Team:Wageningen UR/Model/Safety


iGEM Wageningen 2021

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Modeling a formaldehyde dependent toxin-antitoxin system

safety modeling icon

Modeling a formaldehyde dependent toxin-antitoxin system

The Cattlelyst biofilter will contain GMOs. To ensure biocontainment, we designed a methane-dependent kill switch, which aims to kill the cells outside of the biofilter when the methane concentration decreases. Inside the biofilter, methane is converted to formaldehyde, that can regulate transcriptional processes [1].

In our circuit, formaldehyde sensitivity is coupled to a toxin-antitoxin system. We used an ODE model to investigate whether the circuit would be sensitive to changes in formaldehyde concentrations and found that the sensitivity of the toxin-antitoxin system needs improvement. Therefore, two extensions on the circuit were tested.

To increase toxin production in low formaldehyde conditions, a positive feedback loop on toxin mRNA production was effective. Sensitivity could be further improved by incorporating competition between formaldehyde-sensitive transcription factors. This improved methane-dependent kill switch was sensitive to formaldehyde 15-30 hours after initial exposure. After this time, the toxin level in high formaldehyde conditions increased, which kills the cells – an undesired system behavior. We learned from the model that the methane-dependent kill switch is not sensitive enough to only kill the cells that escape from the biofilter. Therefore, in the laboratory experiments, we put toxin production under control of a hybrid promoter which is activated upon low cell densities and low methane concentrations [2].

Introduction

One of the pillars that Cattlelyst is built on is biosafety. As the biofilter is designed for real-world application on cattle farms, biocontainment of the GMOs is crucial. Therefore, we have designed three layers of safety. One of these layers is based on a unique feature of the biofilter internal conditions: the high methane concentration. This system will be built in our synthetic methanotroph E. coli.

The designed circuit consists of three parts:

FrmR protein

As there are no intracellular methane receptors, we had to look a bit further down the methane oxidation pathway (see Figure 1) and found out that E. coli can detect formaldehyde concentration in the cell by the FrmR protein [3]. In absence of formaldehyde, FrmR binds to the promotor Pfrm and represses transcription. When formaldehyde is present, it causes a conformational change in the protein and dissociation from the promotor region, thereby allowing transcription of the operon (see Figure 2) [3,4]. To kill the organism, the frmR gene will be coupled to a toxin-antitoxin system.

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Figure  1: Methane oxidation pathway in methanotrophs. Methane is converted to methanol. Methanol is converted to formaldehyde by methanol dehydrogenase. Formaldehyde is then converted to formate which will in the last step be converted to CO2. Adapted from Hütsch et al. [5].
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Figure  2: Regulation of the frmABR operon [6]. In situation A there is no formaldehyde present. FrmR will bind to the promoter region and block the transcription of frmA and frmB. In situation B there is formaldehyde present which reacts with FrmR. FrmR dissociates from the promoter region, allowing transcription of frmA and frmB. Adapted from Zhang et al. [6].

Toxin-antitoxin system: hok/sok

To link the cell density to cell survival, the circuit needs to be coupled to something that can kill the cell. For this, we chose the hok/sok toxin-antitoxin system BBa_K1783001. This system normally ensures plasmid maintenance [7,8]. The Hok protein can kill the cell by damaging the cell membrane [7,9]. Translation of the hok mRNA is inhibited by sok RNA by binding the hok mRNA, thereby blocking ribosome access [9]. When the cell loses the plasmid, there is no new transcription of hok and sok (m)RNA. The sok RNA is very unstable with a half-life around 30 seconds while the hok mRNA is relatively stable with a half-life of 20 minutes [10]. After plasmid loss, only hok mRNA will be left in the cell, which is translated to Hok protein, killing the cell [7,11] (see Figure 3).

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Figure  3: Simplified scheme of a toxin/antitoxin system for plasmid maintenance [11]. Transcription of the sense strand (αm) will produce toxin (hok) mRNA, while the transcription of the anstisense strand (αs), will give antitoxin (sok) mRNA. The hok mRNA can be translated to a toxic protein (αp) (top part), but the binding of sok mRNA to the hok mRNA (h+) will prevent translation of the protein. The hok/sok mRNA complex can dissociate again (h-) and all the compounds can degrade with the rates β. When the plasmid is inside the cell, the sok mRNA will inhibit toxin production. However, when the plasmid is lost, no new hok and sok mRNA will be produced. Sok mRNA degrades much faster βs) than hok mRNA (βm), so after some time there is only hok mRNA left, that can be translated to the toxic protein, which will kill the cell. Taken from Gong et al.

