*Measure what is measurable, and make measurable what is not so.*

**-- Galileo Galilei**

Highly characterized parts are an absolute prerequisite for synthetic biology to enable rational design of DNA constructs. While thousands of parts were analyzed for

*E. coli*, no such data is available for

*V. natriegens*making design of plasmids a guessing game.

To tackle this foundational challenge for our chassis, we established a measurement and data analyzation workflow tailored to its unmatched growth rate. Initially, we carefully examined the plasmidal context yielding the highest dynamic range in reporter experiments and concluded superior performance when utilizing the

*lux*operon and ColE1. Subsequently, we applied this protocol to obtain reproducible data for promoter strengths - including inducible promoters, - insulation by our connectors and expression influence of different oris. qPCR experiments provided additional insights into plasmid copy number dependent on reporter expression. We are certain that our data, in combination with our Marburg Collection, will foster the widespread utilization of

*V. natriegens*in synthetic biology.

This page focuses on developing an experimental and data analysis workflow for all experiments that were performed with a platereader. The characterization of promoters, RBS, terminators and our connectors, can be found in the respective section on the result page.

In addition to platereader experiments, we also used methods (e.g. qPCR) to additionally support our measured data. (Link to Results).

Most of our data were obtained by measuring the expression of reporters in platereader experiments. In the first attempts, we failed to obtain reproducible data by applying workflows that are commonly used for* E. coli*. We realized that a new chassis requires overthinking existing procedures and we decided to establish a workflow for platereader experiments tailored to *V. natriegens* which respects species specific properties, primarily its unbeaten doubling time.

*E. coli*start with growing overnight cultures of certain samples, measuring the OD

_{600}and dilute all samples in a 96 well plate to a defined OD

_{600}(e.g. 0.05). Depending on the number of samples, pipetting the test plate can easily take up to 45 minutes.

When first following this approach with

*V. natriegens*, we realized that a different workflow is needed for the worlds fastest growing organism. We tested how much

*V. natriegens*grows during the preparation of a full 96 well plate. We set up an experiment using a stationary culture of

*V. natriegens*with a plasmid weakly expressing

*lux*- to achieve realistic conditions - and diluted this culture 1 : 100 in a 96 well plate with one pipetting step taking 30 seconds. The result of this experiment can be seen in figure 1 A.

The data show an obvious trend towards higher OD

_{600}for wells that were pipetted first. Seemingly,

*V. natriegens*is able to recover from stationary phase and undergo almost one cell division in 45 minutes at room temperature. Please note that performing this experiment with a culture in the exponential phase, as described in some

*E. coli*protocols, would most likely result in an even stronger trend.

With this in mind, we managed to establish a workflow that omits measuring and independently diluting individual wells.

We tested to cultivate our precultures - inoculated from glycerol stocks - directly in 96 well plates and incubated this plate for five to six hours in a platereader or shaking incubator. For

*V. natriegens*, this time frame is sufficient for all cultures to reliably reach stationary phase which equals an overnight culture for

*E. coli*. The cultures are then diluted in two steps: 1:50 and 1:40, finally resulting in a 1:2000 dilution of the preculture. Compared to the commonly used workflows for

*E. coli*, our approach does not consider the OD

_{600}of individual wells but instead dilutes all cultures by the same factor.

The OD

_{600}after 1:50 dilution of the 96 well plate is shown in figure 1 B. Despite not calculating dilutions for individual wells, the range of values is explicitly smaller and additionally no positional bias can be observed.

Moreover, we realized that inoculating with a low cell number is advantageous when working with

*V. natriegens*. This prolongs the exponential phase, the period in which most relevant data are acquired. A 1:2000 dilution results in a cell density much lower than the inoculum used in most

*E. coli*experiments.The subsequent, kinetic measurement can be completed in as less as five to six hours.

Besides the experimental workflow, we established an approach for fast and accurate data analysis. From a mathematical point of view, constant production and constant degradation rates result in a steady state level of a molecule. This is visualized in figure 2. While the absolute degradation increases depending on the concentration of a molecule (e.g. a reporter protein), synthesis from a constitutive promoter remains constant, independent of the concentration. In the context of our reporter experiments, the concentration represents the number of reporter per cell. The intersection of both graphs yield the steady state concentration because degradation and synthesis rates are balanced.

