Mathematical Models

We created several different models, each to try and better predict how our system would work and to inform future tweaks and redesigns of our project.

They include a model of how gene expression is effected by copy number, a model of our intended improvement on BBa_J04450, fixing leaky expression by inserting an double inverter, a model on our LuxR-MazE/F kill switch. Most relevant to the project is our model on the biosensor itself, and different iterations of the design.

Modeling the Effects of Plasmid Copy Number with BBa_J04450

To better understand how our circuit would work, we first wanted to model the effect of plasmid copy number. To do that, we used the commonly used and well characterized part BBa_J04450.

We and others have observed that this part has leaky expression. One explanation for this is that the high copy number of the plasmid titrates the LacI repressor protein. JNFL2020 improved this part by including a copy of LacI repressor on the plasmid, which increases the concentration of LacI and restores the situation normally present in the original LacI operon. (Their improved part BBa_K3605010.can be found on the registry).

Our model does not represent binding of LacI to the DNA as a separate reaction – this is in part because it is easier to find the disassociation constant for LacI:DNA interactions than it is to find the exact Kon and Koff rates for binding.

The typical way for modeling repressor binding is by rearranging the Hill-Equation. Starting from the following assumptions:

(pLAC0 is the total or initial concentration of the promoter; pLAC is the concentration of unbound promoter, while pLAC:LacI is the bound promoter repressor complex. Kd is the disassociation constant, which is estimated to be 0.1nM – see references below)

You can rearrange these to form:

However, we wanted a model that can operate based only on KD and the initial concentrations of both the plasmid borne gene and LacI. This is because we need a model that will respond to titration of available LacI – the above is insufficient, since it assumes saturation of the promoter is always possible (but it isn’t when LacI is limiting). To get this improved model, we needed to carry out some fancy algebraic rearranging (with help from Wolfram Alpha) to get the following expression for unbound promoter:

This was incorporated into our model and gave the predicted results: the transcription rate responds to LacI concentration and binding affinity as long as LacI is not limiting. Once it is titrated, all available promoters are transcribed constantly.

Our Model:

Using the BIONUMB3R5 database, we completed the parameters for our model – these are found below, or by clicking on the constants in the above equations.

Now, we simulated the model, running it for 100 seconds or longer with varying plasmid copy numbers (from 0 to 300) and varying amounts of IPTG (0mM to 1mM, or 1000uM).

This is an example time course of our experiment, varying plasmid copy number with 0mM IPTG. As you can see, RFP production levels off as we approach 1000 seconds (about 16 minutes). Increasing plasmid copy number leads to increased expression. At greater than 8nM (or about 5 copies of plasmid per cell), there starts to be increasing amounts of RFP accumulation – a small amount at 11nM (7 copies per cell). From that level, an increase to 17nM results in a four fold increase (approximately 10 or 11 copies per cell). 18 plasmids per cell, or about 30nM, results in a further 3 fold increase.

We found that the effects of induction were minimal at all but the lowest copy numbers. Plotting copy number against maximum induction (i.e. the ratio between expression induced at 1mM versus the uninduced), we see this optimal point at a copy number of 13 (lucky #13!). This makes sense in light of the fact that there are typically only 10 copies of the LacI tetramer in the cell at a given time. Extending the graph further leads to values of 467 RFP for 100x PCN, and 1496 RFP for 300x PCN – indicating a linear relationship between RFP and PCN once LacI is titrated out.

From this model, we would conclude that low copy number plasmids are more desirable for fine-tuning expression. We hope that this model represents a useful contribution to iGEM team, since it is different than the standard way to approach the modeling of transcriptional repression, but one that seems to better explain the leaky behavior of the R0010 promoter.

Assumptions in our model include ignoring the effect of constant plasmid replication on the transcription activity of RFP. RNA polymerase might be engaged in creation of the replication primer at the origin sequence, or DNA polymerase might collide with the RNA polymerase that is trying to express RFP. This way, paradoxically, a high copy number can actually diminish gene expression. We will work to incorporate this into our model in the future.

Our model matches the trends in experimental observations fairly well. Most of the time, when copy number is increased, expression is increased, and vice-a-versa. Taking the above assumptions into account, this may explain why we see more expression for the +8 mutation, but a total collapse of expression in the +1 mutation, which has an even higher PCN.

