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Team:USTC CHINA/Modeling/KillSwitch
From 2013.igem.org
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| - | <div style="float:left; margin-left: | + | <div style="float:left; margin-left:350px;margin-top:-300px;border:#333 1px solid; width:300px;">In this group, we gave up one former assumption and set k<sub>2</sub> equal to k<sub>9</sub>. We also gave positive values to I<sub>m</sub>, C<sub>i</sub> and R<sub>i</sub>, which were considered to be zero at first. And by groups of stimulations we realized the value of k<sub>2</sub> does matter, as the derivative of C<sub>f</sub> only increased slightly as k<sub>2</sub> lowers, and the positive values failed to avoid the weird phenomenon in the latter three curves. |
We also found that however we adjusted the primary value of I<sub>f</sub> and other parameters, If dropped into approximately zero extremely rapidly at the initial stage and remained balanced, which might account for why the derivatives of the latter curves were abnormally negative. Thus we modified another assumption and increased k<sub>7</sub>. Here is another group of values and corresponding graph:</div></div></br></br> | We also found that however we adjusted the primary value of I<sub>f</sub> and other parameters, If dropped into approximately zero extremely rapidly at the initial stage and remained balanced, which might account for why the derivatives of the latter curves were abnormally negative. Thus we modified another assumption and increased k<sub>7</sub>. Here is another group of values and corresponding graph:</div></div></br></br> | ||
Revision as of 19:49, 27 September 2013
Introduction
To be more user-friendly, 4# circuit contains a reporting system. After melting in water, the spores germinate and express blue pigment protein to report the best using time. Meanwhile, 4# circuit could also ensure biosafety. Because other circuit do not have self-killing device, 4# engineering bacterial should kill all the bacterial after using.Designing of the suicide system
We design a circuit of killing switch based on its endogenous genetic system. In B.subtilis, when it comes to the stationary phase, the environmental pressure increases and nutrition becomes limited, so B begin to produce spores. Now the community will be divided into two different parts. One of them are trying to kill others to get enough nutrient , delaying the production of spores and achieving a competitive advantage. Killing is mediated by the exported toxic protein SdpC. SdpI will appear on the membrane surface to avoid itself from being damaged. SdpI could bind free SdpC and autopressor SdpR, to remove SdpR’s inhibition against I and R, to produce more SdpI to offset SdpC, finally guaranteeing the subgroup alive, thereby delaying the spores production.
We transfer SdpC which is fused by promoter SdpI/R into high copy plasmids in order to damage the balance of the system, thereby killing whole colony. When SdpC appears, SdpI on the membrane will bind free SdpC and adsorb SdpR to cease its inhibition against SdpI P/R, trying to produce more SdpI. At the same time, it will activate the promoter SdpR/I in our circuits and generate more SdpC.The system would fall into an infinite loop, and according to our modeling ,the amount of SdpC increases beyond the ability of SdpI.Thus,the cells with protection mechanism will crack and die because of too much SdpC. All above forms the killing device. We Also designed a test circuit,which contains promotor grac and sdpABC only,aiming to determine the ability of SdpC.
There are both positive and negative feedback loops in this process. On the one hand, SdpI is unable to sequestrate the autorepressor, SdpR, until it captures the toxin, SdpC. The accumulation of SdpC will thus facilitate SdpI to capture more SdpR and thereby relieve the repression of SdpR, stimulating the expression of itself. This is the positive feedback loop which leads to the increasing accumulation of SdpC and finally the death of the bacteria. On the other hand, the removal of SdpR also enhance the expression of SdpI and accelerate the sequestration of SdpC, which forms a negative feedback loop whose effects contradict the positive feedback loop. However, since the copy number of SdpC is much higher, it is believed that the positive loop is strong enough to outweigh the negative one, which guarantees this mechanism will finally leads to collapse instead of equilibrium.
The ODE model of singular cells
There is no denying fact that the essential goal of engineered bacterias who carry this so called “suicide” locus itself is to kill their siblings rather than themselves to ensure the survival of themselves. Surly they can kill their siblings, but can they finally eliminate themselves, as we expects? The trivial experiment protocol and huge uncertainty had put off our experiment, and as expected, we failed to achieve the construction of complete reporter system in our laboratory. Fortunately, we could resort to mathematical models to verify the validity of this locus theoretically. There are six independent variables in individual cells, and the theoretically if the initial conditions are fixed, all of them will be the univariate functions of time. The following table illustrates the mark and meaning of each variable:To construct reasonable ordinary differential equation (ODE) model to describe and predict the operation of the suicide system, we followed the law of mass action, one basic law of chemistry and biology.
Taken as a statement about kinetics, the law states that the rate of an elementary reaction (a reaction that proceeds through only one transition state, which is one mechanistic step) is proportional to the product of the concentrations of the participating molecules. In modern chemistry this is derived using statistical mechanics. Despite the complicated chemical reactions involved in the process of transcription and translation, it is common and logically sound to view the expression of one particular gene as an elementary reaction and assume the repression effects of the protein itself encodes and the repressor are both linear.
