ln(2)/20 – ln(2)/30 / min, for a doubling time between 20 and 30 minutes.
• Maximal growth rate of cells producing viral particles was taken as 1/4g – 1/2g [2].
• Adsorption rate k of viral particles to cells was taken as 3*10-11 ml/min [3]. .
• The rate of viral particle production p from the intact helper plasmid was taken to be 33 particles per cell per minute [4].
• The age at which viral particle production begins in P1 mod cells, j¬1, and the age at which viral particle production reaches a maximum, j2, are assumed to be similar to that of wildtype M13, and these values are taken to be 10 minutes and 40 minutes respectively [4]
• The rate of viral particle production through complemented helper plasmid with gene VIII knockout is taken as bp, where p is the rate of production from intact helper plasmid and b is between 0 and 1. It is assumed that picking apart the M13 genome for purposes of complementation will either reduce efficiency or have no effect.
• The age at which viral particle production begins in P1 mod cells, j¬1, and the age at which viral particle production reaches a maximum, j2, are assumed to be similar to that of wildtype M13, and these values are taken to be 10 minutes and 40 minutes respectively [4].

Sensitivity to Reduced Efficiency of Viral Particle Production

Since we are using gene VIII complementation of the helper plasmid to control viral particle production in primary receivers, it is likely that efficiency of production will be reduced in these cells relative to senders. We asked the model, “What is the sensitivity to reduced efficiency viral particle production in primary receivers, leading to weaker transmission of Mmod?”

Sensitivity to Reduced Efficiency of Viral Particle Production

Since we are using gene VIII complementation of the helper plasmid to control viral particle production in primary receivers, it is likely that efficiency of production will be reduced in these cells relative to senders. We asked the model, “What is the sensitivity to reduced efficiency viral particle production in primary receivers, leading to weaker transmission of Mmod?”

We simulated the system with varying values of b, the percent rate of viral particle production, relative to p, in primary receiver cells. The system performance measure was the ratio of secondary receivers that receive Mmod vs Mori, expressed as P¬2 mod/P2 ori, measured at the time when all secondary receiver cells have received a message (Figure 3). For these simulations, modification and retransmission were induced, and the half-life of the switch in the original message, once received by primary receivers, was assumed to be 30 mins. Initial populations of senders, primary receivers, and secondary receivers were assumed to be 1000, 10000, and 1000 cells/mL respectively.

Figure 3. Effects of genomic tinkering on system outputs. The viral particle production efficiency in primary receivers, as a percentage of efficiency in senders, plotted against the ratio of secondary receivers that received Mmod vs Mori.

We see that sensitivity is highest when efficiency is low. As expected, increasing efficiency always leads to more successful delivery of M¬mod to secondary receivers. For the parameters used, an efficiency of approximately 0.3 is required for an equal proportion of P2 mod and P2 ori.

Effect of Initial Populations on Receipt of Modified Message

The aim of this system is receipt of Mmod by secondary receiver cells. Mmod can only be produced by primary receivers that are infected with Mori. In a sense, the message must “pass through” the primary receivers before it can get to the secondary receivers. We thus suspect that an increase in the initial population of primary receivers will improve system performance.

To confirm this, we simulated the system with varied initial populations for senders and primary receivers. The system performance measure was the ratio of secondary receivers that receive Mmod vs Mori, expressed as P¬2 mod/P2 ori, measured at the time when all secondary receiver cells have received a message. We also kept track of the simulated time required (Figure 4). For these simulations, modification and retransmission were induced; the half-life of the switch in the original message, once received by primary receivers, was assumed to be 30 mins; and the rate of particle production in primary receivers was assumed to be 50% of the rate in senders.

As expected, we note that the system is most efficient in delivering Mmod to secondary receivers when the primary receiver population is high and the sender population is low, so that the majority of phage particles carry Mmod. We also note that very low initial populations of senders results in a longer time taken to “infect” all secondary receivers with DNA messages. Sensitivity to Basal Expression of Integrase in Primary Receivers A key concern in this system is the reliability of the switch on the messaging phagemid. The switch must flip when modification and retransmission is induced, but must hold state when uninduced. When induced, we expect the half-life of the switch in the original message, once received by primary receivers, to be short. In absence of induction, we expect half-life to be long. We simulated the system with varying switch half-life, for short and long half-life (Figure 5 a and b). The system performance measure was the ratio of secondary receivers that receive Mmod vs Mori, expressed as P¬2 mod/P2 ori, measured at the time when all secondary receiver cells have received a message. For these simulations modification and retransmission were induced, and the rate of particle production in primary receivers was assumed to be 50% of the rate in senders. Initial populations of senders, primary receivers, and secondary receivers were assumed to be 1000, 10000, and 1000 cells/mL respectively.

As expected, we note that the system is most efficient in delivering Mmod to secondary receivers when the primary receiver population is high and the sender population is low, so that the majority of phage particles carry Mmod. We also note that very low initial populations of senders results in a longer time taken to “infect” all secondary receivers with DNA messages.

Sensitivity to Basal Expression of Integrase in Primary Receivers

A key concern in this system is the reliability of the switch on the messaging phagemid. The switch must flip when modification and retransmission is induced, but must hold state when uninduced.

When induced, we expect the half-life of the switch in the original message, once received by primary receivers, to be short. In absence of induction, we expect half-life to be long.

We simulated the system with varying switch half-life, for short and long half-life (Figure 5 a and b). The system performance measure was the ratio of secondary receivers that receive Mmod vs Mori, expressed as P¬2 mod/P2 ori, measured at the time when all secondary receiver cells have received a message. For these simulations modification and retransmission were induced, and the rate of particle production in primary receivers was assumed to be 50% of the rate in senders. Initial populations of senders, primary receivers, and secondary receivers were assumed to be 1000, 10000, and 1000 cells/mL respectively.

Figure 5. Switch half-life in primary receivers plotted against the ratio of secondary receivers that received Mmod vs Mori for a) short and b) long half-life.

We note that sensitivity to switch half-life is high for short half-life and low for long half-life. Increasing responsiveness of the switch to induction gives increasingly better returns by increasing receipt of Mmod, while reducing unwanted switch flipping due to basal integrase expression gives diminishing returns in preventing receipt of Mmod¬.

Conclusions

Analysis of our model indicates that,

• Delivery of Mmod to secondary receivers is most sensitive to reduced M¬mod transmission efficiency that may result from tinkering with the M13 genome when efficiency is very low. As expected, increasing Mmod transmission efficiency improves delivery of Mmod.
• A large initial population of primary receivers with a low initial population of senders improves delivery of Mmod.
• Most interestingly, we find that sensitivity of Mmod delivery to switch flipping rate is greatest for fast switch flipping rates and least for slow switch flipping rates. This indicates that improved responsiveness of switch flipping to induction produces increasing returns for system performance, but that improved resistance to uninduced switch flipping to prevent unwanted modification and retransmission produces diminishing returns.

The most significant result for design of this system is the sensitivity of M¬mod delivery to the rate of switch flipping. Leaky expression of integrase in primary receivers must be effectively avoided. Extra measures to prevent leak, such as the T3 promoter system or post-transcriptional regulation, should be employed. In light of the issues with basal integrase expression, this system is most ideal in situations where background levels of modification and retransmission are tolerable.

References:

1. Ausubel FM, Brent R, Kingston RE, Moore DD, Seidman JG, Smith JA, Struhl K. Short Protocols in Molecular Biology. 5th ed. Vol. 1 pp. 1-6.

2. Sambrook J, Russell DW. Molecular Cloning: A Laboratory Manual. 3rd edition. Cold Spring Harbor Laboratory Press, Cold Spring Harbour, NY. 2001.

3. Tzagoloff H, Pratt D. 1964. The initial steps in infection with coliphage M13. Virology. 24:3 pp 372-380.

4. Clackson T, Lowman HB. Phage Display: A Practical Approach. Oxford University Press, NY. 2006.

Ottawa's Collaboration

1. Wet Lab Collaboration with uOttawa iGEM

The following constructs were designed by uOttawa iGEM for Waterloo iGEM

• Inducible Integrase-Expressing constructs

The inducible expression of the PhiC31 and Bxb1 integrase genes was designed by incorporating riboregulators that allow post-transcriptional control of gene expression. Riboregulators include two RNA elements; a cis repressor molecule (also known as the Ribo “lock”) and a trans-activating RNA molecule (also known as the Ribo “Key”). The cis-repressor element is a sequence complementary to the RBS associated with the gene whose expression needs to be silenced (that would be the integrase gene in this case). To control expression of the integrase gene, a Ribo lock was designed to be cloned directly upstream of the integrase gene and its associated RBS (5’ UTR). The sequence encoding the Ribo lock, integrase and its associated RBS as well as a lacI cassette (BBa_Q04121) were to be placed under the control of a strong constitutive promoter (J23119). The trans-activating Ribo key (BBa_I714036) on the other hand, was cloned in the 3’ UTR of the construct and was placed under the control of the PLlac 0-1 promoter (BBa_R0011), which is both LacI-repressible and IPTG-inducible. Simultaneous expression of the LacI cassette and the integrase from a single promoter would ensure repression of Ribo key from the LacI promoter and therefore results in a controlled expression of both PhiC31 and Bxb1 integrase (Isaac et al. 2004).