LacI

The combination of the frmR gene and the hok/sok toxin-antitoxin system should already increase the sok production upon high formaldehyde concentrations. However, we also want the hok concentration to drop in high formaldehyde conditions and to be able to do this, the negative regulation of the FrmR protein should be changed to positive regulation. To solve this problem, the additional layer of LacI is added to the circuit so we have a double negative regulation system. The lacI gene produces LacI, which is a repressor protein for the lac promoter [12].

Safety circuit

All the pieces described before will be part of the methane-dependent kill switch, which is supposed to kill E. coli when the methane concentration is too low (see Figure 4.1). The microorganisms can be either in the filter, where the methane concentration is relatively high, or escape from the filter, where the methane concentration is relatively low. We hypothesized how the system will respond to both conditions as described below.

High methane concentrations

When the microorganisms are in the filter, the methane concentrations are relatively high. The methanol and formaldehyde concentrations will also increase. The formaldehyde will bind to the repressor protein FrmR and upon binding, the repressor will dissociate from the frm operator. When the FrmR protein is dissociated from the operator, both the antitoxin and LacI will be produced. LacI will bind to the lac operator and repress the transcription of hok. If there is still some hok mRNA produced, the sok RNA will bind to it, thereby blocking ribosome access. No, or very little, toxin will be produced, so if the microorganisms are in the filter, they will survive (see Figure 4.2).

Low methane concentrations

When E. coli escapes from the filter, the methane concentrations will be lower. Therefore, the formaldehyde concentrations in the cell will decrease and the FrmR repressor protein will stay bound to the frm operon. Therefore, lacI and the antitoxin will not be expressed. When there is no LacI produced, the repression of hok will stop, so toxin mRNA will be produced. As there is also no sok RNA produced anymore to inhibit translation, Hok will be produced, which will damage the cell and eventually kill it (see Figure 4.3).

Figure  4.1: The proposed methane-dependent kill switch, which is supposed to kill E. coli if the formaldehyde concentration in the cell is low. As methane is converted to formaldehyde, this circuit should be sensitive to changes in methane concentrations. This system will be built in the synthetic methanotroph E. coli.
Figure  4.2: When the formaldehyde concentration is high, formaldehyde will bind to the FrmR protein which will then dissociation from the Pfrm promotor, thereby allowing transcription of LacI and antitoxin mRNA. LacI will repress the production of toxin mRNA.
Figure  4.3: When the formaldehyde concentration drops, there is more free FrmR that binds to the Pfrm promoters. This will inhibit the transcription of the antitoxin and LacI. When there is less LacI present, the transcription of toxin mRNA will not be inhibited anymore. The organism will then be killed due to the production of the toxin that irreversibly damages the cell membrane of the organism.

For predicting whether the system is sensitive enough to formaldehyde to kill E. coli when the formaldehyde concentration is low, and keep it alive when the formaldehyde concentration is high, a model can provide great insights in the dynamics. Therefore a model of ordinary differential equations (ODEs) was built.

Approach

Toy model

As there was no experimental data available to fit the methane-dependent kill switch at this stage of our project, we decided to make a 'toy model' first based on the research done by Peng et al. [13] (see Figure 5). They examined the toxicity of the Hok protein in Erwinia amylovora. The hok gene was put under the control of the lac promoter so they were able to regulate its expression by the addition of IPTG. The toy model helped to give a better estimation of the parameters related to hok/sok interactions that are used later in the full model. All models can be found on our GitHub page.

