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.

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 growth is represented in terms of volume. By doing so we can, to some extent, overlook the process of cell division during the bacteria growth. We can assume the internal concentration of nutrients is reset almost instantaneously to the external concentration. During this time, the total volume does not change.

The growth of the overall volume V is therefore modelled as:

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 (α): 0.64516

The model 'fit' is very good in exponential and stationary phases. However, this does not apply to the 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.