Team:NYMU-Taipei/Modeling/Overview

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=Overview=
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==System description==
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== Overview ==
 
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This year, our team targets to tackle a challenging problem all over the world - CCD, colony collapse disorder by creating a special kind of ''E. coli''. Our project can mainly be separated into four main parts – prevention, sensing and killing, suicuding, and safety.
 
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In the first part prevention, monosidase is used, which can inhibit Nosema polarfilament development. This part is mainly done by experiment.
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Our model can be generally divided into two sections:
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In the second part sensing and killing, it can further divide into three parts – entrance, sensing, and killing. In entrance part, we use beads (encapsulation) to make it easy for our bacteria getting into the bee. For sensing part, we choose ROS-induced promoters, which can be triggered due to the increase concentration of active transcription factor(OxyR or SoxR). As for killing part, microbial peptides defensin and abaecin are used to pierce Nosema cell wall and then let it be bursted.
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*'''Circuit design'''
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:The first section of our model describes the regulation of Beecoli’s system, which mainly consists of three parts:
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Here we are interested in the relationship between concentration ROS, active transcription factor and ROS-induced promoters’ open strength. Furthermore, we also want to know the lag time between sensing the invasion and the production of the killing protein to see if the device can save the bees from being killed by Nosema. As a result, we use sensor model to attain this goal.
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::1. Sensing: In this part, we describe how a constitutive promoter enhances the expressed level of activator OxyR. Results are presented as a graph showing changes in OxyR concentration as time progress.
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However, the spread of E.coli from bee to bee is also another important factor influencing the efficiency of killing Nosema. Consequently, epidemic model is applied to see the relationship between Nosema infection and E, coli treatment.
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::2. Killing protein production: In this part, we describe the regulation pathway of killing protein production after AhpCp senses Nosema invasion. Results are presented as a graph showing changes in killing protein concentration as time progress after sensing promoter is triggered, which explains whether killing protein can eliminate Nosema ceranae timely.
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In the third part suiciding, ethanol is used to make bees which are fail to survive after E.coli loses to kill Nosema to suicide itself. Because this part should not be easily opened, otherwise, bees will under the threat of being killed all the time even without the presence of Nosema, we add several terminals behind promoter. Here, ethanol model is used to simulate how many terminals do we need as a threshold.
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::3. Ethanol production: In this part, we describe the regulation pathway of ethanol production after AhpCp senses Nosema invasion. Results are presented as a graph showing changes in ethanol concentration as time progress after sensing promoter is triggered, which explains whether ethanol eliminates a sick bee timely when killing protein fails to kill Nosema, and that ethanol won’t kill a bee if it is cured in time.
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The last part is safety issue. Since it may be disastrous to the environment if E.coli escapes from bee’s body, we want E.coli to be killed once it leaves bees’ body. Light sensor is used to achieve this goal.
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== Model highlight 1: PoPS for promoter strength ==
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:In this system, OxyR senses changes in ROS level and regulate other circuits with promoter AhpCp, by enhancing its expression it regulates killing protein production and ethanol production more keenly. Killing protein and ethanol production effects an infected bee alternatively when the bee is in different Nosema invasion stages.
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Mostly, promoter strength is determined by single-round in vitro transcriptions (sequences containing -35motifs, spacer, -10motifs, disc, start, initial transcribed region - bits) like the model of PWMs or in vivo GFP fluorescent assays. However, there are several drawbacks for using such methods to determine promoter strength. For example, promoter strength determined by sequences may be incorrect due to interdependency of motifs [1]; GFP fluorescent assays can only determine the relative promoter strength but the absolute one.
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Concerning the disadvantages above, we choose PoPS mechanism for promoter strength for it is closer to the real situation. PoPS is defined as the level of transcription as the number of RNA polymerase molecules that pass a point on DNA each second, on a per DNA copy basis (PoPS = Polymerase Per Second; PoPSdc = PoPS per DNA copy).
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*'''Effect of Beecoli on the bee colony'''
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:The second section of our model describes the system in which Nosema ceranae and Beecoli infect the subjected colony by an epidemic model. Results are presented as graphs showing population changes as time progress. In addition, there is a graph showing what capsule dosage and infection severity will result in which survival rate of the bee colony eventually for practical purpose.
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[[Image:NYMU_ an example of PWMs.png|center]]
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:This part of the model is based on the circuit’s efficiency discussed on the first section of our model.