Additional References and Parameter Values:

Additional References:
Cranenburgh R, M, Lewis K, S, Hanak J, A, J: Effect of Plasmid Copy Number and lac Operator Sequence on Antibiotic-Free Plasmid Selection by Operator-Repressor Titration in Escherichia coli. J Mol Microbiol Biotechnol 2004;7:197-203. doi: 10.1159/000079828

Double Inverter Model

Expression of genes from high copy number plasmids can be leaky. Indeed, one need not look further than the commonly used part BBa_J04450, which often appears red in the absence of the inducer IPTG. We were able to model this effect and found that copy numbers exceeding 10 or 20 render control by LacI ineffective, simply because there are typically only 10 copies of LacI in a given cell and they are titrated out by increasing amounts of plasmid (as our other model shows).

Putting two inverters in between the promoter and the RFP on BBa_J04450 ought to preserve the logic of this BioBrick – in the presence of IPTG, the first inverter represses the second, and RFP is produced. However, it is possible that the intermediate steps and the changed dynamics of this process might help solve the issue of the leaky expression.



The double inverter concept. Diagram created using Pigeon

We built such a construct in the lab (see our new parts, ), but also sought to model their behavior. So we extended our basic BBa_J04450 model as follows:


  • d(lacI)/dt = 1/Original*(-(kf*IPTG*lacI-kr*inactive_lacI))
  • d(tetR_mRNA)/dt = 1/Original*((tetTrxnF*(0.5*(sqrt((KdnaL-pLAC+lacI)^2+4*KdnaL*pLAC)-KdnaL+pLAC-lacI))) - (tettRNAdeg*tetR_mRNA))
  • d(TetR)/dt = 1/Original*((tetTrslF*tetR_mRNA) - (tetDeg*TetR))
  • d(IPTG)/dt = 1/Original*(-(kf*IPTG*lacI-kr*inactive_lacI))
  • d(inactive_lacI)/dt = 1/Original*((kf*IPTG*lacI-kr*inactive_lacI))
  • d(cI_mRNA)/dt = 1/Original*((cTrxnF*(0.5*(sqrt((KdnaT-pTET+TetR)^2+4*KdnaT*pTET)-KdnaT+pTET-TetR))) - (cTrnslF*cI_mRNA) - (cRNAdeg*cI_mRNA))
  • d(cI)/dt = 1/Original*((cTrnslF*cI_mRNA) - (cDeg*cI))
  • d(rfp_mRNA)/dt = 1/Original*(-(rfpTrnslF*rfp_mRNA) + (rfpTrxnF*(0.5*(sqrt((KdnaC-pCI+cI)^2+4*KdnaC*pCI)-KdnaC+pCI-cI))) - (rfpRNAdeg*rfp_mRNA))
  • d(RFP)/dt = 1/Original*((rfpTrnslF*rfp_mRNA) - (rfpDeg*RFP))

Using the parameters listed below, we simulated our model under the similar conditions and time frame that we simulated our original model of BBa_J04450. Below is a comparison of the two models over the same conditions and time frame.

In the above graphs, IPTG concentrations were varied from 0-1000uM in 250uM steps, and plasmid copy number was varied from 0 to 64. One striking result is that IPTG induction has little effect on most of these simulations – when plasmid copy number is high enough, the leaky expression is almost as strong as IPTG induction.

Another observation is that the simple BBa_J04450 continually produces RFP, while the double inverter seems to level off at a lower level. So, at least it appears to have diminished the extent of the leaky expression.

Next we wanted to see what happened to our double inverter if it is allowed to run longer – up to 2 hours. We observed a curious result:

The above is a comparison of three different time frames. Notice that the Y-axis, RFP production, is different in each – a line drawn across acts as a guide to compare between graphs. The steady state level reached at 100 seconds is not stable, but rather dips and then leads to increased RFP production. Once again, IPTG seems to have little effect (the arrangement between IPTG and PCN is the same as the first set of graphs). Next, we wanted to make a direct comparison between both models at 2hrs

As you can see, the double inverter model shows that expression is much diminished compared to the original. Unfortunately, expression is not restored or elevated by addition of IPTG to a greater amount than compared to wild-type. Perhaps TetR or cI can be modified to make them more unstable, allowing recovery of a high level of expression when desired.
It is satisfying in a way that this model matches well the observations we made with the constructed part. The design-build-test cycle in action!

