Team:Imperial College/Growth Curve

Part of our dry lab team concentrated on modelling the growth curve of B. subtilis. This is important to characterise the chassis; particularly, the growth of subtilis is a vital parameter for planning experiments for future projects. Characterisation increases the predictability of the growth of B. subtilis by determining, for example, its growth rate and the duration of its distinctive growth phases. In order to model the growth of B. subtilis, the process was broken down into three main steps, where a separate submodel is produced in MATLAB for each step. Each submodel is an ODE model, which can be simulated using MATLAB. The variables in each submodel can be adjusted according to the boundary conditions (from experimental results).

In the final step, a combination of Submodels 1 and 2 are superimposed with Submodel 3, resulting in a more complex model which enhances the accuracy of illustrating bacterial growth. For more details about the submodels, please see the Dry Lab Appendices page.

The graph on the right shows the log graph used to determine the growth rate.

Lag Phase

During the lag phase, the rate of growth is slow due to two main reasons, B. subtilis is absorbing nutrients in the medium and the replication machinery is being switched on. The higher the concentration of nutrients in the medium, the faster the rate of bacteria growth.

As a result, the volume of the bacteria increases, followed by an increase in the number of bacteria.

Exponential Phase

Both colony number and cell volume increase exponentially during this phase. Our model assumes concentration of the nutrients inside the bacteria is constant.

Stationary Phase

The growth of the colony ceases in number and in volume due to a finite concentration of nutrients, hence it does not have a gradient. Other causes may be death and cell division.

--Mabult 12:05, 27 October 2008 (UTC) Fine! Now that you have given a little overview of the different pahses of the growth curve it is time to introduce your model Create a new section for the Model , give the equations,and explain how they relate to the what you have wirtten before

According to the model, the maximum growth of the bacteria is determined by the concentration of nutrients available initially.

To further enhance the accuracy of the model, the following information will be extracted from experimental data:

• Time span of lag phase, stationary phase and exponential phase
• The growth rate

--Mabult 12:15, 27 October 2008 (UTC) Move model section above result section!!!

The model illustrates the main growth phases the B. subtilis undergoes. These are identified as the lag phase, the exponential phase and the stationary phase. The death phase is a constitutive event and it is possible that it exerts an influence on the three phases discussed below. However, to simplify a complicated model, it is less relevant in this case and therefore is not included in this model.

The M-file used to generate the model below is located in the Appendices section of the Dry Lab hub.

The model for the growth curve was fitted to the experimental results as shown below. The experimental results is depicted by the red curve, while our model is shown by the green curve. The resource curve was also plotted as a function of time and is shown below. Based on our experimental results from the Wet Lab, a log graph was plotted to determine the growth rate. The growth rate was then determined from the gradient of the log graph. This value was included when simulating the growth model using MATLAB.

Experimental Results	Fitted Curve	Resource Curve

The following constants used to generate the model were found to yield the best fit to experimental results.

GROWTH CONSTANT (A): 1.3494

INITIAL NUTRIENT CONCENTRATION (R₀): 2

HILL COEFFICIENT (n): 1.25

INITIAL OD: 0.4

CONSTANT (<math>\alpha/<math>): 0.64516

--Mabult 12:10, 27 October 2008 (UTC) Give conclusion on the fit: Fit is very good in exponential and stationary phases Fit is not as good in lag phase: this suggests that the model should be expanded in order to take into account phenomena such as the movement of the nutrients into the cells