• Inducible Integrase and Recombination Directionality factor expressing constructs:

Inducible PhiC31 and Bxb1 Integrase and Reverse Directionality factor expressing constructs were also designed via the use of riboregulators. As mentioned in section above, controlled expression of integrase would be achieved via a cis-repressor Ribo Lock and a trans-activating Ribo Key. The design of these constructs required the gene for RDF to be cloned under the control of a LacI promoter (BBa_R0011) with a Ribo lock cloned directly upstream (5’ UTR). For simultaneous expression of both integrase and RDF, the inducible integrase-expressing construct described above was modified to also contain RDF under inducible conditions (Isaac et al. 2004).

Figure 1. (A.) Inducible PhiC31 and Bxb1 Integrase-expressing construct: The gene for each integrase is placed under the control of a strong constitutive promoter (J23119) with a Ribo “lock” directly upstream. The trans-activating Ribo “key” is placed downstream under the control of a PLlac 0-1 promoter. The lacI cassette is also placed under the control of promoter J23119. (B.) Inducible PhiC31 and Bxb1 integrase and recombination directionality factor-expressing construct: The genes for the integrase and the RDF were placed directly downstream from their corresponding ribo “lock” with the integrase under the control of a strong constitutive promoter and the RDF under the control of a PLlac 0-1 promoter. The lacI cassette (BBa_Q04121) was placed under the control of a strong constitutive promoter (J23119).

2. Modelling Collaboration with uOttawa iGEM

We constructed a differential equations model to describe uOttawa’s feed-forward fold-change detection system and implemented it in MATLAB. Parameterization and analysis of the model was performed by uOttawa.

Videos

Accomplishments

The Waterloo iGEM team accomplished several milestones:

1. The Waterloo iGEM Wet Lab team submitted several Biobricks to the expanding repository of Parts Registry out of which 5 were characterized. Out of the Biobricks submitted, 2 sets of invertible promoter switches, dependent upon viral recombination mechanisms, were submitted. These invertible promoter switches have also been assembled into testable constructs, which utilize different fluorescent proteins for an easy detectable system. These promoter switches can be used to create more complex systems of communication between bacterial cells as opposed to a standard AHL-based system. Each invertible promoter switch has been submitted with its corresponding Integrases and Recombination Directionality factors. Other Biobricks include HPdO, HPdO with deleted gene VIII, and gene VIII. These Biobricks contain M13 viral genes but without the M13 packaging sequence.
2. The mathematical modeling team modeled the dynamics of Bxb1-style and PhiC31-style invertible promoter switch system and controlled modification and retransmission of a DNA message using differential equations, estimating parameter values and analyzing through simulations.
3. The Waterloo iGEM HP team created an educational series called TIL:Synthetic Biology. These TIL episodes teaches interested viewers from the concepts of Synthetic Biology, fundamentals of DNA messaging (Waterloo iGEM’s main project), to perception of Synthetic Biology of the students, academics and philosophy professors.
4. The Waterloo Human Practices team successfully hosted an “Intent to Invent” seminar, where 3 key speakers gathered together to inform students about the applications of synthetic biology in diverse disciplines
5. The Waterloo Human Practices team created a sandbox/launch pad for Biotechnology and Synthetic Biology start-ups known as “Velocity Science”. This marks the beginning of a biotechnology-driven entrepreneurship opportunity targeting primarily the students at the University of Waterloo.

Future Aspirations

Potentially, E.coli strains such as HB101, do not have a prophage at the phi80 locus in their chromosome and could be used in a single copy as opposed to a multi-copy test. Transformation of the pInt80-649 helper plasmid harbouring a temperature sensitive replication of origin (SCI101) and the lambda pir gene into HB101 competent cells can be done. These cells are made competent again for a subsequent co-transformation. Additionally, the switch constructs can be subcloned into an integrative plasmid, pBBIntPhi-J23118, that has a lambda pir origin of replication (R6K). The HB101 competent cells containing pInt80-649, can thus be transformed with sub cloned pBBIntPhi-J23118 and the switch constructs. This will assure the propagation of the integrative plasmid as it has the lambda pir protein being made from the helper plasmid all in one culture. Clones can then be grown in liquid media at 37°C to inactivate the temperature sensitive plasmid. Clones will then be further streaked purified and grown at 43°C over night. This would select for clones that should theoretically only have the integrative plasmid containing the insert of interest (the switches flanked by reporter genes). To confirm proper integration of insert into the bacterial chromosome, colony PCR could be performed on several clones of each switch construct. Ideally, using the primers provided by Zucca et al., 2013 would help anneal upstream of the Phi80 chromosomal attB site and in the R6K origin respectively. The expected amplicon would be 452bp and would indicate the correct integration position, while negative clow would show no amplicon. Additionally, to identify multiple tandem copies of the integrated switches flanked with reporter genes, can be achieved by using another pair of primers also provided by Dr. Zucca. This pair of primers would anneal in opposite directions in the R6K origin of replication and in the upstream region of the cloning sire. A 572 bp amplicon would prove that atleast two tandem copies are present in the genome.

Experiments

Characterization of PhiC31 and Bxb1 invertible promoter switches

Overview

PhiC31 and Bxb1 invertible promoter switches were characterized using test constructs which utilize differential fluorescent protein expression to distinguish between the directionality of the promoter in its PB state and the RL states. These two states are determined by the expression of phage-derived site-specific recombinase, integrase, as well as their corresponding recombination directionality factor or RDF. As outlined in the project description, PhiC31 or Bxb1 integrase alone mediates site-specific recombination between attachment (att) sites (attP and attB sites specifically), flanking each promoter switch in its PB state. Upon recombination, the orientation of the promoter within each switch is reversed and the switch is now in its RL state due to the production of recombinant product sites, attR and attL. Once the switch is in its RL state, expression of PhiC31 and Bxb1 RDFs ,in conjunction with PhiC31 and Bxb1 integrase expression, allows the reversal of the invertible promoter switch back to its PB state. The RDF reverses the directionality of the site-specific recombination mediated by integrase. Designing constructs which allow the expression of specific fluorescent proteins depending on the direction of the promoter within each promoter switch, helped the development of an easy detectable system for characterizing both the PhiC31 and Bxb1 promoter switches as well as their corresponding integrases and RDFs. The characterization of PhiC31 and Bxb1 promoter switches was carried in the presence of both inducible and non-inducible expression of PhiC31 and Bxb1 integrases and RDFs.

Experimental design

BioBricks

1b. Inverted GFP Biobrick:

This is a GFP sequence with the ORF identical to the sequence of (BBa_E0040), but it is oriented opposite to the BioBrick convention. That is, it is read from suffix to prefix, and the coding strand is the opposite strand compared to most BioBricks. This can be expressed from a similarly “inverted” promoter. The Inverted GFP biobrick consists of the inverted GFP with an upstream RBS. The construct is flanked by iGEM Prefix and Suffix. This Biobrick was characterized by placing it upstream of each PhiC31 and Bxb 1 invertible promoter switch (PB state). GFP expression was confirmed using flow cytometry. For information on the characterization of this biobrick, refer to “Characterization of PhiC31 and Bxb 1 invertible switch constructs (PB state)” in the results sections.

Constructs

2a. Multi-copy test constructs:

Multi-copy test constructs were constructed for the following promoter switches in their PB state (promoter switches flanked by attP and attB sites):

1. BBa_K1039001: Bxb1 Invertible Promoter Switch (Promoter J23119) – PB State

2. BBa_K1039008: PhiC31 Invertible Promoter Switch (Promoter J23119) – PB State

3. BBa_K1039009: PhiC31 Invertible Promoter Switch (Promoter J23118) – PB State

The gene for green fluorescent protein (GFP) was placed downstream of each promoter switch in its inverted orientation (BBa_K1039015). This allows for the promoter switch to transcribe GFP in its PB state. The gene for red fluorescent protein (RFP) (BBa_J04450) was placed upstream of each promoter switch. RFP was therefore only transcribed upon a directionally regulated site-specific recombination event on the attP and attB sites flanking the promoter switch. As mentioned before, this event is mediated by the corresponding PhiC31 or Bxb1 integrases and thereby results in the reversed orientation of the promoter (Fig. 1).