References and Parameters

See BBa_J04450 model section for LacI, pLAC, and IPTG parameters

Model of our Biosensor

Our basic biosensor circuit is based upon three genes: an estrogen receptor (here from rainbow trout), an inverter (here, TetR repressor), and an output protein (RFP). We wanted to model how well our biosensor could detect molecules such as DDT, an important pollutant. We setup our model as follows


  • d(rtER_mRNA)/dt = 1/cell*((rtPro*rtER_gene) - (rtRNADeg*rtER_mRNA))
  • d(rtER)/dt = 1/cell*((rtRBS*rtER_mRNA) - (KonDDT*rtER*DDT-KoffDDT*rtER_active) - (rtDeg*rtER))
  • d(tetR_mRNA)/dt = 1/cell*((tetPro*(0.5*(sqrt((KdERDNA-tetR_gene+rtER_active)^2+4*KdERDNA*tetR_gene)-KdERDNA+tetR_gene-rtER_active))) - (tetRNADeg*tetR_mRNA))
  • d(tetR)/dt = 1/cell*((tetRBS*tetR_mRNA) - (tetDeg*tetR))
  • d(RFP_mRNA)/dt = 1/cell*((rfpPro*(0.5*(sqrt((KdnaT-RFP_gene+tetR)^2+4*KdnaT*RFP_gene)-KdnaT+RFP_gene-tetR))) - (rfpRNADeg*RFP_mRNA))
  • d(RFP)/dt = 1/cell*((rfpRBS*RFP_mRNA) - (rfpDEG*RFP))
  • d(rtER_active)/dt = 1/cell*((KonDDT*rtER*DDT-KoffDDT*rtER_active))

Using parameters listed below (and assuming a plasmid copy number of 200) we ran the model at various concentrations of DDT, in nM. Representative results are shown below


RFP production (Y-axis) is plotted against time, from 0 to 5000 seconds (about 1 hour). Each line represents a different amount of DDT, in nM (or parts per billion). These range from 0 (blue), 0.05 (orange), 0.1 (gold), 0.15 (purple), 0.2 (green), 0.5 (light blue), or 5 (maroon).

This model suggests that the biosensor will work, although RFP production is low even when DDT is detected. We may need a single amplifying module or a better way of detecting RFP in order to verify our design.

Parameters and References

Other values are taken from the other models featured in our project, or from our background research on estrogen receptors, which can be found on our contributions page

Advanced Biosensor Designs

Introduction

For our biosensor, we designed five different genetic circuits. Each with the general goal of detecting DDT (Dichlorodiphenyltrichloroethane). It is not efficient for our team to construct each circuit in wet-lab, and then determine whether it works well. We decided to utilize math modeling to predict how each circuit will function. This was done by the dry-lab team. Each model shows what the circuit is predicted to produce at different given levels of DDT. They also each have specific goals for detection.

This dry-lab work was completed by Isabelle Conn, Alexa Dekorte, Syrine Ben Driss, and Paige Lamoreaux.

Parameters Key for All Circuits:

trxn: Transcription

trnsl: Translation

deg: Degradation

d: Disassociation

K: Constant

lac: Lac Promoter

Ktrnsl (general): 2360.0 time

Ktrxn (general): 0.88 substance

Kdeg (general): 2000000.0 time

KtrnslhERa: 2360.0 time

KtrnslTet: 2360.0 time

KtrxnRFP: 0.88 time

Kd (general): 1.0 E13 second

KtrnslRFP: 0.009 time

KdegRFPmRNA: 2000000.0 time

Ktrxnlac: 2470000.0 time

Parameter Resources:

We found our parameters from these two main sources and made estimations of values based on patterns in these sources:

2019 HZAU-China.

2020 Alma.

Circuit #1:

Idea – In this circuit, the rainbow trout estrogen receptor (rtER) will be able to bind and prevent expression of any gene that is controlled by the Lac promoter with the appropriate operator (rtERE, or Estrogen Response Element). The human estrogen receptor (hERa) will likewise prevent transcription of pTet since there is a hERE element there. Both rtER and hERa can bind both DDT and Estradiol but do so with different affinities (in other words, the kd for rtER binding DDT is lower than that for hERa binding DDT).

Goal – We strived to maximize the RFP signal when and only when DDT is present – the presence of Estradiol should inhibit the expression.

