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Biology is a tangled web of complexity.

Do you know which string to pull?

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How do you
optimize your bioprocess
when you have
1,572,864 possible options?

From choosing the carbon source to selecting the operating temperature, bioprocesses optimization is all about navigating a vast option space.

At every step along the way, you need to find the best solution in a complex and multidimensional option space when you only have a few (and costly) shots on goal.

Navigating the infinite with finite resources is daunting

We know how frustrating it is to spend hours gathering and analysing data only to struggle to find the insight you need. Wrong experiments are the most expensive mistakes one can make. We’ve been there. We knew there must be a better way.

We know how frustrating it is to spend hours gathering and analysing data only to struggle to find the insight you need. Wrong experiments are the most expensive mistakes one can make. We’ve been there. We knew there must be a better way.

Navigating the infinite with finite resources is daunting

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A smarter way to navigate complexity

Adaptive Design of Experiments

Through sequential optimization
we use experimental results to design what experiments to run next and
converge faster to the optima.

Focus your experiments on where it's “hot”

Adaptive Design of Experiments (aDoE) is like your data telling you where is “hot” and where is “cold” in your search space. It balances the exploration of new territories and the exploitation of warm leads between successive rounds of experiments to get you faster to the desired solution, while also exceeding your target.

aDoE Benefits

Go beyond brute force high-throughput screening to leverage advanced statistics to navigate the complex design spaces inherent with Biological Systems. Focus only on the most information-rich experiments so you can uncover actionable insights in fewer iterations.

Improve outcomes

Go beyond the obvious and explore novel solutions
Improve outcomes

Reduce costs

Run less experiments and save on bill-of-material and FET costs
Reduce costs

Learn more

Leverage ML models for subsequent experiments
Learn more

Cut development time

Run meaningful experiments and reduce number of iterations
Cut development time

Build IP

Identify novel, non-obvious solutions that tick IP checkboxes
Build IP

Build for small dataset

Specifically designed for small dataset typical for biology
Build for small dataset

How aDoE works

Design

Identify the most relevant experimental factors and design information rich experiments.

 PLOT 1
 

 

 

Measure

Execute experiments then measure response and variability.

 PLOT 2

 

 

Model

Build advanced statistical models that fit and explain your data.

 PLOT 3

 

Optimize

Leverage mathematical models to design your next set of experiments that maximise response.

 PLOT 4

 

We enable the industry across the board

Let's grow the future

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with our team

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