According to the law of mass action, we got six independent differential equation of the variables:
The following table explain the constants in the above ODE groups:
The following table explain the constants in the above ODE groups:| Mark | Meaning |
| Imax | Mole number of free SdpI in cytoplasm. |
| Im | Mole number of SdpI in the cell membrane. |
| Cf | Mole number of free SdpC in cytoplasm. |
| Ci | Mole number of SdpC captured by SdpI. |
| Rf | Mole number of free SdpR in cytoplasm. |
| Ri | Mole number of SdpR captured by SdpI |
| Mark | Meaning |
| Imax | The maximal number of SdpI than can be fixed on the cell membrane. |
| k0 | Constant describes the normal expression rate of SdpC |
| k1 | Constant describes the self-repression effects of SdpC |
| k2 | Constant describes the repression of SdpR on the expression of SdpC. |
| k3 | Constant describes the rate of SdpI capturing SdpC |
| k4 | Constant describes the normal expression rate of SdpR |
| k5 | Constant describes the self-repression effects of SdpR |
| k6 | Constant describes the rate of SdpI capturing SdpR |
| k7 | Constant describes the normal expression rate of SdpI |
| k8 | Constant describes the self-repression effects of SdpI |
| k9 | Constant describes the repression of SdpR on the expression of SdpI |
| k10 | Constant describes the rate of SdpI binding to the cell membrane |
Discussions on the constants
All the constants given above is steady and theoretically measurable when all the conditions are constant. For example, we could measure k0 by constructing a new engineered bacteria, which contains the gene encoding SdpC and marker gene alone and observing the influence of the concentration of SdpC on its expression. Yet any modification on genome is notoriously time-consuming, which inhibited us from measuring them in person. We also looked up oceans of papers to confer their approximate ranges, but almost all papers are too fragmental to afford any valid information. Therefore, we decided to assume all these constant according to our limited information and make a qualitative analysis instead of quantifiable analysis. All units and dimensions were temporarily ignored. In other words, our model aims at justifying the validity of this suicide mechanism rather than predicting the exact time or any other parameters of the system. Despite the fact that we have hardly any accurate data on these constants, there are some limitations that we extrapolated from known information before we further explore this model:- k0>>k4≈k7: k0,k4 and k7 represent the normal expression rate of SdpC, SdpR and SdpC separately, and the copy number of SdpC is much larger than that of SdpR and SdpI, whereas the value of the latter two is approximately equal;
- k2>>k9: the existence of free SdpR represses the expression of both SdpI and SdpC, and similarly, since the copy number of SdpC is much higher, we expected the repression effect was stronger accordingly;
- k10>>k3,k6：it is hard to predict the value of k3 and k8, yet we suppose both of them is much smaller than k10 because SdpI is a kind of membrane protein inherently, and rarely exists as free protein
- The primary values of all the six variables are very small or strictly zero. We expect it as the most logical initial status. If the primary value of any variable is relatively large, the suicide mechanism may not run normally
Stimulation and discussion
Simple and rough as the above model is, it does theoretically sound. To test the validity of this model, we first tried to get analytic solution of the ODE set. If this analytic solution exists, we could further investigate the interaction among those variables, and draw some phase planes to get accurate and mathematically perfect description of this model. Unfortunately but expectedly, the existence of analytic solution was negated by MATLAB, and we had to assume groups of values for these constants in advance and analyze the arithmetic solutions instead. These arithmetic solutions not only justified this mechanism is effective enough to commit cell suicide but also indicated some unexpected, or even weird results that beyond our wildest imagination. There are two possibility account for the unexpected results: our model is too rough to include some assignable factor; or there are some implicit but objective limitation inside model, which may be substantiate by later experiments or papers. When we explored the arithmetic solutions of this ODE set, we received nearly one hundred warnings from MATLAB and for many times our most powerful computer ran out of its 8GB memory, but sometimes we can receive the solution within seconds. We had adjusted our parameters for several times before we got our first solution. Here is the values of parameters for this group, and the graph of arithmetic solutions is also given:| k0 | k1 | k2 | k3 | k4 | k5 | k6 | k8 | k8 | k9 | k10 | Imax | Cf0 | Rf0 | If0 | Im0 | Ci0 | Ri0 |
| 50 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 20 | 500 | 5 | 5 | 1 | 5 | 3 | 2 |
At the first glance this graph seemed fine. Initially the concentration of SdpC decreased slightly due to the capturing of SdpI and the repression of float SdpR, but gradually the positive feedback loop works, and Cf increases rapidly. But when we turned our attention to the curves of other parameters, things seemed not so perfect:
The curve of Im, Ci and Ri contradicted our common sense severely. First, Im>Ci>Ri is expected to be tenable all the time, which precludes the intersects among the three curves; Second, there is no mechanism in this system that could decrease their concentration, and all of them are increasing function; Third and most serious, never will them be negative, as they represent the concentration of real substances.