Math Model:

m = was used sometimes to denote mRNA

Species Key: s12= hERa and TetR gene, s18= hERa and TetR mRNA, s29= activated rtER protein, s19= TetR protein, s17= inactivated hERa protein, s25= RFP gene, s31= activated hERa protein, s24= RFP mRNA, s27= RFP protein, s49= Estradiol, s39= rtER gene, s38= rtER mRNA, s47= rtER protein, s50= DDT

Reactions (Equations):

· Transcription of TetR and hERa mRNA repressed by activated rtER:

s12 * Ktrxn * 1 / 1 + pow(s29 / Kd, 2) - Kdeg * s18

· Translation of TetR:

s18 * KtrnslTet - Kdeg * s19

· Translation of hERa:

s18 * KtrnslhERa - Kdeg * s17

· Transcription of RFP repressed by TetR and activated hERa:

s25 * KtrxnRFP * 1 / 1 + pow(s19 / Kd, 2) * 1 / 1 + pow(s31 / Kd, 2) - KdegRFPmRNA * s24

· Translation of RFP:

s24 * KtrnslRFP - Kdeg * s27

· Activation of hERa:

1 + pow(Kd * (s17 * s49 / Kd), 2) - Kdeg * s31

· Transcription of rtER:

s39 * Ktrxn - Kdeg * s38

· Translation of rtER:

s38 * Ktrnsl - Kdeg * s47

· Activation of rtER:

1 + pow(Kd * (s47 * s50 / Kd), 2) - Kdeg * s29

Circuit #2:

Idea – Here, the goal is to create/model a circuit that is able to produce one color (green) at intermediate levels of DDT, and another color (red) at dangerously high levels. In this circuit, the Estrogen receptor blocks expression of both cI and TetR repressor. Estrogen receptor, when activated, blocks production of both cI and TetR. GFP responds to only TetR, and so is somewhat less sensitive (and can be tuned with aTC), while RFP is repressed by both, and levels of TetR and cI must decrease to a sufficient level to allow expression of this color.

Goal – We wanted to be able to distinguish between a low (1uM) and high (40uM) level of DDT.

Math Model:

Species Key: s1= cI and TetR gene, s18= activated hERa protein, s2= cI and TetR mRNA, s3= cI protein, s7= GFP gene, s6= TetR protein, s8= GFP mRNA, s9= GFP protein, s10= RFP gene, s11= RFP mRNA, s12= RFP protein, s13= hERa gene, s14= hERa mRNA, s16= DDT, s17= inactivated hERa protein

Reactions (Equations):

· Transcription of TetR and cI repressed by activated hERa:

s1 * Ktrxnlac * 1 / 1 + pow(s18 / Kd, 2) - Kdeg * s2

· Translation of cI:

s2 * Ktrnsl - Kdeg * s3

· Transcription of GFP repressed by TetR:

s7 * Ktrxn * 1 / 1 + pow(s6 / Kd, 2) - Kdeg * s8

· Translation of GFP:

s8 * Ktrnsl - Kdeg * s9

· Transcription of RFP repressed by TetR and cI:

s10 * KtrxnRFP * 1 / 1 + pow(s3 / Kd, 2) * 1 / 1 + pow(s6 / Kd, 2) - KdegRFPmRNA * s11

· Translation of RFP:

s11 * KtrnslRFP - Kdeg * s12

· Transcription of hERa:

s13 * Ktrxn - Kdeg * s14

· Translation of TetR:

s2 * KtrnslTet - Kdeg * s6

· Translation of hERa:

s14 * KtrnslhERa - Kdeg * s17

· Activation of hERa:

1 + pow(Kd * (s17 * s16 / Kd), 2) - Kdeg * s18

Circuit #3:

Idea – Here, the goal is to create/model a circuit that is able to produce one color (green) at intermediate levels of DDT, and another color (red) at dangerously high levels – this is the idea, although in this circuit the colors might have been swapped! In this circuit, TetR is under the control of at least two tandem hERE elements – so repression of this gene is easier to achieve by the estrogen receptor, and less DDT is necessary to repress it. You might reflect this by changing the hill coefficient or otherwise multiplying the repressor binding term in this model.

Goal – We wanted to be able to distinguish between a low (1uM) and high (40uM) level of DDT.

Math Model:

Species Key: s13= TetR gene, s14= TetR mRNA, s15= TetR protein, s16= hERa gene, s17= hERa mRNA, s18= inactivated hERa, s19= cI gene, s20= cI mRNA, s21= cI protein, s22= GFP gene, s23= GFP mRNA, s24= GFP protein, s25= RFP gene, s26= RFP mRNA, s27= RFP protein, s28= activated hERa, s29= DDT

Reactions (Equations):