In most of our experiments we analyze the

*lux*operon which is constantly expressed. From a mathematical point of view, the dilution of a molecule by expanding the volume equals a degradation. For a growing bacterial culture, this dilution is caused by growth and cell divisions. We assume the dilution rate to be constant throughout the exponential growth phase of a culture. Consequently, the ratio of signal/OD

_{600}is expected to be constant in this time window.

With this in mind, we aimed to create an algorithm identifying the exponential growth phase and calculating the signal to OD

_{600}ratio. In our first experiments we tried to identify a point in time at which all cultures grow exponentially, and failed.

We were not able to obtain reproducible data for two reasons. Firstly, cultures grow differently, depending on the starting concentration and fitness differences caused by varying test constructs and expression strengths. Secondly, individual measurements are highly variable (we saw this especially for OD

_{600}measurements). Therefore performing calculations with a single value is highly susceptible to outliers.

We solved both issues by developing a Matlab script that identifies the exponential phase and includes a range of seven measuring points for each individual well. The culture is assumed to be in the mid exponential phase at an OD

_{600}of 0.2. Three time points (5 min intervals), each before and after the culture has first reached 0.2, are taken. Then the mean of all signal to OD

_{600}ratios is calculated. Through this calculation, a single value is assigned to each well representing the strength of reporter expression in the exponential phase. All samples were measured in four technical replicates in three subsequent independent experiments.

Consequently, every result (e.g. promoter strength) is, in total, the mean of 84 measurements. This high number of raw data leads to a high degree of reproducibility and, as we believe, to highly accurate characterization data.

The choice of 0.2 as the OD

_{600}threshold and the range of three time points was not randomly chosen but selected after calculating the mean CV for combinations of OD

_{600}thresholds and ranges. We tested OD

_{600}thresholds ranging from 0.1 to 0.6 with a step size of 0.01 and ranges around this data point from 0 to 10. Please note that the range is applied bidirectionally, meaning that a range of 10 considers 10 data points before and after a well has reached the OD

_{600}threshold resulting in averaging 21 time points. This 3D par plot allows for a visual estimation of advantageous combinations of OD

_{600}threshold and range of data points. Seemingly, using a rather low OD threshold (0.15 - 0.25) and a medium range size (2 - 5) yield a plateau of low coefficient of variation (CV) values as low as 10 %. In contrast, high OD thresholds (<3) and high ranges (<5) result in an explicit increase in CV data up to 38 %.

As discussed previously, expression data can be assumed to be constant throughout the exponential growth phase. To our experience, the cultures leave the fully exponential phase around OD

_{600}= 0.5 - 0.6 resulting in a divergence from the previously constant luminescence/OD ratio which leads to a loss of reproducibility for high OD

_{600}thresholds. Slightly higher CV values can also be observed for very low OD thresholds. We noted that the OD

_{600}fluctuates significantly when measuring very low cell concentrations, presumably due to technical inaccuracies. This effect can drastically effect data points for low OD

_{600}values (<0.1). Generally, we consider using a high range useful because this increases the number of data points that are averaged, thus decreasing the impact of single outliers. However, for a fast growing organism like

*V. natriegens*, the time of a culture with a OD

_{600}high enough to yield reliable measurement results but low enough to be exponentially growing is limited. Therefore an excessive range leads to inclusion of either too low or too high data points, which again reduces reproducibility.

We consider this analysis as a foundation for the selection of the OD

_{600}threshold 0.2 and a range of 3 that was used in the analysis of all our platereader experiments. This enabled us to achieve high reproducibility of our data from three subsequent, independent experiments.

clear all

close all

%% Import Platereader raw data

Lux_Data_raw_Day1 = xlsread('Connector_Lux_071018.xlsx','All Cycles');

OD_Data_raw_Day1 = xlsread('Connector_OD_071018.xlsx','All Cycles');

Lux_Data_raw_Day2 = xlsread('Connector_Lux_071018_2.xlsx','All Cycles');

OD_Data_raw_Day2 = xlsread('Connector_OD_071018_2.xlsx','All Cycles');

Lux_Data_raw_Day3 = xlsread('Connector_Lux_081018.xlsx','All Cycles');

OD_Data_raw_Day3 = xlsread('Connector_OD_081018.xlsx','All Cycles');

%% Merge all data in two matrices

OD_Data(:,:,1) = OD_Data_raw_Day1;

OD_Data(:,:,2) = OD_Data_raw_Day2;

OD_Data(:,:,3) = OD_Data_raw_Day3;