The multi-copy test constructs were assembled via 3A assembly (Parts Registry Assembly Protocol) and sub-cloned into a Parts Registry plasmid, pSB4A5. Each test construct is flanked by the standard iGEM suffix and prefix. The multi-test constructs made include:

1) Bba_K1039025: Test construct for Bxb 1 Invertible Promoter switch (Promoter J23119).

2) Bba_K1039027: Test construct for PhiC31 Invertible Promoter switch (Promoter J23119).

3) Bba_K1039028: Test construct for PhiC31 Invertible Promoter switch (Promoter J23118).

Figure 1. Invertible promoter switch test construct (PB state): The figure above shows the test constructs made for the PhiC31 and Bxb 1 invertible promoter switches in their PB state. The gene for GFP (with its ORF in a reverse orientation) was cloned downstream of each promoter switch. The gene for RFP was cloned upstream of each promoter switch. The invertible promoter switch is flanked by attP and attB sites (as represented by the triangles). The promoter within each promoter switch would drive the expression of GFP in the PB state. Test constructs for all Invertible promoter switches are flanked with iGEM Prefix and Suffix (as indicated by the box labeled “P” and “S”).

2b. Multi-copy Non-inducible Integrase-expressing constructs:

Two constructs for the constitutive expression of PhiC31 and Bxb1 integrases respectively, were assembled via Standard Assembly (Parts Registry Assembly Protocol). Genes encoding the PhiC31 and Bxb1 integrases (BBa_K1039012 and BBa_K1039003) were separately cloned downstream of a constitutive promoter and its corresponding ribosome binding site (K608002) in pSB1C3 (Parts Registry Standard vector) (Fig. 2). Each test construct is flanked by the standard iGEM suffix and prefix. The multi-test non-inducible integrase-expressing constructs include the following:

1) Bba_K1039030: PhiC31 integrase-expressing construct with a Promoter and RBS

2) Bba_K1039029: Bxb 1 integrase-expressing construct with a promoter and RBS.

2c. Multi-copy Non-inducible Integrase and Recombination directionality factor- expressing constructs:

Constructs were assembled to allow constitutive expression of both integrase and recombination directionality factor simultaneously from a single promoter. This construct was assembled via Standard Assembly (Parts Registry Assembly Protocol) for both PhiC31 and Bxb1 integrases/RDFs respectively (Fig. 3). Each construct is flanked by the standard iGEM suffix and prefix. The multi-test non-inducible integrase and RDF producing constructs include:

1) Bba_K1039014: PhiC31 integrase and RDF expressing construct from a single promoter. Both genes have an associated ribosome binding site.

2) Bba_K1039007: Bxb 1 integrase and RDF expressing construct from a single promoter. Both genes have an associated ribosome binding site.

Figure 2. Multi-test non-inducible constructs for PhiC31 and Bxb1 integrase/RDF: (A.) Construct for non-inducible expression of integrase driven from a constitutive promoter and associated RBS for both PhiC31 and Bxb1 integrase (B.) Construct for non-inducible simultaneous expression of integrase and RDF from a constitutive promoter. Both genes have an associated RBS. All constructs are flanked with iGEM Prefix and Suffix (as indicated by the boxes labeled as “P” and “S”).

1. Experimental design for demonstrating the functionality of Bxb1 Invertible Promoter switch (Promoter J23119: PB state)

To demonstrate the functionality of the Bxb1 invertible promoter switch in its PB state, ~500 ng of the test construct was co-transformed into a competent E. coli strain (DH5α) with its corresponding constitutive Bxb1 integrase-expressing construct (~500 ng). For our negative control, ~500 ng of the Bxb1 test construct was transformed into E. coli in the absence of the integrase expressing construct The cells were subsequently plated on LB agar (with appropriate antibiotics to maintain both plasmids) and allowed to grow for two days at 37 degrees (~36h).The growth period was chosen to allow adequate expression of the integrase which would consequently mediate a recombination event resulting in the reversed orientation of the promoter (RL state) within each switch and thereby drive the expression of RFP. Bacterial colonies grown on agar plates after two days were analyzed for RFP expression.

Figure 3. Testing PhiC31 and Bxb1 Invertible promoter switches (PB state): To test the invertible promoter switches in their PB state (flanked by attP and attB), they were independently co-transformed with their corresponding non-inducible integrase-expressing constructs and consequently plated on LB agar with appropriate antibiotics to select for both plasmids. The resulting bacterial colonies were then analyzed for RFP expression.

2. Flow cytometry to demonstrate functionality of “inverted” GFP

The Bxb1 invertible promoter switch in its PB state drives the expression of GFP. A single GFP-expressing colony (from the Bxb1 invertible promoter switch test construct transformation plate) was selected and inoculated in 5mL of LB (with appropriate antibiotics) and grown overnight at 37°C. Since a modified version of GFP (ORF was modified to be in the opposite orientation with respect to convention) was used for these constructs, bacterial cells producing GFP (BBa_I20260) was used as one of the controls. Flow cytometry was then used to confirm GFP expression from all cultures

Results and Conclusions

Functionality of the Bxb1 Invertible promoter switch

Once the Bxb1 Invertible promoter switch test construct was co-transformed with its corresponding Bxb1 integrase-expressing construct, the resulting colonies were screened for RFP and GFP expression for both the co-transfomations and the negative control respectively. Figure 5b. represents the resulting colonies screened for RFP expression after the co-transfomation. All bacterial colonies were expressing detectable amounts of RFP. These colonies were also screened for GFP expression (Fig 5a.) and showed no fluorescent protein expression, thus confirming the integrase-mediated inversion of the Bxb1 promoter switch.

The negative control consisting of our single transformation of the Bxb1 promoter switch test construct in the absence of integrase. Figure 5c. and 5d. represent the resulting colonies when screened for GFP and RFP expression repectively. As expected, fig. 5d. confirmed no expression of RFP, however, GFP expression was also undetectable. To detect GFP from our control plates, a bacterial colony from the negative control transformation plate (fig. 5e.) was inoculated in culture for ~16 hours and centrifuged at 13,000 rpm for 1 minute. The allowed the bacterial cells to be concentrated into a pellet, which was consequently screened for GFP expression. As shown in Fig. 5f., the cell pellet showed detectable GFP expression when compared to its corresponding controls (RFP expressing bacterial cells and bacterial cells expressing no fluorescent protein). The observed GFP expression confirmed the proper functioning of our switch in its PB state.

Figure 5: Characterization of PhiC31 and Bxb 1 invertible promoter switches (PB state ): Bxb1 promoter switch test construct co-transformed with Bxb1 integrase expressing construct and viewed under (a) GFP filter (b) RFP filter; The Bxb1 test construct was transformed in the absence of Bxb1 integrase and viewed under (c) GFP filter (d) RFP filter (e) no filter; RFP expression was only seen in plate(b). (e) (f) shows a pellet of cells containing the Bxb1 test construct without the integrase expressing construct. When viewed under the GFP filter, the pellet fluoresced green when compared to its corresponding controls (cells not expressing a fluorescent protein, and cells expressing RFP)

Flow cytometry

The results for flow cytometry are shown in Figure 6. GFP expression from both cultures (Figure 6b. and 6c.) result in a shift in fluorescent populations when compared to control cells expressing no fluorescent protein (Figure 6a.). These results confirm the proper functioning of our modified GFP gene and furthermore, demonstrate the functionality of the promoter switches in their PB state.

Figure 6: Flow cytometry for confirming expression of inverted GFP (A) negative control: bacterial cells expressing no fluorescent protein (B) GFP (BBa_I20260) expressing bacterial cells (C) Bacterial cells expressing inverted GFP from the invertible promoter switch test construct. GFP expression from cultures (B) and (C) result in a shift in fluorescent populations when compared to control cells expressing no fluorescent protein (A). These results confirm the proper functioning of our modified GFP gene and furthermore, characterize the promoter switches in their PB state

BioBricks

Bba_K1039000	Φ attP site
Bba_K1039001	BXB1 pb switch J23119 promoter
Bba_K1039002	BXB1 pb switch J23118 promoter
Bba_K1039003	BXB1 integrase
Bba_K1039004	BXB1 lr switch J23119 promoter
Bba_K1039005	BXB1 lr switch J23118 promoter
Bba_K1039006	BXB1 RDF
Bba_K1039007	BXB1 integrase and RDF
Bba_K1039008	ΦC31 pb switch J23119 promoter
Bba_K1039009	ΦC31 pb switch J23118 promoter
Bba_K1039010	ΦC31 lr switch J23119 promoter
Bba_K1039011	ΦC31 lr switch J23118 promoter
Bba_K1039012	ΦC31 integrase
Bba_K1039013	ΦC31 RDF
Bba_K1039014	ΦC31 integrase and RDF
Bba_K1039015	iGFP
Bba_K1039016	Hpdo backbone
Bba_K1039017	Hpdo with no gene 8
Bba_K1039018	gene 8 of M13 virus
Bba_K1039019	RBS+gene 8 of M13 virus
Bba_K1039020	J23104+RBS+gene 8 of M13 virus
Bba_K1039021	loc+BXB1int+key+lock+BXB1 RDF
Bba_K1039022	loc+PhiC31int+key+lock+phiC31 RDF
Bba_K1039023	loc+BXB1int+key
Bba_K1039024	loc+BXB1int+key
Bba_K1039025	BS1TC
Bba_K1039026	BS2TC
Bba_K1039027	ΦS1TC
Bba_K1039028	ΦS2TC
Bba_K1039029	BXB1 integrase with the Promoter and RBS
Bba_K1039030	ΦC31 integrase with the Promoter and RBS
Bba_K1039031	BXB1 att P
Bba_K1039032	BXB1 att B
Bba_K1039033	BXB1 att R
Bba_K1039034	BXB1 att L
Bba_K1039035	ΦC31 att B
Bba_K1039036	ΦC31 att R
Bba_K1039037	ΦC31 att L