  • Click here to find sources of known parameter values
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    Description Value Reference
    Half-life sok mRNA 30 seconds [9]
    Half-life hok mRNA 20 minutes [9,10]
    Half-life LacI protein 50 minutes [14]
    Binding rate hok and sok 3·105 M-1 s-1 [10]
    Binding FrmR Hill function 26.3 ± 15.6 μM [15]
    Binding LacI Hill function 1.5 ± 0.4·10-11 M [16,17]
    High formaldehyde concentration 18 μM [18]
    Low formaldehyde concentration 9 μM [18]
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Figure  5: Schematic overview of the toy model that is used to give a better estimation of some of the parameter values of the full model. IPTG is activating the transcription of the hok mRNA, while the sok mRNA transcription is assumed to be constant. Sok mRNA will bind to hok mRNA.
  • Curious what model assumptions were made?
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    To be able to make the toy model, several assumptions are made.

    1. The production of sok is constant as this was unregulated [13].
    2. The transcription rate of the Hok protein can be modelled by using a Hill equation, in which ITPG is activating the promoter [19].
    3. The toxicity of the hok/sok toxin-antitoxin system is determined by the ratio between hok mRNA and sok RNA (from now on called ‘hok/sok ratio’) present in the cell [13]. A hok/sok ratio of 18 is not toxic for the cell and a hok/sok ratio of 6000 is lethal.
    4. The initial concentrations of the molecules in the cell are zero.

Using these assumptions, three ODEs were set up to be able to simulate the data. The first ODE describes the change of sok RNA concentration over time. The second ODE describes the change in the hok /sok mRNA complex over time and the last ODE describes the change in hok mRNA concentration over time.

  • Click here to see the ODE’s
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    In the ODE's α1 is the transcription rate of the sok gene, α2 is the background transcription rate of the lac promoter and α3 is the transcription rate of the lac promoter controlled by IPTG. βsok, βhoksok and βhok are the degradation rates of sok RNA, hok /sok mRNA complex and hok mRNA respectively. K1 is the binding rate of hok and sok and K-1 is the unbinding rate of the hok/sok mRNA complex. K3 is the binding of IPTG to the lac promoter in the Hill equation and n is the Hill coefficient.

The model was simulated using 100,000 random parameter sets that were created using Latin hypercube sampling. Parameter sets that were selected that gave a hok/sok ratio between 10-30 without IPTG induction and between 5000-7000 with IPTG induction and the 95% confidence intervals were used as model bounds in the full model.

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    Table  2: Median values of the good parameter sets from the toy model and their 95% confidence intervals. The parameter sets that were selected gave a hok/sok ratio between 10-30 without IPTG induction and between 5000-7000 with IPTG induction. The toy model was simulated using Eq. 1-3
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Full model

The next step is to model the full methane-dependent kill switch. The hok/sok part of the circuit is already modelled using the toy model. In the full model, the formaldehyde sensitivity is added using the FrmR protein and the Pfrm promoter (see Figure 4).

  • Curious what model assumptions were made?
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    To be able to make the full model, several assumptions are made.

    1. The binding of LacI to the lac promoter can be modelled using a Hill function [14,16,20].
    2. The binding of FrmR to the Pfrm promoter can be modelled using a Hill function [15].
    3. The degradation rate of the formaldehyde-FrmR complex is the same as the degradation rate of the FrmR protein.
    4. The toxicity of the hok/sok toxin-antitoxin system is determined by the hok/sok ratio present in the cell [13]. A hok/sok ratio of 18 is not toxic for the cell and a hok/sok ratio of 6000 is lethal.
    5. An estimation of high and low formaldehyde concentrations is made based on Woolston et al. [18]. A high formaldehyde concentration ([FA]high) is 18 μM and a low formaldehyde concentration ([FA]low) is 9 μM.
    6. The initial concentrations of the molecules in the cell are zero.

The model was simulated using eight ODEs which described the change of frmR mRNA, FrmR protein, FrmR-formaldehyde complex, flacI mRNA, LacI protein, sok RNA, hok/sok mRNA complex and hok RNA concentrations over time, respectively.

  • Click here to see the ODE’s
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    In these equations, μ, β, γ, K, and n represent transcription rates, degradation rates, binding/unbinding rates, translation rates and Hill coefficients respectively.

The model was simulated with 100,000 random parameter sets and the sensitivity of the circuit to formaldehyde was calculated using:

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Sensitivity analysis was done to investigate which parameters have an influence on the sensitivity of the system to the formaldehyde concentration and which parameters are important for the hok/sok ratio that is obtained. The parameters that have a hok/sok ratio between 15-25 after both 12 and 48 hours are selected and their parameter values were perturbed one by one to either ten times higher or ten times lower than the original value. With these new parameter sets, the system was re-run and the new sensitivities were compared with the old sensitivities.