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Figure1: an example of PWMs [2]
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[[Image:NYMU_ an example of GFP fluorescent assays.png|center]]
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==Contributions==
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Figure2: an example of GFP fluorescent assays [3]
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What’s more, instead of describing transcription and translation together, we separate the two apart by applying the similar mechanism (Ribosome on RBS per second) to translation. The following pictures show the comparison between PoPS combing transcription and translation together and the separating counterpart. (figure3,4). The results conclude that the separate one is closer to real situations and thus is more practical.
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#Our models for the circuit help the wet lab to choose biobricks for when designing the circuit.
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##To learn how the constitutive promoter regulating OxyR production was chosen, click here.
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##To learn how the regulators CI and LuxR were chosen in AMP production circuit, click here.
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##To learn how the terminator set in ethanol producing circuit was chosen, click here.
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#Our model for the synergistic effect of ''Nosema ceranae'' and Beecoli on an invaded colony provides a guideline for those who want to put our cure into practice.  
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To learn about the tug of war between ''Nosema ceranae'' and Beecoli over the bees, and how many capsules should be fed to a bee colony in different infection stages, [[click here]].
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[[Image:NYMU_ pops defensin.png|center]]
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==Innovations==
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Figure 3: the left shows defensin to time with PoPS combing transcription and translation together; the left shows the separating counterpart. According to experiment, this picture indicates the right one is closer to real situation.
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#We describe transcription and translation process separately and subsidized promoter strength with PoPS, constructing a realistic dynamic system rather than the old one described by possibilities. [[To learn about this mechanism, click here.]]
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#We describe positive and negative regulation by combining the experimental data with hill equation.[[To learn about this mechanism, click here2.]]
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[[Image:NYMU_ pops ethanol.png|center]]
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==Parameters and Reference==
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Figure4: the left shows defensin to time with PoPS combing transcription and translation together; the left shows the separating counterpart. The left shows that ethanol production is less affected by terminator addition, while the right one fits the expected result.
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The promoter strengths used in our circuit model is provided by promoter testing conducted by our wet lab and partly from partregistry.
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Reference:
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Statistics regarding how the capsules carry Beecoli into a bee’s midgut is provided by our wet lab experiment. The population growth rate is provided by previous research on ''Nosema ceranae'' influences on ''Apis Mellifera'' done by the experts.
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1. Virgil A. Rhodius and Vivek K. Mutalik. Predicting strength and function for promoters of the Escherichia coli alternative sigma factor, σE. December 29, 2009
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{{:Team:NYMU-Taipei/Footer}}
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2. Virgil A. Rhodius and Vivek K. Mutalik. Predicting strength and function for promoters of the Escherichia coli alternative sigma factor, σE. December 29, 2009
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3. John Blazeck, Rishi Garg, Ben Reed, Hal S. Alper. Controlling Promoter Strength and Regulation in Saccharomyces cerevisiae Using Synthetic Hybrid Promoters.
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== Model highlight 2:How capsule concentration influences Bee.coli survival rate ==
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We do this model in order to know whether capsules ingested by bees could be effective enough. That is, whether capsules could be digested by bees’ digestive juice, Bee.coli in capsules is able to proliferate, and finally, Bee. coli could reach the effective concentration (Bee.coli/bee) to defend Nosema infection. (Figure 1.)
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The data of capsule digestion rate and Bee.coli survival rate is from experiment, while Bee.coli proliferation rate is assumed according to several papers [1] and confirmed dividing output by input.
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The input is capsule concentration and the function contains the transfer of capsule digestion, survival and proliferation of Bee.coli, which then generates the output of Bee.coli/bee.
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Last but not least, we retrieve the standard concentration of capsule-contained sugar water which will be fed to be colony via the formula below:
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<html>
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<div lang="latex" class="equation">
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[capsule  concentration]={[Bee.coli  per  bee]}\times {proliferation  rate}\frac {1}{{digestion  rate  (of    capsule)}\times {survival  rate  (of  Bee.coli})}
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</div>
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</html>
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[[Image:NYMU_ highlight 2.png|center]]
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Figure1: model of capsule concentration and Bee.coli survival rate|center
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== Our project ==