Notebook

Switch Modelling

Modelling the Dynamics of Bxb1-style and PhiC31-style Invertible Promoter Switch Systems

Invertible Promoter Switch “Styles”

We made invertible promoter switches based on the Bxb1 and PhiC31 integrase systems. In both of these systems Int dimerizes before attaching to att sites, and RDF modifies Int by binding it stoichiometrically (ie two RDF molecules bind each Int dimer). However, the details of the interaction between Int and RDF in these two systems is thought to differ. Notably, the Bxb1 RDF is thought to interact with Bxb1 Int only when Int is bound to DNA at the Bxb1 att sites [1] while PhiC31 RDF is also able to complex with Int in the cytosol independently of DNA binding [2] (Figure 1).

Figure 1. Bxb1 RDF can only complex with Int dimers that are bound to att sites, while PhiC31 RDF can complex with cytosolic Int dimers as well.

We refer to a switch system where RDF binds Int only when Int is already complexed with an att site as a “Bxb1-style” system, and we refer to a switch system where RDF can complex with Int independently of DNA as a “PhiC31-style” system. We sought to investigate the differences in the dynamics of these systems.

We first modeled switch systems where Int and RDF are driven by separate promoters (Figure 2). We should note that Bonnet et al did simulations describing the same switch systems in their original design of the RAD module [3].

Figure 2. We modeled the dynamics of Bxb1- and PhiC31-style promoter switch systems with Int and RDF driven by separate promoters.

Questioning the Model to Inform Model-based Design

We wanted to know the ranges of integrase and RDF expression that would flip switches of each style to identify potential differences in the ways they should be applied.

Model Construction

State variables (all time-dependent):

I = Integrase

R = RDF

mx = mRNA corresponding to protein X.

SPB = Switch in “PB” state, where we have attP and attB sites flanking the promoter

SRL = Switch in “RL” state, where we have attR and attL sites flanking the promoter

I2 = integrase dimer

I2SPB = integrase dimer bound to one att site in a PB state switch

I2RXSPB = integrase dimer bound to one att site in a PB state switch, with X RDF proteins also bound (X = 1 or 2)

I4SPB = one integrase dimer bound to each att site in the PB state switch

I4RXSPB = one integrase dimer bound to each att site in the PB state switch, with X RDF proteins also bound (X = 1 – 4)

In the PhiC31 style switch but NOT the Bxb1 style switch, the following complexes are also included:

I2Rx = X RDF proteins bound to an integrase dimer, X = 1 or 2

Parameters:

αI = transcription rate of Int

αR = transcription rate of RDF

δx = degradation rate of mRNA corresponding to protein X

βx = translation rate of protein X from mx

ρx = degradation rate of protein X

k1 = forward rate constant for association of a protein to a complex

k-1 = reverse rate constant for dissociation of a protein to a complex

krec = rate of switch flipping through recombination when bound to appropriate complex

Note that rate constants k1 and k-1 are assumed to be the same for formation of all complexes, since rate constants for the complexes are not known. krec is assumed to apply to flipping of the switch from PB to LR state and also from LR to PB state, when DNA is bound in the appropriate complexes.

Differential Equations: The differential equation for the abundance of an mRNA species is determined by transcription and degradation rates of mRNA:

The differential equation for the abundance of a complex X of I, R, and S accounts for the following factors:

If X is a monomer of I or R, it can be produced through translation and we add the term,

If X is formed from association of any complexes A and B in a reaction,

then we add the terms,

for association and dissociation.

If X can associate with a complex Y to form a complex Z in a reaction,

then we add the terms,

for association and dissociation.

If X is a complex that allows flipping of the switch (ie X is SPBI4 or SRLI4R4) then we add the term,

for production of X through switch flipping

Protein degradation in complexes is assumed such that individual proteins degrade at the same rate as they would outside of a complex. Degradation rate ρ is assumed to be equal for all monomers, so we assume that a complex X with N subunits degrades at a rate proportional to 1/N, since all subunits degrade together in the complex. For degradation of complexes, we add the term,

DNA is assumed to be conserved, without production or degradation. To allow protein complexes to degrade without losing DNA, degradation of any complex involving DNA is assumed to apply only to the protein; the DNA is left behind. For free DNA involved in complexes Ci, we add the term,

where Ni is the number of proteins in complex Ci.

Thus the general differential equation for abundance of a protein complex X is:

where n different protein pairs Ai and Bi can complex to form X, and X can complex with m different other complexes Yj to form m complexes Zj, and there are N proteins in complex X.

If X is a monomer of I or R then we also add the translation term, if X can be consumed or formed through switch flipping we add the appropriate production or consumption terms, and if X is naked DNA then we add terms for its formation through degradation of complexes.

Note that the only reactions involving switch flipping are:

Example:

For abundance of integrase dimer, which for a PhiC31 style system takes part in the interactions,

the differential equation is:

Estimation of Parameter Values

Promoter J32101 was reported to drive transcription at a rate of 0.03 PoPS (polymerases per second) [3]. We ranged our promoter strengths from 10-12 to 0.1 PoPS, as this was the range in which we saw interesting dynamics.

A previous study found that the average half-life of an mRNA is approximately 5 minutes [4]. We used this parameter for mRNA degradation in our model.

To find a reasonable range for translation rate, we referred to a study in which lacZ translation was found to range from roughly 15 to 50 translation initiation events per minute per mRNA [5]. We assumed translation occurred at a rate of 20 initiation events per minute per mRNA, and varied only transcription rate to change protein concentrations.

Based on a study of protein degradation rates [6], we produced protein degradation constants by assuming the half-life of all proteins to be 60 minutes.

For the forward and reverse rate constants, we did not have any data. We assumed k1, k-1, and krec all to be 1.

For all simulations discussed below, we ran the simulations with varying rate constant values to check if our assumptions had a significant impact on the results. We found that results were qualitatively similar.

Sensitivity Analysis of Bxb1- and PhiC31-style Switch Systems

We simulated the behavior of each switch with varying integrase and RDF promoter strengths. We used 50 switches in the simulations, and kept track of the percentage of switches that had flipped from PB to RL state at the system’s steady state (Figure 3). We also ran the simulations with different switch copy numbers and obtained qualitatively similar results.

Figure 3. Comparison of a) Bxb1-style and b) PhiC31-style switches flipping with various Int and RDF promoter strengths.

These graphs are qualitatively similar to those obtained by Bonnet et al using their simulation [3].

We note that the dynamics for very low Int and RDF promoter strengths (10-12 – 10-9 PoPS) are similar for the two switches, but that for higher promoter strengths they differ. In the Bxb1-style system RDF promoter strength above 10-9 PoPS rules out flipping of the switch, regardless of integrase promoter strength, while in the PhiC31-style system high RDF expression can be matched with high Int expression and the switch can still flip. Since the PhiC31-style system allows for RDF to complex with Int in solution, RDF can be sequestered by Int in solution. This allows for they dynamics we see in Figure 3.

To investigate the effects of stochastic processes, we implemented our differential equations model for the switch system stochastically using the Gillespie algorithm and varied Int and RDF promoter strengths. The shape of the parameter regions where Bxb1 and PhiC31 switches flipped was qualitatively similar to those found in the continuous model (Figure 4).

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Figure 4. Comparison of a) Bxb1-style and b) PhiC31-style switches flipping with various Int and RDF promoter strengths using a stochastic simulation.

We note that stochastic variation seems to have a particularly strong effect at low Int and RDF expression levels, as expected. In these simulations, it was particularly clear that the PhiC31-style switch was reliable over a more flexible range of expression levels.

Figure 4. Comparison of a) Bxb1-style and b) PhiC31-style switches flipping with various Int and RDF promoter strengths using a stochastic simulation.