Lux_Data(:,:,1) = Lux_Data_raw_Day1;

Lux_Data(:,:,2) = Lux_Data_raw_Day2;

Lux_Data(:,:,3) = Lux_Data_raw_Day3;

%% Blank substraction and cut off

for h = 1:size(Lux_Data,3)

for k = 1:size(Lux_Data,2)

Blank = mean(Lux_Data([60,72,84,96],k,h));

for j = 1:size(Lux_Data,1)

Lux_Data(j,k,h) = Lux_Data(j,k,h)-Blank;

if Lux_Data(j,k,h) < 50

Lux_Data(j,k,h) = 50; % Cut off for Lux signal

end

end

end

end

for h = 1:size(OD_Data,3)

for k = 1:size(OD_Data,2)

Blank = mean(OD_Data([60,72,84,96],k,h));

for j = 1:size(OD_Data,1)

OD_Data(j,k,h) = OD_Data(j,k,h)-Blank;

if OD_Data(j,k,h) < 0.01

OD_Data(j,k,h) = 0.01; % Cut off for OD

end

end

end

end

%% Calculating ratio around OD 0.2

Ratios = zeros(size(OD_Data,1),1,3);

for q = 1:size(OD_Data,3) % looping over days

for o = 1:size(OD_Data,1) % looping over samples

for p = 1:size(OD_Data,2)-3 % looping through time points

if OD_Data(o,p,q) > 0.2 && OD_Data(o,p+1,q) > 0.2 && Ratios(o,q) == 0

Ratios(o,q) = mean(Lux_Data(o,p-3:p+3,q)./OD_Data(o,p-3:p+3,q));

end

end

end

end

%% Calculating mean and std of individial samples

for k = 1:12

Samples(k,1,1:3) = mean(Ratios([k,k+12,k+24,k+36],:)); % calculate mean of technical triplicates

Samples(k,2,1:3) = std(Ratios([k,k+12,k+24,k+36],:)); % calculate std of technical triplicates

end

for k = 13:24

Samples(k,1,1:3) = mean(Ratios([k+36,k+48,k+60,k+72],:)); % calculate mean of technical triplicates

Samples(k,2,1:3) = std(Ratios([k+36,k+48,k+60,k+72],:)); % calculate std of technical triplicates

end

%% Sort samples for top and bottom row

Names = {'J23100', '5Con1 long','5Con2 long','5Con3 long','5Con4 long',...

'5Con5 long','5Con1 short','5Con2 short','5Con3 short',...

'5Con4 short','5Con5 short','5Con1 long','5Con2 long','5Con3 long',...

'5Con4 long','5Con5 long','5Con1 short','5Con2 short','5Con3 short',...

'5Con4 short','5Con5 short', 'Promoter Dummy'};

Top_row = Samples(1:11,:,:);

Bot_row = Samples(13:23,:,:);

SortedValues = [];

for k = 1:size(Top_row,1)

SortedValues = [SortedValues; Top_row(k,:,:); Bot_row(k,:,:)];

end

SortedValues = [SortedValues;Samples(12,:,:)];

SortedValues(22,:,:) = [];

%% Calculate relativ strength

Relative_strength = SortedValues;

for h = 1:size(SortedValues,3)

for k = 1:size(SortedValues,1)

Relative_strength(size(SortedValues,1)-k+1,2,h) = ...

Relative_strength(size(SortedValues,1)-k+1,2,h)/Relative_strength(1,1,h);

Relative_strength(size(SortedValues,1)-k+1,1,h) = ...

Relative_strength(size(SortedValues,1)-k+1,1,h)/Relative_strength(1,1,h);

end

end

%% plot relative strenths

figure(1) % constructs with J23100

hold on

bar(mean(Relative_strength([1:11,22],1,1:3),3),'facecolor',[125/255,202/255,97/255])

errorbar((mean(Relative_strength([1:11,22],1,1:3),3)),...

std(Relative_strength([1:11,22],1,1:3),1,3),'linestyle','none','color','k')

set(gca, 'YScale', 'log')

ylabel('normalized Luminescence/OD_6_0_0')

set(gca,'Color','w')

xticks([1:12])

xticklabels(Names([1:11,22]))

xtickangle(45)

ylim([0.001 3])

yticks([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0 ])

yticklabels([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0])

figure(2) % constructs with promoter dummy

hold on

bar(mean(Relative_strength([1,12:22],1,1:3),3),'facecolor',[125/255,202/255,97/255])

errorbar((mean(Relative_strength([1,12:22],1,1:3),3)),...