Then we adjusted the parameters slightly for several times. To eliminate those absurd curves, we reconsidered some assumptions.
Here we listed another representative group of parament values and relative graph:
| k0 | k1 | k2 | k3 | k4 | k5 | k6 | k8 | k8 | k9 | k10 | Imax | Cf0 | Rf0 | If0 | Im0 | Ci0 | Ri0 |
| 100 | 5 | 5 | 5 | 5 | 5 | 5 | 20 | 5 | 5 | 20 | 500 | 5 | 5 | 1 | 5 | 3 | 2 |
| k0 | k1 | k2 | k3 | k4 | k5 | k6 | k8 | k8 | k9 | k10 | Imax | Cf0 | Rf0 | If0 | Im0 | Ci0 | Ri0 |
| 100 | 5 | 5 | 5 | 5 | 5 | 5 | 20 | 5 | 5 | 20 | 500 | 5 | 5 | 1 | 5 | 3 | 2 |
In this group, we gave up one former assumption and set k2 equal to k9. We also gave positive values to Im, Ci and Ri, which were considered to be zero at first. And by groups of stimulations we realized the value of k2 does matter, as the derivative of Cf only increased slightly as k2 lowers, and the positive values failed to avoid the weird phenomenon in the latter three curves.
We also found that however we adjusted the primary value of If and other parameters, If dropped into approximately zero extremely rapidly at the initial stage and remained balanced, which might account for why the derivatives of the latter curves were abnormally negative. Thus we modified another assumption and increased k7. Here is another group of values and corresponding graph:
Although the derivative of Im is not seriously positive constantly, the three latter curves seemed much more reasonable. Hence, we extrapolated although SdpI and SdpR share the same promoter, the expression of SdpI must much faster than SdpR to ensure successful “suicide.” Additionally, the increase of k7 also represses SdpC, and hence the copy number of SdpC must be larger.
We kept all other parameters constant and gradually augmented k0. The larger k0, the more perfect the curve seemed, and here are the values table and graph where k0 equals 400, 80 times larger than k4.
| k0 | k1 | k2 | k3 | k4 | k5 | k6 | k8 | k8 | k9 | k10 | Imax | Cf0 | Rf0 | If0 | Im0 | Ci0 | Ri0 |
| 400 | 5 | 5 | 5 | 5 | 5 | 5 | 20 | 5 | 5 | 20 | 500 | 5 | 5 | 1 | 5 | 3 | 2 |
Take the curve of Cf and Rf separately, the curves seemed more perfect:
In wild bacteria who are unable to produced SdpC, naturally k0 equals zero. We expected Cf would decreased gradually and finally approximate zero, and here are the corresponding table and graph:
| k0 | k1 | k2 | k3 | k4 | k5 | k6 | k8 | k8 | k9 | k10 | Imax | Cf0 | Rf0 | If0 | Im0 | Ci0 | Ri0 |
| 0 | 5 | 5 | 5 | 5 | 5 | 5 | 30 | 5 | 5 | 20 | 500 | 8 | 5 | 1 | 5 | 3 | 2 |
Wired but not surprising, there were intersects among the latter three curve, and Cf decreases continually when it is negative. We continued to try groups of these parameters, and this is the best one where we increased the primary concentration of SdpC and the normal expression rate of SdpI.
| k0 | k1 | k2 | k3 | k4 | k5 | k6 | k8 | k8 | k9 | k10 | Imax | Cf0 | Rf0 | If0 | Im0 | Ci0 | Ri0 |
| 0 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 20 | 500 | 8 | 5 | 1 | 5 | 3 | 2 |
The curve of Cf and Rf alone:
In spite of minimal abnormal phenomenon (Cf was negative in later stage), this graph roughly testified that in wild bacterial the concentration of float SdpC will drop to nearly zero quickly.
In sum, the ODE model of singular cells indicate following results:
- The character of free SdpC is most affected by k0, if the copy number of SdpC is large enough, it is theoretically reasonable to commit suicide;
- The influence of the value of Imax and k2 is much limited;
- The amount of free SdpI is always near zero;
- SdpC will not increase limitlessly however we transform parameters;
- To ensure the success of suicide, it is required k0>>k4>>k7;
The last conclusion was our biggest windfall, and we have verified the validity of this suicide mechanism in math. On the one hand, if further experiments proven #4 engineered bacteria will kill both siblings and themselves, it is highly like that the expression rate SdpI is much larger than SdpR even if they share the same promoter; on the other hand, if #4 engineered bacteria are not able to commit suicide, we can try to boost the expression of SdpI to adjust the bacteria.