Two-switch Systems

A promoter switch system may be subject to noise, where the switch temporarily flips accidentally due to a burst of integrase expression. If there are multiple copies of the switch present in the cell, there is a region of Int and RDF expression where some copies of the switch are flipped while others are not (the range of colours between blue and red in Figure 3). This behvaiour is undesirable, as it is not “switch-like”; since a switch is an all-or-nothing device, we would like our switches to function this way even when they are present in multiple copies.

We wanted to see if use of a two-switch system, wherein the first switch controls the second switch, could improve the “switch-like nature” of a multi-copy set of switches. If the first switch must flip to completion before the second switch flips, then the expression range where switching is only partial may be reduced in the second switch.

We modeled a two-switch system where the first switch controls expression of the Int and RDF corresponding to the second switch (RDF2 and Int2). RDF2 is expressed when switch 1 is in its initial PB state, and Int2 is expressed when switch 1 is flipped to RL state. The Int and RDF corresponding to switch 1 are now referred to as Int1 and Int2 (Figure 4).

Figure 4. A two-switch system where the second switch is controlled by the first switch.

Modelling the Two-switch System

In this system, we have state variables for Int2 and RDF 2 (I2 and R2), their mRNAs, and complexes involving them and the second switch. The two-switch system was modelled using the same principles described for the one-switch system, with one extra detail: the transcription rate of Int2 and RDF2 genes depends on the state of switch 1. We introduce the parameter,

αS1,

the transcription rate from the promoter in switch 1. The differential equations for Int2 and RDF2 mRNAs are given by:

where Pi are complexes involving S1 in PB state, and Rj are complexes involving switch 1 in RL state.

Sensitivity Analysis of a Two-switch System

We simulated the two-switch system with varying Int1 and RDF1 promoter strengths. We used 50 switches in the simulations, and kept track of the percentage of switches that had flipped from PB to RL state at the system’s steady state for each of the two switches. The strength of the switch 1 promoter was chosen such that the sum of expression from all the switch 1 promoters was equal to the expression from the Int1 promoter. This way, similar amounts of integrase are in the system for both switches. Figure 5 displays a two-switch system using two PhiC31-style switches. We selected the PhiC31-style switch because it offers control over switching at a wider range of parameter values than the Bxb1-style switch.

We also ran the simulations with different switch copy numbers, different switch 1 promoter strengths, and different combinations of switch 1 and switch 2 styles, and obtained qualitatively similar results.

Figure 5. Percentages of promoters flipped for a) Switch 1 and b) Switch 2 in a two-switch system. Both switches were PhiC31-style in this case.

We see that the ranges of Int1 and RDF1 promoter strengths corresponding to incomplete switch flipping (the colours between blue and red in Figure 5) are narrower for switch 2 (Figure 5b) than for switch 1 (Figure 5a). This simulation suggests that controlling one switch with another helps make a multi-switch system more switch-like, with flipping of all copies at once.

To investigate the effects of stochastic processes, we implemented our differential equations model for the two-switch system stochastically using the Gillespie algorithm and varied Int and RDF promoter strengths. The shape of the parameter regions where each switch flipped switches flipped were qualitatively similar to those found in the continuous model (Figure 7).

IMAGE 2 HERE

Figure 7. Percentages of promoters flipped for a) Switch 1 and b) Switch 2 in a two-switch system simulated stocahstically. Both switches were PhiC31-style in this case.

We note a clear reduction in the variability of the percentage of switches flipped in switch 2 compared to switch 1. By controlling switch 2 with switch 1, we have reduced the stochastic nature of switch 2 to give it more robust switch-like behavior. This complements the finding using the continuous model, which showed that a two-switch system narrowed the parameter range corresponding to partial flipping of switch 2.

Conclusions

Our goal is to use modelling to inform design decisions in a model-based design approach. The following points are relevant to the application of these invertible promoter switches:

• Our single-switch model indicates that a Bxb1-style switch has a threshold of RDF expression above which no flipping can occur, regardless of Int expression, while a PhiC31-style switch can be flipped at any RDF expression level as long as Int expression is high enough.

  • o The PhiC31-style switch is thus more flexible in different situations that may require performance at different ranges of Int and RDF expression levels.
  • o The Bxb1-style switch has the advantage that switching can generally be controlled by varying one parameter, RDF expression, past a specific threshold.
• • Our two-switch model shows that switch-like behavior of a switch present in multiple copies is improved if it is controlled by a second switch; the two-switch system corresponds to a narrower range of parameter values that permit partial switching.

References

1. Ghosh P, Wasil LR, Hatfull GF. (2006) Control of phage Bxb1 excision by a novel recombination directionality factor. PLoS Biology, 2006, 4:e186.

2. Khaleel T, Younger E, McEwan AR, Varghese AS, Smith MCM. A phage protein that binds PhiC31 integrase to switch its directionality. Molecular microbiology, 2011, 80(6), 1450-63.

3. Bonnet J, Subsoontorn P, Endy D. Rewritable digital data storage in live cells via engineered control of recombination directionality. Proceedings of the National Acadamy of Science USA, 2012, 109(23) pp 8884-8889.

Population & Infection Modelling

Modeling Controlled Modification and Retransmission of a DNA Message

We sought to combine controlled modification and controlled transmission of a DNA message to design a system wherein receiver cells are able to modify a DNA message by flipping an invertible promoter switch and then retransmit the modified message (see the section on Controlled Modification and Retransmission of a DNA Message on the Design Page).

The system is summarized as follows:

• Three populations are present in co-culture: sender cells, primary receiver cells, and secondary receiver cells.

• Senders transmit a DNA message with an invertible promoter switch in PB state. M13 gene VIII along with a T7 RNA polymerase gene are positioned on the message phagemid such that they are expressed when the switch is in LR state.

• Primary receiver cells are F+ and contain a helper plasmid with a gene VIII knockout. Viral particles cannot be produced using protein products from this helper plasmid alone. Primary receivers also contain an inducible integrase gene corresponding to the switch on the DNA message.

• When the switch on the message is flipped in primary receiver cells, gene VIII is expressed and the helper plasmid with gene VIII knockout is complemented. With a full suite of M13 genes, the modified version of the messaging phagemid (Mmod) can be packaged into viral particles and retransmitted.

• Secondary receiver cells are F+ and contain a gene for a fluorescent protein driven by a T7 promoter. When a secondary receiver cell receives a modified messaging phagemid (switch flipped), the T7 RNA polymerase expressed from the message causes detectable fluorescent protein production.

The goal for this system is for secondary receiver cells to receive the modified message phagemid when modification and retransmission by primary receivers is induced. The modified message should not be transmitted in absence of induction.

This system is prone to several undesired outcomes cause by various modes of failure, outlined below:

• Since primary receivers must be infected by Mori before they can produce Mmod, there is a time lag between accumulation of Mori and Mmod. A time lag is also expected in the “turnaround” between reception of a DNA message and accumulation of copy number and protein concentration that can fuel packaging and transmission of the modified message. If the majority of secondary receivers become infected with Mori during this turnaround, Mmod will not have a significant presence in the secondary receiver population.

• We have picked apart the M13 genome in order to allow control over production of viral particles. As a result, it is likely that production of viral particles by primary receivers may be less efficient than particle production in senders. If the rate of particle production is drastically reduced in primary receivers as compared to senders, Mori may accumulate much faster than Mmod throughout the entire experiment

2. Since the integrase that flips the switch in primary receivers is controlled by an inducible promoter, basal (uninduced) expression of integrase may cause unwanted flipping of the switch, which would result in production of Mmod in the absence of induction. Since a switch holds state after flipping, such an error would be irreversible. This problem is exacerbated by the fact that the resulting unwanted message transmission would spread to other cells. Such amplification of error could make the system effectively constitutive, even though an inducible promoter is employed.

Questions We would Like to Shed Light On

Our goal in modeling is model-based design. We would like to use our model to guide our design choices to direct lab efforts. We sought to use a model of the spread of Mori and Mmod through the co-cultured populations to shed light on the following questions:

• What is the minimum efficiency of viral particle production in primary receivers as compared to senders that preserves functionality of the system?

• What ratio of initial populations (senders : primary receivers : secondary receivers) is optimal to accomplish successful delivery of Mmod to the secondary receiver population?

• What is the effect of basal uninduced expression of integrase in primary receivers? To what extent does such error become amplified?

Model Construction

We used the following set of state variables and parameters to model the spread of Mori and Mmod through the co-cultured populations:

State variables (all time-dependent):

Ps = population of sender cells

P1 uninfected = population of primary receiver cells that have not received a DNA message

P2 uninfected = population of secondary receiver cells that have not received a DNA message

P1 ori = population of primary receiver cells carrying the original DNA message

P2 ori = population of secondary receiver cells carrying the modified DNA message

P1 mod (t,a) = population of primary receiver cells of age a at time t. Age a corresponds to how long the cell has been a P1 mod cell. This is important to know because of the turnaround time between reception of a DNA message and accumulation of phagemid and protein to fuel viral particle production.