std(Relative_strength([1,12:22],1,1:3),1,3),'linestyle','none','color','k')

set(gca, 'YScale', 'log')

ylabel('normalized Luminescence/OD_6_0_0')

set(gca,'Color','w')

xticks([1:12])

xticklabels(Names([1,12:22]))

xtickangle(45)

ylim([0.001 3])

yticks([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0 ])

yticklabels([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0])

close all

%% Import Platereader raw data

Lux_Data_raw_Day1 = xlsread('Connector_Lux_071018.xlsx','All Cycles');

OD_Data_raw_Day1 = xlsread('Connector_OD_071018.xlsx','All Cycles');

Lux_Data_raw_Day2 = xlsread('Connector_Lux_071018_2.xlsx','All Cycles');

OD_Data_raw_Day2 = xlsread('Connector_OD_071018_2.xlsx','All Cycles');

Lux_Data_raw_Day3 = xlsread('Connector_Lux_081018.xlsx','All Cycles');

OD_Data_raw_Day3 = xlsread('Connector_OD_081018.xlsx','All Cycles');

%% Merge all data in two matrices

OD_Data(:,:,1) = OD_Data_raw_Day1;

OD_Data(:,:,2) = OD_Data_raw_Day2;

OD_Data(:,:,3) = OD_Data_raw_Day3;

Lux_Data(:,:,1) = Lux_Data_raw_Day1;

Lux_Data(:,:,2) = Lux_Data_raw_Day2;

Lux_Data(:,:,3) = Lux_Data_raw_Day3;

%% Blank substraction and cut off

for h = 1:size(Lux_Data,3)

for k = 1:size(Lux_Data,2)

Blank = mean(Lux_Data([60,72,84,96],k,h));

for j = 1:size(Lux_Data,1)

Lux_Data(j,k,h) = Lux_Data(j,k,h)-Blank;

if Lux_Data(j,k,h) < 50

Lux_Data(j,k,h) = 50; % Cut off for Lux signal

end

end

end

end

for h = 1:size(OD_Data,3)

for k = 1:size(OD_Data,2)

Blank = mean(OD_Data([60,72,84,96],k,h));

for j = 1:size(OD_Data,1)

OD_Data(j,k,h) = OD_Data(j,k,h)-Blank;

if OD_Data(j,k,h) < 0.01

OD_Data(j,k,h) = 0.01; % Cut off for OD

end

end

end

end

%% Calculating ratio around OD 0.2

Ratios = zeros(size(OD_Data,1),1,3);

for q = 1:size(OD_Data,3) % looping over days

for o = 1:size(OD_Data,1) % looping over samples

for p = 1:size(OD_Data,2)-3 % looping through time points

if OD_Data(o,p,q) > 0.2 && OD_Data(o,p+1,q) > 0.2 && Ratios(o,q) == 0

Ratios(o,q) = mean(Lux_Data(o,p-3:p+3,q)./OD_Data(o,p-3:p+3,q));

end

end

end

end

%% Calculating mean and std of individial samples

for k = 1:12

Samples(k,1,1:3) = mean(Ratios([k,k+12,k+24,k+36],:)); % calculate mean of technical triplicates

Samples(k,2,1:3) = std(Ratios([k,k+12,k+24,k+36],:)); % calculate std of technical triplicates

end

for k = 13:24

Samples(k,1,1:3) = mean(Ratios([k+36,k+48,k+60,k+72],:)); % calculate mean of technical triplicates

Samples(k,2,1:3) = std(Ratios([k+36,k+48,k+60,k+72],:)); % calculate std of technical triplicates

end

%% Sort samples for top and bottom row

Names = {'J23100', '5Con1 long','5Con2 long','5Con3 long','5Con4 long',...

'5Con5 long','5Con1 short','5Con2 short','5Con3 short',...

'5Con4 short','5Con5 short','5Con1 long','5Con2 long','5Con3 long',...

'5Con4 long','5Con5 long','5Con1 short','5Con2 short','5Con3 short',...

'5Con4 short','5Con5 short', 'Promoter Dummy'};

Top_row = Samples(1:11,:,:);

Bot_row = Samples(13:23,:,:);

SortedValues = [];

for k = 1:size(Top_row,1)

SortedValues = [SortedValues; Top_row(k,:,:); Bot_row(k,:,:)];

end

SortedValues = [SortedValues;Samples(12,:,:)];

SortedValues(22,:,:) = [];

%% Calculate relativ strength

Relative_strength = SortedValues;

for h = 1:size(SortedValues,3)

for k = 1:size(SortedValues,1)

Relative_strength(size(SortedValues,1)-k+1,2,h) = ...