None of the parameter sets showed formaldehyde sensitivity above 100. Therefore, two model extensions were made with two goals:

  1. Increase the sensitivity of the system to formaldehyde.
  2. Increase the hok/sok ratio (preferably in a formaldehyde dependent manner).

Extensions

Two of the extensions showed an improvement of the circuit:

The goal of extension 1 is to increase the sensitivity of the system. This is done by adding a formaldehyde competitor to the system, which is produced in a formaldehyde-dependent manner (see Figure 6). The formaldehyde competitor will hold FrmR inactive, which is called sequestration. Sequestration has been modelled before for different systems [21,22]. Upon high formaldehyde concentrations, there is less free FrmR, so there will be less inhibition of Pfrm. The competitor will be produced and bind to the free FrmR, which will lower the free FrmR concentration even further. When the formaldehyde concentration is low, there is more free FrmR, which means more repression of Pfrm. Less competitor will be produced, so a bigger part of the free FrmR will stay unbound.

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Figure  6:  In extension 1, a competitor protein is added to the original methane-dependent kill switch. This competitor protein will bind to free FrmR similarly as formaldehyde does. The competitor is produced in a formaldehyde-dependent manner, which could increase the sensitivity of the system to formaldehyde (see text for more details).
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To give a bigger increase in the hok/sok ratio, protein X is put under control of the lac promoter. Protein X activates promoter Px. Production of more X and Hok is put under control of this promoter, giving a positive feedback loop which could increase the Hok concentration (see Figure 7).

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Figure  7:  Extension 7 has a positive feedback loop on hok production, which could increase the hok/sok ratio a lot (see text for more details).
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Combined extensions

A combination of extension 1 and 7 was made to couple the improved formaldehyde sensitivity with the increased hok/sok ratio. An overview of the circuit can be seen in Figure 8.

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Figure  8:  In extension 1+7, the competitor protein that binds to FrmR is used because it shows a high sensitivity for some hours as the increase in hok/sok ratio happens much later in high formaldehyde conditions compared to low formaldehyde conditions. This is combined with extension 7, which increases the hok/sok ratio (see text for more details).
  • Click here to see the ODE's
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    In these equations, μ, β, γ, K, and n represent transcription rates, degradation rates, binding/unbinding rates, translation rates and Hill coefficients respectively.

  • Click here to find the values of the parameter sets used
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Results

Original methane-dependent kill switch

The optimal formaldehyde sensitivity is 6000/18 = 333 [13]. We define a good formaldehyde sensitivity as a sensitivity above 100, as that is in the same order of magnitude. For the original circuit, there were no parameter sets found that have a good formaldehyde sensitivity after 48 hours of simulation (see Figure 9).

Time series plots of original biosafety circuit - p-set with highest sensitivity at t=48h
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Figure 9: Time series plots of the parameter set that showed the highest formaldehyde sensitivity after 48h of simulation and that gave a hok/sok ratio between 15-25 after both 12 and 48 hours of simulation. After 48 hours of simulation, both the hok/sok ratio and the formaldehyde sensitivity did reach a steady state. The change of the concentrations of the compounds over time are plotted in the first eight plots. The bottom left plot shows the change in hok/sok ratio over time and in the bottom right the sensitivity is plotted over time. The blue line shows the dynamics of the system in low formaldehyde conditions (9 μM) and the orange line in high formaldehyde conditions (18 μM).

A sensitivity analysis (SA) was done to see which parameters have a big influence on the sensitivity of the system to formaldehyde, which showed that the binding (K1) and unbinding rates (K-1) of formaldehyde and FrmR are very important for the formaldehyde sensitivity of the system. Other parameters show little to no change in the sensitivity of the system. In Figure 10, the time series plots are shown of the parameter set that shows the biggest increase in formaldehyde sensitivity when the binding rate of formaldehyde and FrmR is multiplied by ten. Even though the sensitivity increased, it is still not sufficient for the circuit (see Figure 10).