P2 mod = population of secondary receiver cells carrying the modified DNA message

Mori = concentration of M13 viral particles carrying the original unmodified DNA message

Mmod = concentration of M13 viral particles carrying the modified DNA message

Parameters:

c = carrying capacity of the liquid growth medium

g = maximum growth rate of cells that are not producing viral particles in absence of pressure due to carrying capacity

r = maximum growth rate of cells that are producing viral particles as a fraction of g

k = adsorption rate of viral particles to cells

i = induction constant. i = 1 when message modification and retransmission is induced, and i = 0 when it is not induced

S(i) = rate of switch flipping per primary receiver carrying Mori

p = rate of viral particle production through intact M13 cistrons on the helper plasmid

b = rate of viral particle production through complemented helper plasmid with gene VIII knockout, expressed as a fraction of p

j1 = age at which viral particle production begins in P1 mod cells. Production starts at zero at age j1 and increases.

j2 = age at which viral particle production reaches its maximum and continues to maintain a steady rate

We built a differential equations (DEs) model of the system using these state variables and parameters, which is described below.

When modeling the growth of each population over the course of the experiment, the carrying capacity of the media had to be considered. To simplify our equations, we define:

The DE for senders is a simple logistic growth equation:

where the growth rate G is modified by r since senders produce viral particles and therefore grow more slowly. The DEs for P1 uninfected and P2 uninfected incorporate loss due to receipt of DNA messages, and the DE for P2 ori incorporates production due to receipt of DNA message:

Since there is a turnaround time associated with receipt, modification, and retransmission of a DNA message in primary receivers, the “age” of P1 mod cells – the time since they became capable of producing viral particles – is an important aspect of the system state for determining the rate of production of Mmod. We are therefore interested in P1 mod (t,a), where a is age, and we must use a partial differential equation to keep track of this state variable. The boundary condition for the PDE accounts for P1 mod(t,0), the production of P1 mod through flipping of the switch in P1 ori cells as well as direct infection by M1 mod.

Production of Mori by senders occurs through intact helper plasmid at rate p

The rate of production of Mmod by P1 mod cells requires consideration of the age of the cells. The rate of production of Mmod by cells of a given age is defined by the function.

where viral particle production by a P1 mod cell begins at age j1 and increases with age to maximal production rate bp at age j2, where 0 < b < 1. Production in these cells is likely slower than the rate in senders, p, because one of the M13 cistrons has been picked apart to allow complementation. The production rate of Mmod at time t is determined from the distribution of P1 mod cells over all ages and the rate of particle production at each age through a convolution:

Analysis

Analysis Analysis Analysis Analysis Analysis Analysis Analysis Analysis

Discussion

DiscussionDiscussionDiscussionDiscussionDiscussionDiscussion

Estimation of Parameter Values

Estimation of Parameter ValuesEstimation of Parameter ValuesEstimation of Parameter ValuesEstimation of Parameter ValuesEstimation of Parameter ValuesEstimation of Parameter ValuesEstimation of Parameter Values

In the absence of precise parameter values, a literature review provided reasonable ranges for the parameters that allowed us to make qualitative statements about the behavior of the system.

• Carrying capacity c was estimated as 1.5*109 cells/ml [1].

• Maximal growth rate g was estimated as ln(2)/20 – ln(2)/30 / min, for a doubling time between 20 and 30 minutes.

• Maximal growth rate of cells producing viral particles was taken as 1/4g – 1/2g [4].

• Adsorption rate k of viral particles to cells was taken as 3*10-11 ml/min [5].

• The rate of flipping of an invertible promoter switch inside a P1 ori cell when induced, S(1), was taken between 0.5 and 1 flips per cell per minute. The rate of switch flipping when uninduced, S(0), was taken between 0 and 0.5 flips per cell per minute.

• The rate of viral particle production p from the intact helper plasmid was taken to be 33 particles per cell per minute [6].

• The rate of viral particle production through complemented helper plasmid with gene VIII knockout is taken as bp, where p is the rate of production from intact helper plasmid and b is between 0 and 1. It is assumed that picking apart the M13 genome for purposes of complementation will either reduce efficiency or have no effect.

• The age at which viral particle production begins in P1 mod cells, j1, and the age at which viral particle production reaches a maximum are assumed to be similar to that of wildtype M13, and these values are taken to be 10 minutes and 50 minutes respectively [6]

• j1 = age at which viral particle production begins in P1 mod cells. Production starts at zero at age j1 and increases.

• j2 = age at which viral particle production

Some interesting questions that we can pose in the model could be what would happen if the parameters that were chosen were allowed to vary significantly. These questions could be answered by analyzing our model with a few changes. Some questions could be

- What would happen if the reduction in efficiency, r, would change to different factors? Even if the system has already been induced.

- What initial conditions should we start with so we can attain the values we hoped for?

- What is the threshold of leakiness should we allow and how would the model break if the leaky promoter were quite severe?

We shall now examine the first question.

Below, we have values of the reduction in efficiency plotted for various values of r. Namely, we have r = 0.01, 0.1, 0.25, 0.75 and 1. We would also like to note that these graphs were plotted assuming that the inducer has been put in, s=0.5, and the initial conditions of the bacteria is a 1:10:5 where the first value is the sender population, the second value is the primary receiver population and the third value is the secondary receivers.

As we can see, as r increases, the secondary modified message would increase which would make sense since as the production of the secondary phagemid is more efficient, it would create more viruses thus there would be a greater volume infecting the cells. There is one remark that is interesting and that is of when r = 0.01 and that is even when the inducer has we induced into the environment, there are not enough virus to infect the secondary receivers. We would also like to note that when the secondary phagemid is not efficient, the primary virus is infecting all the secondary cells. Now we will examine the second question. Below, would be the graphs of different ratios of the starting populations in an induced environment with the reduction of efficiency set to 0.5.

As shown, when the initial conditions are at a ratio of 1:20:10, we see this seems to be the most optimal since this is in the induced system; we have a strong presence of the secondary modified message. This is a strong statement because it shows that the secondary phagemid is indeed being produced by the primary cells.

For these two situations when the primary population is too low, there are not enough primary cells to be infected to produce the secondary phagemid thus there would be no modified secondary population. The reason why the secondary population with the original is so high is because the sender phagemid have nothing else to infect thus must infect the secondary cell population.

For the graphs above, we see that the secondary cells is the least thus there are not enough of the secondary cells to be infected. For the top graph, there is also a lot of secondary cell with original message because the primary cells get infected first thus would have more time to multiply which would then get infected by the primary phagemid since there are an abundance of them. For the bottom graph, we see that the primary cells are absorbing all the sender phagemid and due to the scarcity of the secondary cells, are not being infected with the secondary phagemid that would explain the low levels of the secondary cells infected with the secondary phagemid.

In this case, we see that there is a large amount of secondary cells infected with the secondary phagemid. This is due to the high amounts of secondary cells available for the secondary phagemid to infect. The small concentration of primary infected cells is due to the low amount of sender population that is infecting the primary cells however when the secondary phage is produced, it would quickly infect a secondary cell. Now we shall analyze the third question An interesting question would be how much is leakiness involved with the model and what would be the upper bound on leakiness that would conform to the model. Similar to the previous graphs, the initial conditions are a 1:10:5 ratio and the reduction of efficiency is 0.5.

As we can see when the probability of leakiness is on a magnitude of 10^-3, it is well behave as in the secondary modified cells are not as present since there are not a lot of secondary phagemid to infect the cells. However, as shown above, when the magnitude is about 10^-2, it seems as though there are more secondary phagemids that can infect the secondary cells which would cause a large concentration of the secondary modified cell. Thus as we have seen in this analysis that there are some questions that can be solve however many questions can be asked since there may be improvements to the model or the initial parameters themselves.


 References:

3. Short protocols in molecular biology, Fred Ausubel et al., 5th ed. Vol. 1 pp. 1-5

4. Sambrook J, Russell DW. Molecular Cloning: A Laboratory Manual. 3rd edition. Cold Spring Harbor Laboratory Press, Cold Spring Harbour, NY. 2001.

5. Tzagoloff, H., and D. Pratt. 1964. The initial steps in infection with coliphage M13. Virology 24:372?380.

6. Clackson, T., & Lowman, H. B. (2006). Phage display, a practical approach. (pp. 2-14). New York, NY: Oxford University Press, USA.

