# Team:Amsterdam/Project/Phy param/Synechocysytis

## *Synechocystis* physiological parameters

###### Counting cells, counting molecules

## Overview

### Background

Estimating physiological parameters is necessary for building meaningful models that can help us to rationally design our consortium. In addition, production stability can be assessed by estimating these parameters over a long cultivation periods.

### Aim

We aim to use different cultivation strategies to characterize growth and production rates of carbon producing *Synechocystis* strains, while assessing their stability.

### Approach

We have used a turbidostat cultivation system to determine the stability of the physiological parameters

### Results

- We have demonstrated the instability of classical engineering strategies. As shown in figure 1 the strain SAA023 loses the ability to produce lactate after 300 hours of continuous cultivation.
- We show that the Q
_{p}in the growth coupled producer Δacs remains constant over that time (figure 2). - We have determined Q
_{p}and growth rates for six*Synechocystis*constructs (Table 1).

### Connections

- Stable Romance: Measure stability.
- Engineering Romance: Estimate parameters of new strains.
- Simulating Romance: Provide parameters.

## How to measure stability?

One faces two main problems when trying to study the stability of the aforementioned parameters in *Synechocystis*. One is how to maintain a culture originated from the same population growing for many generations giving the opportunity to the cells to accumulate mutations and evolve. One option is to grow the cells on a flask until they reach the lag phase then reinoculate a fraction of this culture to a new fresh medium. Although this method allows the population to evolve it can take a long time to observe mutations because during the lag and the stationary phase of bacterial growth cells do not divide. This may seem trivial but due to the slow growth rate of *Synechocystis* it can make a difference.

Turbidostats are culture devices that allow cells to be maintained in constant exponential growth. This is achieved by automatically diluting the culture, i.e. pumping in fresh medium and pumping out culture medium, when a certain threshold is reached. Its name refers to the fact that the turbidity of the medium, so the amount of cells, is maintained (-stat from static) within a narrow range. This requires a three compartment system where the first contains the cultures, the second holds a reservoir of fresh medium and the third collects the medium extracted from the cultures. Pumps move the medium between containers. The cell density is recorded at regular intervals by a spectrophotometer and based on this value an algorithm decides when the culture is diluted.

This system allow us to reduce the time of observing a mutation but now it comes the second problem. How do we know if a mutation leading to a change in the parameters has happened? The first option that comes to our synthetic biologist mind is by sequencing periodically the genome of the population and screen it for mutations. The major drawback of sequencing is that it does not inform whether the mutation affects the parameters or not. The remaining solution is then to estimate the physiological parameters with high time resolution to spot when the mutation has occurred. Using the turbidostat it would be possible to store all the values recorded by the spectrophotometer and calculate the growth rate from these data. This is done by fitting a linear model to the logarithm of the OD over time. The production rate can be obtained by periodically measuring the amount of product in the medium.

Luckily for us when we came to the lab there was a master student, Joeri Jongbloets, who had transformed a multicultivator (MC) like the one on figure 4 in a 8 channel turbidostat. For his internship he wrote a program that connects with the MC hardware and controls the pumps to dilute the culture when necessary. In addition the software stores the measurements from the MC spectrophotometers in a database and analyzes it to obtain the growth rate. Thanks to this impressive piece of software engineering we were able to run long term experiments and observe evolution.

## Methods summary

#### Cultivation conditions

The strains producing glycerol, lactate and acetate (Δacs) were grown in turbidostats for 900 hours under constant LED lights providing 60 μE/ m2* s * OD.

The Pta1, Pta2, Δacs and Ack strains were cultured on photostats. This system, developed by Wei Du and also implemented by Joeri Jongbloets in the MC, is basically a batch culture where the light intensity per OD provided to the cells is maintained constant. In this case the light intensity per OD was set to 60 μE/ m2* s.

In both cases the culture medium was BG-11 supplemented with NO3- and TES buffer.

#### Measuring product

Samples were taken from the cultures and centrifuged (5 min, 14.500 rpm) to obtain the supernatant. Product concentration was then determined by enzymatic assays in the case of lactate and acetate and by HPLC for glycerol.

#### Estimating parameters

##### Growth rate

Growth rates were estimated by fitting a linear regression model to the logarithm of the OD over time during the exponential phase of growth. The slope of this model is considered as the growth rate since it shows how much the OD changes per unit of time. Its unit is 1 / h.

##### Production rate

Q_{p} are obtained by dividing the concentration of measured product by the biomass of the culture estimated from OD (1 OD unit = 0.2 gDW). This value is in fact the production yield, that is, how much product the cells excrete per amount of biomass. To get the production rate, Q_{p}, we multiply this value by the estimated growth rate. Its units are amount of product (in mmol) / gDW * h.

## Results

#### Stability

In our turbidostat experiments we observed the results we expected. In the lactate strain (figure 1), a non growth coupled carbon producer, we started to observe an increase in growth rate about 300 hours after the beginning of the experiment. At the same time point, we recorded a drop in the production of lactate. This result confirms our hypothesis about how mutations leading to the loss of production can be quickly fixed inasmuch as it releases the burden imposed by the carbon production increasing the fitness of the mutated cells.

On the other hand, the growth coupled acetate producer, Δacs, shows a constant production and growth rate over the length of the experiment (figure 2) demonstrating that this strategy is more stable than the classical engineering approach. The Q_{p} estimated around 400 hours shows a decrease in the production but it is most likely due to experimental failure in the acetate determination assay. In the turbidostat, cells were grown at very low OD (threshold was set to 0.35) therefore the concentration of acetate was below the detection limit of the enzymatic assay method. To overcome this problem we dehydrate the samples by lyophilization, also called freeze-drying, then dilute them in a smaller volume, therefore increasing the concentration above the detection limit. Although this method worked (we obtained similar Q_{p} values using different culture systems), the sample processing could have introduce noise in this measurement.

#### Parameters

In table 2 the physiological parameters obtained for six strains producing glycerol, lactate and acetate are presented. The lactate strain shows the highest Q_{p} followed by Δacs and Syn1413. An intriguing result is the differences in Q_{p} and growth rate of Δacs between cultivation methods. In the turbidostat the length of the exponential phase from where the growth rate is calculated is smaller than in photostat which could be influencing the estimation. For the Q_{p}, the previously discussed argument could also be an explanation for this difference. In addition it is possible that the photostat conditions are more favourable for the growth of *Synechocystis*.