· Transcription of TetR repressed by activated hERa:

s13 * Ktrxnlac * 1 / 1 + pow(s28 / Kd, 2) - Kdeg * s14

· Translation of TetR:

s14 * KtrnslTet - Kdeg * s15

· Transcription of hERa:

s16 * Ktrxn - Kdeg * s17

· Translation of hERa:

s17 * KtrnslhERa - Kdeg * s18

· Transcription of cI repressed by activated hERa:

s19 * Ktrxnlac * 1 / 1 + pow(s28 / Kd, 2) - Kdeg * s20

· Translation of cI:

s20 * Ktrnsl - Kdeg * s21

· Transcription of GFP repressed by cI:

s22 * Ktrxn * 1 / 1 + pow(s21 / Kd, 2) - Kdeg * s23

· Translation of GFP:

s23 * Ktrnsl - Kdeg * s24

· Transcription of RFP repressed by TetR:

s25 * KtrxnRFP * 1 / 1 + pow(s15 / Kd, 2) - KdegRFPmRNA * s26

· Translation of RFP:

s26 * KtrnslRFP - Kdeg * s27

· Activation of hERa:

1 + pow(Kd * (s18 * s29 / Kd), 2) - Kdeg * s28

Circuit #4:

Idea – This circuit is designed as an IFFL, which should detect DDT within a particular range. In this circuit, estrogen receptor activated by DDT represses expression of TetR, which in turns represses both cI and RFP. RFP is also repressed by cI, thanks to a hybrid promoter.

Goal – We be able to detect a specific range of DDT (i.e., between 1 and 40uM).

Math Model:

Species Key: s1= hERa gene, s2= hERa mRNA, s3= inactivated hERa, s4= TetR gene, s13= activated hERa, s5= TetR mRNA, s6= TetR protein, s7= cI gene, s8= cI mRNA, s9= cI protein, s10= RFP gene, s11= RFP mRNA, s12= RFP protein, s14= DDT

Reactions (Equations):

· Transcription of hERa:

s1 * Ktrxn - Kdeg * s2

· Translation of hERa:

s2 * KtrnslhERa - Kdeg * s3

· Transcription of TetR repressed by activated hERa:

s4 * Ktrxnlac * 1 / 1 + pow(s13 / Kd, 2) - Kdeg * s5

· Translation of TetR:

s5 * KtrnslTet - Kdeg * s6

· Transcription of cI repressed by TetR:

s7 * Ktrxn * 1 / 1 + pow(s6 / Kd, 2) - Kdeg * s8

· Translation of cI:

s8 * Ktrnsl - Kdeg * s9

· Transcription of RFP repressed by TetR and cI:

s10 * KtrxnRFP * 1 / 1 + pow (s6 / Kd, 2) * 1 / 1 + pow(s9 / Kd, 2) - KdegRFPmRNA * s11

· Translation of RFP:

s11 * KtrnslRFP - Kdeg * s12

· Activation of hERa:

1 + pow (Kd * (s3 * s14 / Kd), 2) - Kdeg * s13

Circuit #5:

Idea-This circuit is meant to detect DDT at varying degrees with red fluorescence, depending on the concentration of DDT the microbes are exposed to. In this circuit, the estrogen receptor activated by DDT represses the expression of TetR. Which stops it from repressing the expression of RFP. Red fluorescence would then appear in the presence of high DDT

Goal- We strived to maximize the RFP signal when and only when DDT is present – the presence of Estradiol should inhibit the expression.

Math Model:

Species Key: s1= RFP gene, s6= TetR protein, s2= RFP mRNA, s3= RFP protein, s4= TetR gene, s10= activated hERa, s5= TetR mRNA, s7= hERa gene, s8= hERa mRNA, s9= inactivated hERa, s11= DDT

Reactions (Equations):

· Transcription of RFP repressed by TetR:

s1 * KtrxnRFP * 1 / 1 + pow(s6 / Kd, 2) - KdegRFPmRNA * s2

· Translation of RFP:

s2 * KtrnslRFP - Kdeg * s3

· Transcription of TetR repressed by activated hERa:

s4 * Ktrxnlac * 1 / 1 + pow(s10 / Kd, 2) - Kdeg * s5

· Translation of TetR:

s5 * KtrnslTet - Kdeg * s6

· Transcription of hERa:

s7 * Ktrxn - Kdeg * s8

· Translation of hERa:

s8 * KtrnslhERa - Kdeg * s9

· Activation of hERa:

1 + pow(Kd * (s9 * s11 / Kd), 2) - Kdeg * s10

The Process:

Circuit #1:

Worked on by: Isabelle Conn, Alexa DeKorte, and Paige Lamoreaux

The math modeling process for our team began with the spring term at our college. This term was a learning session for the dry-lab team on how to math model. A lot of revisions and learning was done during this time. The first circuit was modeled during this term. Then edited later to improve its function, as it did not run the first time, we created it. Here are the revisions that occurred:


1. Model Rough Draft:


2. Model Edited:

Edits made to Model. Fixed copy of s38 being on top of another copy of itself. Fixed s18 from being on top of s11, s11 was deleted. Reversed reaction 18 by double clicking on arrow to reaction. Deleted reaction 21 since the computer will generate that equation on its own. Reversed reaction 13 by double clicking on the reaction arrow. Deleted reaction 22 since the computer will self-generate.