Phage Particle Production Modelling

'''Description''' M13’s relatively small genome can be classified in three succinct subsets: structural (genes III, and VI - IX), morphogenetic (genes I, IV, and XI), and replicative (genes II, V, and X). The replicative genes play the largest role in our project, but in order to model the behaviour of the virus from infection to secretion, we must take into account all of the genes, their respective proteins, and their functions. By creating a differential model of the set of mRNA strands, proteins, and DNA forms present inside a bacterial host cell at any point in time, one can see the effects of increasing or decreasing levels of specific compounds on viral production and packaging. The main function behind each of the proteins can be found in the main project summary. The protein produced by the translation of gene V (pV) is responsible for much of the regulation of viral replication, and thus is central to the model. If there is too much pV present in the cell, it will sequester the infected form (IF) DNA and stop the replicative process; too little, and the process can’t take place [3-7]. As the concentration of pV stabilizes (along with the other regulatory proteins, pII, pV, and pX), a steady state of viral production and secretion is achieved. The model constructed by the viral assembly team is complimentary to the goals of the switch and the population dynamics teams. The designs conceived by the switch team could be mathematically tested by the assembly model. For example, what is the effect of removing a subset of genes from M13’s genome? Or, will assembly/secretion still occur if those genes are re-introduced after a switch event? Further, the fidelity of the population dynamics model would benefit from having realistic estimates of viral secretion rates over time. This is a specific quantity the assembly model hopes to determine. The assembly group’s research and model development has implications on the project’s design. Through the model, genes that were mandatory for assembly or secretion can be characterized. Selecting which genes to withhold from the primary receivers (i.e. which genes to include on the helper phagemid) relies on this info. The wrong choice could result in host cell death or crippling of the infecting viral particles, preventing retransmission (the goal of the design). Testing such choices with a mathematical model rather than in a lab experiment saves both time and money. Citations: [1] Sambrook J, Russell DW. Molecular Cloning: A Laboratory Manual. 3rd edition. Cold Spring Harbor Laboratory Press, Cold Spring Harbour, NY. 2001 [2] Baas, P. D. (1985). Biochim. Biophys. Acta. 825, 111-139 [3] Mazur, B. J. & Model, P. (1973). J. Mol. Biol. 78, 285-300. [4] Webster, R. E. & Cashman, J. S. (1973). Virology. 55, 20-38. [5] Mazur, B. J. & Zinder, N. D. (1975). Virology. 68, 490-502. [6] Geider, K. & Kornberg, A. (1974). J. Biol. Chem. 249, 3999-4005. [7] Salstrom, J. S. & Pratt, D. (1971). J. Mol. Biol. 61, 489-501. [8] Fulford, W. "Bacteriophage F1 DNA Replication Genes *1II. The Roles of Gene V Protein and Gene II Protein in Complementary Strand Synthesis." Journal of Molecular Biology 203.1 (1988): 39-48. Print. [9] Haigh, Nora G., and Robert E. Webster. "The pI and pXI Assembly Proteins Serve Separate and Essential Roles in Filamentous Phage Assembly." Journal of Molecular Biology 293 (1999): 1017–1027. [10] Russel, Marjorie. "Interchangeability of Related Proteins and Autonomy of Function: The Morphogenetic Proteins of Filamentous Phage f1 and IKe Cannot Replace One Another." Journal of Molecular Biology 227 (1992): 453-462. [11] Wickner, William, Gail Mandel, Craig Zwizinski, Marjorie Bates, and Teresa Killick. "Synthesis of phage M13 coat protein and its assembly into membranes in vitro." Proceedings of the National Academy of Sciences 75.4 (1978): 1754-1758. '''Equations''' \begin{equation} \frac{d[S_j]}{dt} = \alpha_j[RF] - \delta_j[S_j] \end{equation} \begin{equation} \frac{d[P_i]}{dt} = \beta_i[M_i^F] - \delta_D[P_i] \end{equation} \begin{equation} [M_i] = \sum_{\text{mRNA}}[S_j] \end{equation} \begin{equation} [M_i^F] = \left(1 - \frac{[P_5]}{k_i + [P_5]}\right)[M_i] \end{equation} \begin{equation} \frac{d[RF]}{dt} = k_{conv}[IF^F] - \delta_D[RF] \end{equation} \begin{equation} \frac{d[IF]}{dt} = k_{RC}\left(1 - \frac{[P_2]}{H + [P_2]}\right)[RF] - k_{exp}[IF^S] - \delta_D[IF] \end{equation} \begin{equation} [IF^F] = \left(1 - \frac{[P_5]^n}{K^n + [P_5]^n}\right)[IF] \end{equation} \begin{equation} [IF^S] = \left(\frac{[P_5]^n}{K^n + [P_5]^n}\right)[IF] \end{equation}\\* \begin{equation} \forall m \in \{1, \dots, 8\},\;\; \forall j \in \{1, \dots, 11\} \end{equation} '''Variables''' S_j : The jth mRNA sequence, with j starting at the gene 2 promoter α_j : Transcription rate from of jth promoter β_i : Translation rate from gene i mRNA δ_j : Degradation rate of jth mRNA δ_D : Dilution rate k_conv : Rate constant for IF → RF converstion k_RC : Rate constant for rolling-circle replication k_exp : Rate constant for export of sequestered IF from infected cell k_i : Half-saturating constant for P5 binding to mRNA for gene i H: Half-saturating constant for P2 binding to RF K : Half-saturating constant for P5 co-operative binding to IF n : Co-operativity constant for P5 binding to IF M_i : mRNA coding for the ith gene on the M13 genome, as labelled in literature M_i^F: Unbound (“free”) mRNA for gene i P_i : The protein product of the ith gene RF: “Replicative Form” (double stranded) viral DNA IF: “Infective Form” (single-stranded) viral DNA '''Output''' '''Discussion''' Our governing differential equations reflect our attempt to model M13’s genetic regulatory mechanisms with appreciable fidelity. All direct byproducts of the viral genome were regarded as state variables over time, and degradation/dilution play a role in each synthesis equation. Equations (20) and (21) correspond to mRNA and subsequent protein synthesis, with considerations for unique translation and transcription rates, captured by our estimated parameters. Equation (22) accounts for the fact that mRNA concentrations for a specific gene are given by the sum of all pertinent mRNA chains (chains which contain said mRNA), as described by the known order of genes and promoters in the genome. In equation (23), P5 plays the role of regulating the translation of all mRNA. This is critical; by hindering its own synthesis, it creates a stabilizing negative feedback loop. Equations (24) through (27) measure the rates of change of each form of viral DNA. RF synthesis (equation (24)) is controlled by host processes and depends on free IF DNA. Equation (25), which considers total IF DNA synthesis, acknowledges the role of P2 in rolling circle DNA production, as well as the fact that IF DNA is constantly being sequestered by P5. Free IF DNA and its counterpart, sequestered IF DNA, have rates of synthesis that depend on the total IF concentration and incorporate co-operativity, as suggested by previous M13 research. Through its presence in equations (23), (26), and (27), P5 asserts its role as a key player in the genetic regulatory system.

The University of Waterloo’s iGEM – Human Practices team is a diverse team whose goal is to raise awareness on issues regarding synthetic biology. The team also provides the student community information about the latest in the research area of synthetic biology to help the community make informed, accurate and fact-based opinions. Our goal is to strengthen the bridge between the community and their knowledge of synthetic biology while eliminating misconceptions regarding synthetic biology.

In the past year, the team gained valuable experience and information through the projects they worked on. Each project provided more insight on how informed the student community is on the topic of synthetic biology. This further helped us plan out activities that helped us achieve our goal.

One of the main purposes behind the projects this year to enrich, educate and empower the student community. To achieve this goal, various activities were planned to inform the student community about the field of synthetic biology, it’s potentials and how it affects the world around us. These activities provide fundamental knowledge of synthetic biology and it’s uses, allowing the participants and the viewers to form an informed, accurate and fact-based opinion about the topic.

T.I.L. Syn Bio

iGEM is a community of people passionate about synthetic biology – how can we best convey this while reaching out to the student community? Sometimes reading papers and textbooks doesn't quite do it for understanding an idea. As students, we know it can be difficult to grasp some concepts we’re not familiar with. So what’s a better way to communicate an idea? Could social media be the answer? That was the idea behind the VLOG series TIL: Syn Bio.

These series are a quick and effective way to convey the ideas and passion of synthetic biology. The series has many episodes that highlight various aspects of synthetic biology through a mixture of one-on-one videos and animated style videos. The series begin with episodes explaining “What is Synthetic Biology?, “Fundamental Advances” and “Cell-to-Cell Communication” (Waterloo iGEM’s 2013 project). This phase of the series is important to orient the viewers and provide some background information.

igem-cmit from Waterloo iGEM on Vimeo.

During the second part of the series, the team takes a fun twist. Using the TIL: Synthetic Biology outreach event footage to compare the viewpoints of students and professors on various topics relating to synthetic biology. The footage from this event is used for addressing many factors associated with the idea of synthetic biology. These factors range from the background knowledge to stigma associated with synthetic biology and from the regulations needed to its future potential.

The series begins with these six videos, leaving the rest of the series to be shaped by viewers. Ultimately, viewers engage with the team about what they want to see in future videos, ask questions they want answered and connect with information from a variety of sources.