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    Time series plots - sensitivity analysis - parameter 16 times 10
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    Figure  10: Time series plots of the parameter set that showed the highest formaldehyde sensitivity after 48h of simulation and that gave a hok/sok ratio between 15-25 after both 12 and 48 hours of simulation. After 48 hours of simulation, both the hok/sok ratio and the formaldehyde sensitivity did reach a steady state. The change of the concentrations of the compounds over time are plotted in the first eight plots. The bottom left plot shows the change in hok/sok ratio over time and in the bottom right the sensitivity is plotted over time. The blue line shows the dynamics of the system in low formaldehyde conditions (9 μM) and the orange line in high formaldehyde conditions (18 μM).

Extensions

As both the sensitivity and the hok/sok ratio in low formaldehyde conditions are not high enough yet, two extensions on the original biosafety circuit were made. The extensions have two goals:

  1. Increase the formaldehyde sensitivity of the biosafety circuit. Due to the low formaldehyde sensitivity of the original biosafety circuit, there is only a small difference in the hok/sok ratio between high and low formaldehyde conditions. This difference needs to increase to be able to only kill the cells in the low formaldehyde conditions.
  2. Increase the hok/sok ratio (preferably only in low formaldehyde conditions). The ‘good’ parameter sets that were selected from the original biosafety circuit, have a hok/sok ratio between 15-25 in high formaldehyde conditions. However, the hok/sok ratio in low formaldehyde conditions is also close to 15-25 as the sensitivity is very low, which means that the cells will not die when the formaldehyde concentration is low. Therefore, the hok/sok ratio needs to be increased.

We made new parameter sets for the extensions and looked at whether they influence the formaldehyde sensitivity of the system at 48 hours after the system starts, the maximum sensitivity and the hok/sok ratio that is obtained.

Extension 1

In extension 1, a competitor is added that similarly binds to the free FrmR as formaldehyde does (see section 2.3.1 Explanation of the extensions). In the time series plots of the parameter set with the highest maximum sensitivity, a peak in sensitivity was observed (see Figure 11). Two parameter sets reach a maximum sensitivity above 100.

Time series plots extension 1 - p-set with highest max sensitivity
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Figure  11: Time series plots of extension 1 of the hok/sok ratio (left) and the sensitivity (right) of the parameter set that showed the highest maximum formaldehyde sensitivity. The sensitivity of this system increases after ~ 15 hours and drops after ~30 hours. The dynamics of extension 1 are shown by the ‘e1’ lines, while the ‘FM’ lines show the dynamics of the original biosafety circuit. In the left plot, the lines with circles are simulated in low formaldehyde conditions (9 μM) and the lines without circles are simulated in high formaldehyde conditions (18 μM).

The sensitivity observed after 48 hours is not sufficient to yield a high enough hok/sok ratio in low formaldehyde conditions to kill the cell while keeping it alive in high formaldehyde conditions. However, there is a very high maximum sensitivity. This is caused by the hok/sok ratio going up in low formaldehyde conditions much earlier than in high formaldehyde conditions. We will use the parameter set with the highest maximum sensitivity later on for the combination of extensions.

Extension 7

The goal of this extension is to increase the hok/sok ratio of the system. Compound X is added to the circuit which is expressed under the control of the Plac and activates the Px. In this extension, not only hok is produced under the control of the Px, but also compound X, which creates a positive feedback loop to amplify hok production as can be seen in Figure 7 (see Approach). The end hok/sok ratio of this system shows a great increase for some of the parameter sets. Some parameter sets give a hok/sok ratio of around 6000, as can be seen in Figure 12. This extension will be used to increase the hok/sok ratio of the system and is combined with extension 1. The parameter set that gives a hok/sok ratio of 6000 is used to increase the hok/sok ratio of extension 1.

Time series plots extension 7 - p-set with hok/sok ratio 6000 at t=48h
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Figure  12: Time series plots of the hok/sok ratio and the sensitivity of extension 7. The parameter set that gave a hok/sok ratio of 6000 after 48 hours was plotted. See approach for an explanation of this extension. The original methane-dependent (FM) was plotted as a reference and ‘e7’ are the plots of this extension. Plots were made for both low (9 μM – blue and yellow lines) and high (18 μM – orange and purple lines) formaldehyde conditions.