3. Model Edited:

Edits made to Model

Simplified re13 by removing Ktrxn * s18 and changed the last s18 to s31; Simplified the hill equation from 1/1 + pow (Kd(s17*s33/Kd), 2) to 1 + pow (Kd(s17*s33/Kd), 2)

Simplified re18 by removing Ktrxn * s18 and changed the last s18 to s29; The rate of transcription was unnecessary for repression activation and the amount of active protein is related to degradation. Simplified the hill equation from 1/1 + pow (Kd(s47*s30/Kd),2) to 1 + pow (Kd(s47*s30/Kd),2)

We also made global transcription, translation, and degradation constants.


4. Model Edited:

Edits made to Model

Almost all hill equations were changed to include a missing * Like 1 + pow (Kd*(s17 * s33/Kd),2) - Kderg * s31

We defined the parameter Kd for re9.

Running During Spring Term:

After much editing with variable representation and pathways, the program cell designer allowed us to run a simulation. The computer program kept shutting down every time the simulation was executed. No simulations of the model were obtained.

Academic Year

The model was partially redone with new techniques to remove any inconsistencies. All the reactions were changed from state transitions to transcription, translation, and state transition reactions.

Circuit #2, #3, #4, #5:

Worked on by: Syrine Ben Driss and Paige Lamoreaux

At the beginning of the academic year, the dry-lab project details and purpose was further brainstormed. We decided that we will be math modeling four other proposed circuits and modifying the spring term group’s math modeling of circuit one. In the first week, dry-lab team members collaborated, and we shared our knowledge and skills about math modeling. This experience helped to refine everyone’s math modeling knowledge, skills and teaching skills. Moving forward, we hoped to continue to review math modeling and plan our method for building the circuits.

For week two, dry-lab members learned and reviewed all the relevant information for the circuits. We also planned out how we were going to build and edit the next five circuits. We planned to create the math and computer model for each circuit first. Then find the parameters and solve any issues with them during and after the first steps. So far, we had only worked on circuit #2. We planned to continue forward with circuit #2 and the other circuits in the next week.

In week three, we worked out the rest of the math for circuits #2, #3, and #4. We accomplished this by dividing the work amongst ourselves. Currently, we hoped to accomplish the math and documentation. We decided to distribute tasks so that we could speed up the basic steps of math modeling. Such as making the math, making the circuit diagram in cell designer, and inputting the math and parameters in cell designer. This was to ensure that we would have enough time to research parameters and test them in our model. Moving forward, we planned to continue preparing the models in weeks four, five, six, and seven.

Running During the Academic Year:

After further editing of the equations and cleaning up the models in general, we got the Cell Designer program to execute a simulation. The simulation showed everything to be consistently the same over many periods of time. This is not what we expected. The amounts of molecules should be changing if a genetic circuit is functioning. There is no apparent reason why the program is showing these results. All aspects of the modeling were refined and verified.

Parts: biobricks or parts necessary for circuits

a. Plasmid Backbone: BBa_K3445000

b. Estrogen response element-controlled inverter: BBa_K3445001

c. RFP: BBa_K3445002

d. BBa_I135221

e. Estrogen receptor and TetR: BBa_K123002

f. Promoter: BBa_K123003

g. RFP coding device: BBa_J04450

The MATLAB Switch:

These models were redone with MATLAB’s symbiology model builder. Each model shows what the circuit is predicted to produce at different given levels of DDT. They also each have specific goals for detection. We originally tried modeling with cell designer, but the models did not function. Multiple refining methods were tried, and it was found that they did run in MATLAB. Therefore, it was a program issue and we switched to MATLAB. These last weeks were focused on transferring our earlier models to MATLAB. The extensive process with cell designer and MATLAB is documented on our wiki. The circuits are also explained. These designs allowed our team to visualize the project during the build and test phases of lab work. They also help our audience understand the project on the molecular level.


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