Relative_strength(size(SortedValues,1)-k+1,2,h)/Relative_strength(1,1,h);

Relative_strength(size(SortedValues,1)-k+1,1,h) = ...

Relative_strength(size(SortedValues,1)-k+1,1,h)/Relative_strength(1,1,h);

end

end

%% plot relative strenths

figure(1) % constructs with J23100

hold on

bar(mean(Relative_strength([1:11,22],1,1:3),3),'facecolor',[125/255,202/255,97/255])

errorbar((mean(Relative_strength([1:11,22],1,1:3),3)),...

std(Relative_strength([1:11,22],1,1:3),1,3),'linestyle','none','color','k')

set(gca, 'YScale', 'log')

ylabel('normalized Luminescence/OD_6_0_0')

set(gca,'Color','w')

xticks([1:12])

xticklabels(Names([1:11,22]))

xtickangle(45)

ylim([0.001 3])

yticks([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0 ])

yticklabels([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0])

figure(2) % constructs with promoter dummy

hold on

bar(mean(Relative_strength([1,12:22],1,1:3),3),'facecolor',[125/255,202/255,97/255])

errorbar((mean(Relative_strength([1,12:22],1,1:3),3)),...

std(Relative_strength([1,12:22],1,1:3),1,3),'linestyle','none','color','k')

set(gca, 'YScale', 'log')

ylabel('normalized Luminescence/OD_6_0_0')

set(gca,'Color','w')

xticks([1:12])

xticklabels(Names([1,12:22]))

xtickangle(45)

ylim([0.001 3])

yticks([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0 ])

yticklabels([0.001,0.01,0.05, 0.1, 0.5, 1.0, 2.0])

After having established a reliable workflow for

*V. natriegens*, we investigated four different reporters and measured the signal to blank ratio. Test constructs (shown in figure 5) were built by using the same set of parts except for the coding sequence. sfGFP, RFP, YFP and the

*lux*operon were analyzed for their performance in

*V. natriegens*. The best signal to blank ratio by far was achieved for the

*lux*operon (2000), followed by sfGFP (3), RFP (1) and YFP (no detectable signal). The main explanation for the superior performance of the

*lux*operon is the almost complete absence of background signal without reporter expression. This makes the

*lux*operon a perfect reporter that can even be used to analyze extremely low levels of expression caused by very weak promoters or terminator read through. Based on this finding, we decided to use the

*lux*operon as our reporter for all subsequent experiments.

In contrast to fluorescence reporters, the enzymes expressed from the

*lux*operon lead to continuous emission of light. This can result in increased cross talk between neighboring wells. The extent of cross talk highly depends on the type of 96 well plate that is used in the experiment. We analyzed the cross talk in clear and black 96 well plates by placing a single lux expressing sample in well C3 and filled all remaining wells with medium. As can be seen in figure 6, the signal from a single well is sufficient to significantly illuminate a huge portion of the clear plate (~ 1 % signal overflow to neighboring wells) while the cross talk is reduced tenfold when using a black plate (~ 0.1 % signal overflow to neighboring wells).

Thus, we used black plates and payed attention not to place the brightest cultures in direct proximity to the darkest cultures. Therefore we do not see crosstalk as a decisive argument against the

*lux*operon. However, algorithms are under development that will allow for a mathematical correction to further improve the performance of the

*lux*operon as reporter (Mauri

*et al.*unpublished)

The dynamic range of a reporter experiment does not only depend on the used reporter but also on the copy number of the tested plasmids, which is determined by the used origin of replication. We wanted to identify the ori which yields the highest dynamic range when expressing the

*lux*operon. To do that, we constructed three plasmids expressing Lux. All parts, except for the ori, were identical and tested them for signal strength. We obtained the highest expression from the construct harboring the ColE1 ori, followed by p15A and pMB1 (figure 7). We suggest that ColE1 yields plasmids with the highest copy number. We performed qPCR experiments that that support this hypothesis. We observed a qualitative correlation between copy number and expression strength. As a high dynamic range is essential for analyzing weak expression levels, we chose ColE1 as our default ori for all subsequent experiments.