Combined extensions

We combined extension 1 with extension 7 because some parameter sets of extension 1 showed good sensitivity. The parameter set from extension 1 that we selected shows a sensitivity above 100 between 15.4 and 30.6 hours, which is also what we see when combining the extensions (see Figure 13). In this combined extension, the hok/sok ratio in high formaldehyde conditions increases after 30.6 hours to a ratio that would kill the cells. This system behaviour would be undesired in a closed biofilter, but we were unable to prevent this action with further model modifications. We will discuss the implications of this below.

Time series plots extension 1+7
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Figure  13: Time series plots of the hok/sok ratio (left) and the sensitivity (right) of the combination of extension 1 and 7. This extension shows a peak in sensitivity between 15-30 hours after the system starts. This is because the hok/sok ratio in low formaldehyde conditions increases much earlier than in high formaldehyde conditions. The original biosafety circuit (FM) was plotted as a reference and ‘e1+7’ are the plots of this combined extension. Plots were made for both low (9 μM – blue and yellow lines) and high (18 μM – orange and purple lines) formaldehyde conditions.

Conclusion

To ensure biocontainment of the GMOs, we designed a biosafety circuit in which the FrmR protein is combined with the hok/sok toxin-antitoxin system. We designed the circuit such that we hypothesized that it could kill the cells in low formaldehyde concentrations (outside of the biofilter) while keeping it alive in high formaldehyde concentrations (inside the biofilter). However, given the low methane concentrations, the difference in formaldehyde concentrations in the cell will be small [23]. We modelled the biosafety circuit and showed that the formaldehyde sensitivity is too low. This led us to create hypothetical systems incorporating protein X and a competitor protein to improve formaldehyde sensitivity. Whilst we are able to achieve this goal, we found that the cell population would still die as we could not prevent toxin production within single cells.

To improve the dynamics of the biosafety circuit, a second input signal could be useful. Therefore, we decided to use a hybrid promoter in laboratory experiments which is activated by LuxR and repressed by LacI [2]. This way, quorum sensing can be coupled to the methane kill switch. We hypothesised that this would delay the increase in toxin we have observed in our models.