The TIL: Synthetic Biology outreach event (used as part of the video series) was well-received. The team came prepared with questions to ask passing students. Students were also given 4-5 days notice via Facebook. The idea behind this aspect of the video was to have it be a surprise. Questions like "do you support GMOs?", "would you eat modified fruit/meat?", "who should be able to practice synthetic biology/should it be open sourced?" and many more were asked. The team was in for some surprises with the diversity of knowledge on campus! We hope incorporating the footage into our series will give participants a fun look into their experience, which they can easily share with their friends and family. Overall, we hope that the team's work will inspire more leaders to take part and contribute to the advances in synthetic biology, regardless of their academic or professional background.

Intent to Invent

Intent to Invent was hosted on March 07, 2013 at the University of Waterloo’s Quantum Nano Center. The purpose of the event was to:

1. Connect the students to experts in 3 key industries that use synthetic biology in their processes: Agriculture, Health and Pharmaceuticals.
2. Bridge the level of discomfort a scientist has in regards to business.
3. To encourage entrepreneurship within the scientific community by delivering resourceful content from industry experts.

The event promoted open panel discussions of emerging technologies in biotechnology and other advanced biological fields within the 3 industries. Students got a chance to see how synthetic biology is the connected to entrepreneurship, innovation and commercialization. They learned about the industry perspectives and barriers faced by biological companies at different stages in their business model. This talk also encouraged entrepreneurship within the scientific community by delivering resourceful content form industry experts. Each speaker gave a 20-minute mini lecture on topics including: Clinical Trial Drug Development, Commercialization of Biomass and Energy Products and Entrepreneurial Barriers for Biotechnology Companies.

Steve jobs once said, “I think the biggest innovation of 21st century will be the intersection of biology and technology. A new era is beginning, just like the digital one…”. Through sessions such as Intent to Invent, Waterloo iGEM hopes to enrich the experience of science enthusiasts as well as those just curious about synthetic biology and it’s potential. By connecting these students to industry experts, we were able to gage their interests in an innovative and entrepreneurial aspect of science. Many students showed interest in learning more about the bridge between science and business in the future. iGEM received good feedback regarding Intent to Invent, as many students felt that the information they learned was very valuable. Waterloo iGEM provided many students the appropriate connection and information they need to start connecting the scientist in them with the businessman/businesswoman in them.

VeloCity Science

How do we inspire young people to eliminate the gap between science and business? The conventional education system does not provide for this overlap. There is a job unemployment crisis throughout North America, and from the past experience, this is the perfect time to turn to entrepreneurship for solutions. It is time to do what we have done to the IT industry back in the 80s, but with biotechnology this time. The perfect storm is brewing. Economic downturns have proven to be the best time for entrepreneurship. The Canadian government has seen this and supports entrepreneurial initiatives like these. And most importantly, we have bright young people hungry to make changes to the world.

And that’s why VeloCity Science has been started, an entrepreneurship program that brings together the right business resources (networks, mentorship, legal and financial services, etc.) and the right technical resources (wet-lab space, consumables and equipment) to create kick-ass biotechnology start-ups.

But that’s not all. Above of all these resources, it is the sense of community that is crucial for the success of these entrepreneurs. The University of Waterloo has proven time and time over in providing a strong sense of community to our entrepreneurs through the programs like Accelerator Centre, Communitech Hub, and VeloCity.

That’s the story of VeloCity Science, and we are just starting to write it.

Laboratory

Intent to Invent

Safety

All experiments are carried out in a BL2 certified lab. Researcher safety when using E. coli, would not be compromised in safety issues due to use of M13. It poses no threat at all to humans. While, the E. coli strain used was relatively harmless, treatment of possible infections may potentially be affected by the antibiotic resistance. Furthermore, spontaneous mutations which result in increased infectivity may result. However, measures and precautions suggested by the Canadian biosafety guidelines were taken to minimize even the slight chance of infection. Additionally, the working conditions of the lab is already above the recommended safety level of BL1 for usage of M13 viruses. Every member of the team has been trained with safety modules and went through a week of lab training and continuous oversight from the Advisors and his graduate students in the lab. All lab members, including graduate students or other students that were working in the lab, wore appropriate PPEs and disposed all consumable in appropriate biological waste boxes. All surfaces were wiped down with ethanol after use and all glassware was washed immediately after their usage.

The design of the project does not call for release into the public. Additionally, the project design does not produce any harmful products. Through it is possible that the construct could get released to the general public accidentally. But, the product of the constructs only produce fluorescent proteins and it can only be used in a controlled setting with a certain type of chemical present in the environment, thus making it ineffective when released to the public. Because safety of the public and the lab members is our utmost concern, we have ensured that all wastes are thrown out appropriately and autoclaved so that accidentally release would never occur.

There are no additional risks posed by our projects compared to other general BL1 lab concerns. Our bacteria are not pathogenic and are unable to survive outside of the lab environment, because they are unable to effectively compete with other organisms in nature. As stated above, all wastes are discarded according to the Waterloo standards and autoclaved.

There is no potential for harm to human health through use of our constructs, as described above. There is therefore no risk of malicious use.

Our constructs pose no threats to human health, as described above, and scaling up would not change this. Our project is a "fundamental advance" that contributes to the coordination of population-level cellular behavior by allowing messages to be sent between populations of E. coli cells. However, many additional layers of complexity in engineering would be required to use our method to enable pathogenic or otherwise dangerous behaviors in populations of cells.

The cell to cell communication project does include packaging viral particles. Although there are only some proteins of the M13 virus that are packaged and are therefore not a safety risk. M13 is not a safety risk even if its whole genome is packaged. Our project poses no threat to safety and thus we haven't implemented any of these mechanisms.

All the lab and design team members successfully passed the following safety training: Employee Orientation Training Session: https://info.uwaterloo.ca/infohs/hse/online_training/employee-orientation/Staff%20Orientation.swf Workplace Violence and Harassment Training: https://info.uwaterloo.ca/infohs/hse/online_training/workplace_violence/workplace_violence.html General Laboratory Safety: https://info.uwaterloo.ca/infohs/hse/online_training/lab_safety/lab_safety_course.html WHMIS: http://www.safetyoffice.uwaterloo.ca/hse/lab_safety/index.html Laboratory BioSafety Training: https://info.uwaterloo.ca/infohs/hse/online_training/biosafety/biosafety.swf

The BioSafety Guidelines followed by uWaterloo iGEM team can be found here: http://www.safetyoffice.uwaterloo.ca/hse/bio_safety/legislation.html

University of Waterloo has a Biosafety Committee and can be found here: http://www.safetyoffice.uwaterloo.ca/hse/bio_safety/bsc.html. Although the project has not been discussed with the Biosafety Committee, it has been discussed with several faculty members and has been found to have no risks. Furthermore, the laboratories operating at the University of Waterloo have obtained permits from the Bio-Safety Committee in order to perform intended research. Since the Waterloo iGEM team performs all laboratory work in a parent lab under the guidance of the Masters and PhD students of that lab, the permits obtained by the parent lab cover the projects carried out in the lab.

Canada has very well established biosafety regulations and guidelines which can be found here: http://www.phac-aspc.gc.ca/lab-bio/

The laboratory we work on cell to cell communication project is rated level 1.

E.coli strains that Waterloo iGEM team works with falls within the risk level 1. Additionally the laboratory we operate in is certified for work with the above listed risk group of the E.coli.

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Acknowledgements

We would like to offer special thanks to the following groups for your help

• Our advisors for donating your time and intellectual knowledge to the team

• Members of the Charles lab from University of Waterloo for your generous sharing of lab space, equipment and constructive suggestions to our project.

• Endy lab from Stanford University for proposing the use of cell-cell DNA messaging and sharing of knowledge and parts.

• Monica Martinez from Endy lab for your supportive role in our project and expertise in the usage of M13.

• Our IGEM collaborator this year: team UOttawa for helping us with the making of our constructs and interchange of knowledge.

• Staff of University of Waterloo Department of Biology for your timeless support and encouragement to our team.

• Dr. Maud Gorbet's from University of Waterloo for the sharing and expertise on flow cytometry for detecting reporter molecules.

• Dr. Mongol Marsden from University of Waterloo for the use of fluorescent microscope

• Susanna Zucca from Magni lab of Università degli Studi di Pavia for your sharing of parts for the single copy switch experiment.

• Andrew Dhawan for your guidance for the Mathematical Modelling team.

• Dr. Roderick Slavcev from pharmacy for his guidance with M13 bacteriophage.

• Dr. Gord Surgeoner, Dr. Catherine Burns, and Nicky Arvanitis for your insight presentation at our Intent to Invent seminar.

• All members of University of Waterloo IGEM 2013 for your sleepless nights and love for synthetic biology