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We divide our project into two main parts, one is molecular way to describe our circuit, including sensor(oxyR) model , kill protein (defensin) model, suicide (ethanol) model; the other is bee-bee interaction for the whole colony.
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1. molecular way:
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－sensor(oxyR) model
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－kill protein (defensin) model
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－suicide (ethanol) model
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2. bee-bee interaction for the whole colony:
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－epidemic model
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[[Image:NYMU_ step3.png|center]]
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1. molecular way:
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[[Image:NYMU_ molecular way.png|center]]
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(1) sensor(oxyR) model
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When Nosema enters the bees, it will trigger bees’ ROS (reactive oxygen species) production, which in turn launches Bee.coli’s OxyR production. After that, ROS and OxyR will form a complex and bind to the sensor. As a result, we choose a ROS/oxyR complex-induced promoter as our sensor to activate the whole circuit. (figure1,2)
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[[Image:NYMU_ ROSoxyR.png|center]]
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figure1: This picture shows that how ROS is produced by the infected bees and trigger the production of oxyR in Bee.coli
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[[Image:NYMU_ ROSoxyR complex.png|center]]
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figure2: This picture shows that ROS and OxyR will form a complex and bind to the sensor
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The aim of this model is to see whether oxyR concentration can reach its effective level to activate the sensor in time.
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However, since naturally produced oxyR in Bee.coli (our E.coli) is not sufficient, we add a constitutive promoter before the oxyR-producing gene to increase oxyR concentration to reach the effective level. (figure3,4)
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[[Image:NYMU_ oxyR concentration to time after adding constitutive promoter.png|center]]
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Figure4: oxyR concentration to time after adding constitutive promoter
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(2) kill protein (defensin) model
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The aim of this model is to know whether kill protein can reach its effective concentration to kill Nosema in time.
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Originally, we use CI promoter, which will be inhibited by CI protein, to repress kill protein production when there’s no Nosema invasion. However the result shows that kill protein's full expression is too low to kill Nosema even though CI is absent. (figure5)
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[[Image:NYMU_ pci+kill protein.png|center]]
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Figure5: Defensin concentration to time under promoter CI’s control (control group)
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Therefore, we replace CI promoter with pLux/CI hybrid promoter. Besides, we also add LuxR, LuxI genes behind to build a positive-feedback mechanism for kill protein to reach its effective level and kill Nosema in time.(figure6)
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[[Image:NYMU_ hybrid promoter+kill protein.png|center]]
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Figure6: Defensin concentration to time under pLux/CI hybrid promoter’s control and LuxR, LuxI’s positive feedback
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(3) suicide (ethanol) model
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We want the suicide part (ethanol) to be opened only after the first way (kill protein) fails; that is, Nosema infection is too severe to be killed by kill protein (defensin).
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As a result, we add several terminators behind ROS/oxyR complex-induced promoter to build a threshold for RNA polymerase to overcome. In other words, it takes RNA polymerase much more time to go through the terminator barrier and thus, create a time lag for ethanol production.
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Nevertheless, if we put the ethanol synthesizing enzymes PDC and ADH right after these terminators, it will take much time to accumulate the enzyme’s effective concentration for ethanol production because a lot of effort is wasted to overcome terminator's threshold over and over again. Therefore, instead of having ROS/oxyR complex regulate ethanol production directly, we constructed a circuit that has a “key”, another activator (the key we choose is T7 polymerase)to amplify the production of PDC and ADH so that ethanol concentration can reach its effective level to kill the infected, incurable bees and save the whole colony.(figure 7.8)
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[[Image:NYMU_ circuit of ethanol design.png|center]]
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Figure7: this picture shows the circuit of ethanol design, which insert terminators to create a time lag and T7 polymerase genes as a positive feedback.
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[[Image:NYMU_ the result of ethanol design.png|center]]
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Figure8: the result of ethanol design. It shows that ethanol production can reach its effective concentration after 80 hours of Nosema infection.
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The result shows that after adding several terminators and T7polymerase mechanism, we successfully build a time delay as well as a boom of PDC and ADH enzymes to attain our goal.(figure9)
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[[Image:NYMU_ the cartoon picture of ethanol mechanism design.png|center]]
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Figure9: the cartoon picture of ethanol mechanism
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Latest revision as of 03:07, 29 October 2013