  • References
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    1. H. Yurimoto, N. Kato, Y. Sakai, Assimilation, dissimilation, and detoxification of formaldehyde, a central metabolic intermediate of methylotrophic metabolism, Chem. Rec. 5 (2005) 367–375. https://doi.org/10.1002/tcr.20056.
    2. Y. Chen, J.M.L. Ho, D.L. Shis, C. Gupta, J. Long, D.S. Wagner, W. Ott, K. Josić, M.R. Bennett, Tuning the dynamic range of bacterial promoters regulated by ligand-inducible transcription factors, Nat. Commun. 9 (2018) 1–8. https://doi.org/10.1038/s41467-017-02473-5.
    3. D. Osman, C. Piergentili, J. Chen, L.N. Sayer, I. Usón, T.G. Huggins, N.J. Robinson, E. Pohl, The Effectors and Sensory Sites of Formaldehyde-responsive Regulator FrmR and Metal-sensing Variant * □, 291 (2016) 19502–19516. https://doi.org/10.1074/jbc.M116.745174.
    4. K.J. Denby, J. Iwig, C. Bisson, J. Westwood, M.D. Rolfe, S.E. Sedelnikova, K. Higgins, M.J. Maroney, P.J. Baker, P.T. Chivers, J. Green, The mechanism of a formaldehyde-sensing transcriptional regulator, Sci. Rep. 6 (2016) 1–15. https://doi.org/10.1038/srep38879.
    5. B.W. Hütsch, Tillage and land use effects on methane oxidation rates and their vertical profiles in soil, Biol. Fertil. Soils. 27 (1998) 284–292. https://doi.org/10.1007/s003740050435.
    6. M. Zhang, X. Yuan, C. Zhang, L. Zhu, X. Mo, Bioconversion of Methanol into Value- added Chemicals in Native and Synthetic Methylotrophs, (2020). https://doi.org/10.21775/9781912530045.13.
    7. K. Gerdes, L.K. Poulsen, T. Thisted, A.K. Nielsen, J. Martinussen, P.H. Andreasen, The hok killer gene family in gram-negative bacteria, 2 (1990) 946–956.
    8. T. Thisted, K. Gerdes, Mechanism of Post-segregational Killing of Plasmid Rl by the hok / sok System Sok Antisense RNA Regulates hok Gene Expression Indirectly Through the Overlapping mok Gene, 223 (1992) 41–54.
    9. K. Gerdes, E.G.H. Wagner, RNA antitoxins, Curr. Opin. Microbiol. 10 (2007) 117–124. https://doi.org/10.1016/j.mib.2007.03.003.
    10. T. Franch, A.P. Gultyaev, K. Gerdes, Programmed cell death by hok/sok of plasmid R1: Processing at the hok mRNA 3’-end triggers structural rearrangements that allow translation and antisense RNA binding, J. Mol. Biol. 273 (1997) 38–51. https://doi.org/10.1006/jmbi.1997.1294.
    11. C.C. Gong, S. Klumpp, Modeling sRNA-regulated plasmid maintenance, PLoS One. 12 (2017) 1–19. https://doi.org/10.1371/journal.pone.0169703.
    12. B. Müller-Hill, The function of auxiliary operators, Mol. Microbiol. 29 (1998) 13–18. https://doi.org/10.1046/j.1365-2958.1998.00870.x.
    13. J. Peng, L.R. Triplett, Activation of metabolic and stress responses during subtoxic expression of the type I toxin hok in Erwinia amylovora, (2020) 1–22.
    14. E. Ullner, A. Zaikin, E.I. Volkov, Multistability and Clustering in a Population of Synthetic Genetic Oscillators via Phase-Repulsive Cell-to-Cell Communication, 148103 (2007) 1–4. https://doi.org/10.1103/PhysRevLett.99.148103.
    15. J. Rohlhill, N.R. Sandoval, E.T. Papoutsakis, Sort-Seq Approach to Engineering a Formaldehyde-Inducible Promoter for Dynamically Regulated Escherichia coli Growth on Methanol, ACS Synth. Biol. 6 (2017) 1584–1595. https://doi.org/10.1021/acssynbio.7b00114.
    16. H. Zhan, M. Taraban, J. Trewhella, L. Swint-Kruse, Subdividing Repressor Function : DNA Binding Affinity , Selectivity , and Allostery Can Be Altered by Amino Acid Substitution of Nonconserved Residues in a LacI/GalR homologue, Biochemistry. (2008) 8058–8069. https://doi.org/10.1021/bi800443k.
    17. L. Swint-Kruse, K.S. Matthews, [ 11 ] Thermodynamics , Protein Modification , and Molecular Dynamics in Characterizing Lactose Repressor Protein : Strategies for Complex Analyses of Protein Structure – Function, 379 (2004).
    18. B.M. Woolston, T. Roth, I. Kohale, D.R. Liu, G. Stephanopoulos, Development of a formaldehyde biosensor with application to synthetic methylotrophy, (2018) 206–215. https://doi.org/10.1002/bit.26455.
    19. M. Pasini, C. De Mas, D. Calleja, A. Fernández-casta, J. López-santín, Quantitative modeling of inducer transport in fed-batch cultures of Escherichia coli, 91 (2014) 210–219. https://doi.org/10.1016/j.bej.2014.08.017.
    20. B. Munsky, B. Trinh, M. Khammash, Listening to the noise : random fluctuations reveal gene network parameters, Mol. Syst. Biol. 5 (2009) 1–7. https://doi.org/10.1038/msb.2009.75.
    21. S.K. Aoki, G. Lillacci, A. Gupta, A. Baumschlager, D. Schweingruber, M. Khammash, A universal biomolecular integral feedback controller for robust perfect adaptation, Nature. (2019) 533–537. https://doi.org/10.1038/s41586-019-1321-1.
    22. T. Frei, C. Chang, M. Filo, M. Khammash, Genetically Engineered Integral Feedback Controllers for Robust Perfect Adaptation in Mammalian Cells, (2020) 1–13.
    23. C. Levrault, Cubicle Hood system, n.d.
About Cattlelyst

Cattlelyst is the name of the iGEM 2021 WUR team. Our name is a mix of 1) our loyal furry friends, cattle, and 2) catalyst, which is something that increases the rate of a reaction. We are developing “the something” that converts the detrimental gaseous emissions of cattle, hence our name Cattlelyst.

Are you curious about our journey? We have written about our adventures in our blog, which you can find here:

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