Team:KU Leuven/Project/Oscillator/Modelling

In this part of the wiki we will describe how we performed an analysis of our proposed oscillating system and the results. This text starts with a small introduction on what we want to achieve with this oscillator, a topic that is more thoroughly elaborated on the design page. Before we started the analysis that is stated here, we looked up how similar networks have been analyzed before in order to see what direction we will take. A full-scale analysis would go beyond the scope of the project so we will stick to an elaborate indicative study. The first step is to translate our network into ODE’s (ordinary differential equations), which we will make more realistic step by step. We will use these to see how easily sustained oscillations form. This is of course not the most impressive feature since there are many known networks that easily produce oscillations. We chose not to include the effect on amplitude and frequency, since that would make the scope of this study explode and the many assumptions we have to make, render it unrealistic. The important feature of our network is its synchronization features. In order to check whether our model achieves rapid resynchronization, we will solve systems of PDE’s (partial differential equations). Since those are a lot more computationally intense, we used the Flemish Super Computer Centre (VSC) in order to do our computations time-efficiently. The explanation of how the network functions and how it attains synchronized oscillations can be found on the page explaining the design. For the transformation into a biological network, we refer you to the wetlab page.

Firstly, it is important to clarify what we exactly mean with ‘parameter’. A biochemical system has a very high variability in ranges for transcription rates, translation rates, degradation rates, etc. On top of that these parameters are not perfectly quantified and are subject to changes in the conditions. We checked what part of the parameter space (Box 1) creates oscillations. There are possibilities for doing this in a purely mathematical manner. Tyson (2002) gives a good example of how to study systems that produce biochemical oscillations. Polynikis, Hogan and Bernardo (2009) described modelling approaches for gene regulatory networks more generally. This is typically done by investigating the eigenvalues of the Jacobian matrix of the system. This becomes increasingly more difficult when the number of parameters and variables increases. A high level of non-linearity complicates the study of the behavior even further. In order to see what is possible we contacted Professor Dirk Roose, an expert in non-linear systems analysis. We explained him we want to investigate what parameter values create a synchronized oscillation. However, our parameter space consists of about 20 parameters that can each vary with more than a factor ten. On top of that we will have highly non-linear equations. Professor Roose told us this amount of variability would make a clean-cut mathematical examination of our model impossible . Since it is not possible to reduce our parameter space, without diverting from our goal of fully studying our system, we decided to use another strategy. We will study this enormous parameter space by generating random sets of parameters throughout this space. This offers a less theoretical, nonetheless effective means of assuring this model robustly produces oscillations.

We will explain this concept with a simple example. Let’s say we want an iGEM team (our system) to develop an oscillator. Two parameters of our system will determine whether this endeavour is successful. These parameters are the number of engineers in a team and the amount of coffee they consume per engineer. As to determine the parameter space we have to find relevant ranges for each of the parameters. We assume more than 10 engineers in the iGEM team is unlikely and a coffee consumption of more than 1.2 liter per day per engineer can also be discarded. The resulting parameter space for this two-parameter model can be easily visualised by a rectangle. This resulting parameter space can be probed and the parameter values we obtained were then used in a simulation which gave as an output whether an oscillator is produced or not. In the figure below we show the result of such a random probing of the parameter space. The blue dots represent unsuccessful endeavours and the orange dots successful ones. The results allow us to hypothesise on what is necessary to produce an oscillator. Apparently a higher amount of engineers still makes success possible, but only when the coffee consumption is not too high. We hypothesise here that this might be because the engineers get too much energy and start to fight amongst themselves. We used a similar approach in the testing of our model, with some distinctions though. We deme parameter sets positive when synchronized oscillations take place. But we have 26 parameters and that makes it impossible to visualise our positive parameter space. So we chose another way to look at the parameter space and look at the total fraction of the parameter space that exhibits the wanted behaviour.

As mentioned on the design page the quorum-sensing molecules are produced by an enzymatic step. Equation 2 shows the equations that result in that behavior. The transcription factors control the production of the enzymes rather than A and B directly. The production of A and B is modelled by using Michaelis-Menten kinetics (S_A/(K_A+S_A)). To not overly complicate our model we will assume that the substrates S_{A and SB are at saturation levels. This means they are a lot higher than K_A, K_B respectively and the Michaelis-Menten consequentely returns 1. This resulting system of equations is shown in Equation 3.}