Calculating total DCM degraded

1) Obtaining theoretical growth curve

To start this calculation, we needed to know how many bacteria we could reasonably expect to have in our system. To do this, we used reasonable bead dimensions and reasonable bead numbers to calculate the volume of bacteria infused agarose that we were likely to have in the system. We then used the assumption that the bacteria would grow to an optimum density of 10^7 bacteria per ml of agarose (REFERENCE) and combined these to give us an approximation of how to scale the growth curve:

A = Gompertz vertical scaling constant

N = number of beads

V = volume of each bead in ml

ρ = number of bacteria per ml

Our theoretical growth curves were based on Gompertz functions for the reasons explained when you follow this link: (what are Gompertz functions?). An example output growth curve of the model is shown here.

The scaling of the growth rate of the Gompertz function comes directly from growth curves of the DM4 bacteria that we obtained in the lab. See more about our work with growth curves here.

2) Calculating the volume of DCM that these bacteria could degrade

Our next task was to model the rate of DCM degradation by the average bacteria. Using Michaelis-Menten kinetics[1], this was predicted to be:

d[Ndcm]/dt = rate of DCM molecule degradation (s-1)

Kcat = dcmA turnover rate (= 0.6 s-1 for DM4)

[DCM] = DCM concentration (= 0.02M for our system)

[DcmA] = Number of DcmA molecules per cell (87576) Where did this number come from?

Km = Michaelis constant ( = 9 x 10^-6 M for this reaction)

Through the use of diffusion-limiting beads, [DCM] is kept constant at 0.02M. This is significantly larger than our Michaelis constant so this equation can be simplified by using the following assumptions:

Multiplying this by our population function, the total rate of DCM molecule degradation is given as:

Turning this into a more recognisable value (a volume) gives the total rate of DCM degradation as:

Where:

When all of these calculations were modelled in Matlab with the input conditions shown above, the total volume of DCM that we would predict the native bacteria DM4 to degrade in 24 hours is shown by the red line below. This is before you account for the possible toxicity of the pH drop. This is taken into account in the sections below.

Reference:

  1. Michaelis L. and Menten M.L. Kinetik der Invertinwirkung Biochem. Z. 1913; 49:333–369 English translation Accessed 6 April 2007

Calculating the pH change

The degradation of DCM by DcmA produces hydrochloric acid (HCl) according to the reaction below: 

There is a resulting pH change as a result of this. Because we are dealing with an organic system which cannot tolerate excessive acidity (pH<6), we must track the anticipated HCl production and resulting pH change. The following relationships were used:

NHCL = molecules of HCl

NDCM = molecules of DCM

V1(t) = aqueous layer volume (ml)

V2(t) = DCM layer volume (ml)

y(t) = bacteria population

MRDCM = DCM molar mass

NA = Avogadro’s constant (mol-1)

ρDCM = DCM density (kg/dm3)

[HCl] = HCl concentration (M)

These equations were then simulated using a series of linked functions on MatLab and the results are displayed below:

Summary:

As you can see from the above graph, the native bacteria (Methylobacterium extorquens DM4) will not be able to degrade a large volume of DCM. This bacteria will not be suitable for our purposes as the solution to chlorinated waste disposal. There are several reasons for this, these include:

The degradation of DCM is a stress response for the DM4 bacteria.

The DM4 bacteria grow very slowly (time to grow up a culture is approximately 2 weeks) and are not very robust to external conditions at all.

The DM4 bacteria are very vulnerable to the toxic intermediates of this reaction.

DM4 are not very well known characterised or known about.

However, using synthetic biology, we can dramatically increase the amount of chlorinated solvents that certain bacteria can degrade. This is because:

We will remove the stress response dependence for the degradation of chlorinated solvents. Also, due to this being a reaction driven by the removal of product, we will be able to significantly increase the rate of degradation.

The bacteria that we’re using are E-coli and pseudomonas strains. These bacteria are very fast growing (relatively) and are much more robust to changes in the external conditions.

These strains are less vulnerable to our toxic intermediates.

This model proves the power of computer modelling and shows the importance of using synthetic biology to solve global problems.

Gompertz Functions

We used a variation of a sigmoid function called a Gompertz function to model the theoretical growth of our bead-encapsulated bacterial population. These functions are well established[1] as a method of predicting population growth in a confined space, as birth rates first increase and then slow as resource limits are reached. As this is how our bacteria will be growing (when confined in the beads), we took this information and assumed that the bacteria’s population over time will follow one of these functions (when scaled correctly).

Gompertz functions are of the form:

y(t) = population size as a function of time

A = maximum sustainable population

B = shift on time axis

C = growth rate

Using this theoretical form, we could then calibrate the values of our variables through comparison with actual growth curve data from wet lab experiments. This was an important step because it would then allow us to calculate the total theoretical degradation rate of DCM that our kit could support.

Varying each of the three constants allows us to fit our Gompertz function to the actual growth data. The effect of varying each constant is shown below:

Reference:

Zwietering, M. H.; Jongenburger, I.; Rombout, F. M.; van 't Riet, K. (1990), "Modeling of the Bacterial Growth Curve", Applied and Environmental Microbiology 56 (6): 1875–1881

Calculating the pH change

Calculating the pH change