Team:Calgary/Modelling/Blog

Every Monday (started at the end of June), our modelling team uses our Synthetic BLOGology blog to update both the public and the rest of the team of their progress in the modelling projects. For more details regarding Synthetic BLOGology, please click HERE.

-July
-August

These updates have been posted with newest ones at the top.

Hi everyone, its Carol again!

This week for modeling, we focused mainly on writing up several ways to characterize the AI-2 signaling system. The characterization methods that we decided to explore are the following:

1. Static Performance
2. Dynamic Response
3. Response Time
4. Robustness

The robustness part of characterizing the system would be the most interesting. The degree of robustness depends on how sensitive the system is to fluctuations and changes. If this part of the signaling system can be explored in more detail, it will be an advantageous component to our project. We spent the past week summarizing this in paper format. I am currently working on a promoter library, which will play a huge role in exploring the expression of luxPQ. Once the circuits are done, some of these experiments can be done in a few days. We will hopefully by the end this summer have some characterization done. Stay tuned! Signing off now, peace!

Hey there! So I’m a little late with this entry…hopefully none of you are too crushed by this. As a refresher, I’m Vicki, and we’ve already met repeatedly over lab editions. With my fabulous team of brilliance – Carol, Chinee, Kevin, Afshin and Iman – we’re going to give the term “America’s next top model” a whole new meaning.

It has been a very busy week of searching for reaction constants for our differential and membrane-computing efforts. Indeed, the literature is vast, but it sure isn’t easy to navigate. And it doesn’t help when published authors don’t really seem to make effective communication a priority. Regardless, we’re progressing, slowly but surely.

We have also been discussing how we’re going to approach the circuit characterisation matter. After a long and fruitful chat on what others have done previously to characterise their signalling circuits, we came up with a list of areas for which quantitative characterisation would be useful AND realistic to achieve. Because as much as we want to pay for sequencing every day to assess circuit stability, it doesn’t seem like the most effective use of our funds, especially with so other areas (read: ice cream parties) where the money would be better spent! We also outlined general procedure approaches that would enable us to collect the data to come up with and validate our models. Stay tuned!

Hi! My name is Carol and I’m heading into my third year of biomedical and chemical engineering in the fall, and I have completed a degree in biomedical sciences. I love watching sports (especially football and hockey) and I enjoy cooking and reading. I’m part of the laboratory and modelling team, so you’ll be hearing from me lots this summer!

In the past few weeks, the modelling team focused on familiarizing with Matlab and Simbiology. Before I move on, I should introduce you to my team mates that make this team possible! Vicki is a recent graduate from Engineering from the University of Toronto and she has worked with Matlab and its applications extensively throughout her studies. Chinee is also a third year chemical engineering and has some experience with Matlab as well. Finally, Kevin who is going into his second year of Kinesiology is just helping out because he is just a nice individual! With all the skills from each team member, we believe that we can produce a great model for our project. Back to modelling, one of our facilitators (Dr. Nygren) gave us an assignment that helped us understand the program more. We had to model the three gene repressilator in Matlab via two different methods, stochastic and deterministic. I’m going to spend some time now to explain the two methods that we will be using to model our quorum sensing model.

Differential (Deterministic) Model
This model uses equations that involve derivatives to illustrate concentrations of different molecules within a network. In our case, we will be using equations to describe the concentrations of different molecules within the Autoinducer-II (AI-2) cascade. With the ability to solve the differential equations, we can investigate how the concentration of different molecules within the cascade modifies compared with initial conditions. However, this method of modelling is often used with systems with high concentrations of chemicals and we expect that the importance of rare events is low. Furthermore, this type of modelling is often used in smaller networks.

Stochastic Models
The other type of model that can be used to describe our model is through probabilistic equations that can describe the probability that a certain chemical reaction will occur between certain types of molecules at any instant. Furthermore, these equations can also be used to calculate the quantities of all species at the end of a small time step. Therefore, it is plausible to evaluate how the molecules within the cascade change over time by repeating this process over many steps. Since random variable input is involved with this type of modelling, each simulation run can produce varying results. By using averages through numerous simulation runs, a trend can be predicted. This type of modelling is used for small numbers of molecules because they take more computing power. This is because of the random nature of molecular interaction and it accounts for the probability of rare events occurring.

Our goal for this week is to successfully build the AI-2 system in Simbiology. We’ve been busy the last few weeks reading up on literature and we were able to successfully find some phosphorylation rates that can be incorporated into our model. Next week, we will show you what we’ve built in Simbiology and give you a tour of Simbiology and Matlab! Stay Tuned!