National Yang Ming University


Contents

Overview

System description

Our model can be generally divided into two sections:

  • Circuit design
The first section of our model describes the regulation of Beecoli’s system, which mainly consists of three parts:
1. Sensing: In this part, we describe how a constitutive promoter enhances the expressed level of activator OxyR. Results are presented as a graph showing changes in OxyR concentration as time progress.
2. Killing protein production: In this part, we describe the regulation pathway of killing protein production after AhpCp senses Nosema invasion. Results are presented as a graph showing changes in killing protein concentration as time progress after sensing promoter is triggered, which explains whether killing protein can eliminate Nosema ceranae timely.
3. Ethanol production: In this part, we describe the regulation pathway of ethanol production after AhpCp senses Nosema invasion. Results are presented as a graph showing changes in ethanol concentration as time progress after sensing promoter is triggered, which explains whether ethanol eliminates a sick bee timely when killing protein fails to kill Nosema, and that ethanol won’t kill a bee if it is cured in time.
In this system, OxyR senses changes in ROS level and regulate other circuits with promoter AhpCp, by enhancing its expression it regulates killing protein production and ethanol production more keenly. Killing protein and ethanol production effects an infected bee alternatively when the bee is in different Nosema invasion stages.
  • Effect of Beecoli on the bee colony
The second section of our model describes the system in which Nosema ceranae and Beecoli infect the subjected colony by an epidemic model. Results are presented as graphs showing population changes as time progress. In addition, there is a graph showing what capsule dosage and infection severity will result in which survival rate of the bee colony eventually for practical purpose.
This part of the model is based on the circuit’s efficiency discussed on the first section of our model.

Contributions

  1. Our models for the circuit help the wet lab to choose biobricks for when designing the circuit.
    1. To learn how the constitutive promoter regulating OxyR production was chosen, click here.
    2. To learn how the regulators CI and LuxR were chosen in AMP production circuit, click here.
    3. To learn how the terminator set in ethanol producing circuit was chosen, click here.
  2. Our model for the synergistic effect of Nosema ceranae and Beecoli on an invaded colony provides a guideline for those who want to put our cure into practice.

To learn about the tug of war between Nosema ceranae and Beecoli over the bees, and how many capsules should be fed to a bee colony in different infection stages, click here.

Innovations

  1. We describe transcription and translation process separately and subsidized promoter strength with PoPS, constructing a realistic dynamic system rather than the old one described by possibilities. To learn about this mechanism, click here.
  2. We describe positive and negative regulation by combining the experimental data with hill equation.To learn about this mechanism, click here2.

Parameters and Reference

The promoter strengths used in our circuit model is provided by promoter testing conducted by our wet lab and partly from partregistry.

Statistics regarding how the capsules carry Beecoli into a bee’s midgut is provided by our wet lab experiment. The population growth rate is provided by previous research on Nosema ceranae influences on Apis Mellifera done by the experts.

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