Imperial College/Courses/Spring2008/Synthetic Biology/Computer Modelling Practicals/Practical 2

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Synthetic Biology (Spring2008): Computer Modelling Practicals

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Practical 2


Objectives:

  • To explore computationally some simple genetic motifs:
    • Constitutive gene expression.
    • Activated and Repressed gene expression.
    • Positive and Negative feedback gene expression.

Deliverables

  • A report is expected.
  • When you find in the text (illustration needed), it means that you will have to provide an image export of your simulation results in your report.


Part I: Constitutive Gene Expression


In this section, we explore a simple model to represent a gene expressed constitutively. The model is based on an interpretation of the central dogma: Gene -(transcription)-> mRNA -(translation)-> Protein, with both the mRNA and the Protein being also naturally degraded. The parameters used throughout this practical will be characteristic of the E.Coli bacteria.


Model CellDesigner Instructions
[math]\displaystyle{ Gene \rightarrow mRNA \rightarrow Protein }[/math]

Define the topology of the reaction network:

  • Download this File, and Open it with CellDesigner.
  • This file contains the network topology to describe a simple constitutive gene expression model. No Kinetic information is yet described.

Constitutive Gene Expression

Following the law of mass action, we can write:

[math]\displaystyle{ \begin{alignat}{1} \frac{d[mRNA]}{dt} & = k_{1} - d_{1}[mRNA] \\ \frac{d[Protein]}{dt} & = k_{2}[mRNA] - d_{2}[Protein] \\ \end{alignat} }[/math]
  • k_1 is the transcription rate. It is considered constant, and depends on ...
  • d_1 is the mRNA degradation rate of the mRNA. The typical half-life of mRNAs in E.Coli has been measured to be between 2min and 8min (average 5min).
  • k_2 is the translation rate. It is considered constant, and depends on ...
  • d_2 is the protein degradation rate. Here, we will only consider very stable proteins, not engaged in any active degradation pathways. In that case, we can approximate the degradation of the protein to be only due to the dilution effect happening during cell division. Cell division will be 30min.

Questions:

  • From the ODE system given, write down the steady-state expression of [mRNA] and [Protein], with regards to [math]\displaystyle{ K_1, K_2, d_1, d_2 }[/math].
  • Knowing that average mRNA per gene is 2.5 in E.Coli, what is the average transcription rate ?
  • Knowing that the average number of proteins per gene is 1000 in E.Coli, what is the average translation rate ?
  • Define all the necessary kinetics laws for the model, and create all the appropriate parameters.
  • Run a simulation, and comment on the simulation outputs.
  • From a Synthetic Biology point of view, this motif can be seen as a 'Protein Generator'. One might be interested in controlling the Protein output of this device. Using the 'parameter scan' function, proceed to a simple sensitivity analysis on each of the 4 parameters, within a 10% range. Describe how each parameter impacts the Protein steady state.


Next, we want to explore the quasi-steady-state assumption on the mRNA expression. From the previous simulations, you might have noticed that the concentration of mRNA is reaching steady-state very quickly, compared to the Protein concentration. In that case, we can approximate the previous model by considering that [math]\displaystyle{ \frac{d[mRNA]}{dt}=0 }[/math].

Model CellDesigner Instructions
[math]\displaystyle{ Gene \rightarrow Protein }[/math]
  • Within the same CellDesigner file, build a new network.
  • Create a network that would allow you to directly synthesis a Protein from a Gene.

Quasi-Steady-State Constitutive Gene Expression

Following the law of mass action, we can write:

[math]\displaystyle{ \begin{alignat}{2} \frac{d[Protein]}{dt} = s - d[Protein] \\ \end{alignat} }[/math]
  • Create the appropriate parameters and reaction kinetics laws.


Questions:

  • Taking into account the quasi-steady-state assumption on the [mRNA], work-out the value of 's', and 'd' with regards to [math]\displaystyle{ K_1, K_2, d_1, d_2 }[/math].
  • Simulate the full model, along with the quasi-steady state approximation. Comment on how good this approximation seems to be.


Additional Resources:


Part II: Activated and Repressed Gene Expression


Very few genes are known to have a purely constitutive expression, most of genes have their expression controlled by some outside signals. In this section, we will particularly focus on the study of DNA-binding proteins, called transcription factors. These proteins, when binding to a promoter region, can either have an activation effect on the gene (positive control), or a repression effect (negative control). In this part of the tutorial, we investigate one of the most common model used to describe this type of interactions.

Let's first consider the case of a transcription factor acting as a repressor. A repressor will bind to the DNA so that it prevents the initiation of transcription. Typically, we expect the transcription rate to decrease as the concentration of repressor increases. A very useful family of functions to describe this effect is the Hill function: [math]\displaystyle{ ... }[/math] This function has 3 parameters: Vmax, n, Km


Model CellDesigner Instructions
[math]\displaystyle{ Gene \rightarrow mRNA \rightarrow Protein }[/math]

Define the topology of the reaction network:

  • Download and open this file

Hill function for repressors: [math]\displaystyle{ \frac{Vmax.{K_m}^n}{{K_m}^n+R^n} }[/math]

Hill Function (Repressor)

Following the law of mass action, we can write:

[math]\displaystyle{ \begin{alignat}{1} \frac{d[mRNA]}{dt} & = \frac{k_{1}.{K_m}^n}{{K_m}^n+R^n} - d_{1}[mRNA] \\ \frac{d[Protein]}{dt} & = k_{2}[mRNA] - d_{2}[Protein] \\ \end{alignat} }[/math]

Repressed Gene Expression


Then, let's consider the case of a transcription factor acting as an activator. A activator will bind to the DNA so that it promotes the initiation of transcription. Typically, we expect the transcription rate to increase as the concentration of repressor increases. Once again, q very useful family of functions to describe this effect is the Hill function. It is slightly different from the previous one: [math]\displaystyle{ ... }[/math] This function has 3 parameters: Vmax, n, Km

Model CellDesigner Instructions
[math]\displaystyle{ Gene \rightarrow mRNA \rightarrow Protein }[/math]

Define the topology of the reaction network:

  • Download and open this file

Hill function for activators: [math]\displaystyle{ \frac{Vmax.A^n}{{K_m}^n+A^n} }[/math]

Hill Function (Activator)

Following the law of mass action, we can write:

[math]\displaystyle{ \begin{alignat}{1} \frac{d[mRNA]}{dt} & = \frac{k_{1}.A^n}{{K_m}^n+A^n} - d_{1}[mRNA] \\ \frac{d[Protein]}{dt} & = k_{2}[mRNA] - d_{2}[Protein] \\ \end{alignat} }[/math]

Activated Gene Expression

Questions:

  • Draw the transfer function between the Activator concentration and the steady-state protein concentration. And illustrate the effect of the parameters 'Km' and 'n'.
  • Imagine an application where this behaviour might be useful.
  • Draw the transfer function between the Repressor concentration and the steady-state protein concentration. And illustrate the effect of the parameters 'Km' and 'n'.
  • Imagine an application where this behaviour might be useful.


Additional Resources:


Part III: Positive and Negative Feedbacks

In this section, ....


Model CellDesigner Instructions
[math]\displaystyle{ Gene \rightarrow mRNA \rightarrow Protein }[/math]
  • Define the topology of the reaction network:

Following the law of mass action, we can write:

  • ...

Questions:

  • ...
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