Transform your laboratory productivity

Engineered by scientists, for scientists. Antha is a software platform that brings the power of the digital world into biological research, development and manufacturing.

 
 

Introducing Antha

 

Your Lab's Problem

Biology research is often manual: pipette, pen, paper, spreadsheet. One factor at a time. Error-prone and time consuming.

Even with sophisticated lab hardware, the software controlling it is often inflexible, time-consuming to re-program, and incompatible across devices.

Scientists must focus on what they can do, not what they want to do.

Synthace's Solution

Antha connects a lab’s equipment and automates biology protocols, running consistent, reproducible workflows and producing integrated data in a fraction of the time.

Antha automates lab protocols, running consistent, reproducible routines and producing integrated data in a fraction of the time.

Scientists program instruments in minutes, freeing them to focus on the science we all dream of; engineering biology to heal, feed, fuel, manufacture… and solve humanity’s grand challenges.


Antha has made it incredibly easy for us to run very complicated experiments through the use of simple ‘elements’. It has transformed our view of lab automation, enabling us to use it in many different ways.
— Esha Khullar | Dow AgroSciences

Case Study

Our lab team ran studies looking for expression of human CES1 in Escherichia coli. There is some literature precedent for this, but yields were very poor.

Data that follow shows iterative optimisation of expression over five weeks of lab work.

Graphs can be clicked to enlarge.

 

Week 1 - Factor Screen

 

15 Factors Tested

64 Experiments Run

 

Multi-Factorial Experimentation enabled us to both exclude non-significant factors and discover a significant synergy between two factors in this short time frame.

Fig.1. Actual data from experimental runs (run in microwells), most runs produced no product!

Fig.2. Two major factors were discovered - one genetic and one process factor, but they must be combined to see any production - you need chaperones, and you need to give enough time.  This explains why only a quarter of the runs showed production.

 

Week 2 - Iteration

 

15 Factors Tested

64 Experiments Run

 

In only this second iteration, using the optimised factors identified in week 1, we achieved production in 100% of runs, discovering further important genetic and process factors.

Fig.3. Data from week 2 runs show a substantial increase in production

Fig.4.  More important genetic and process factors and synergies are revealed

 

Week 3 - Refinement

The benefit of being able to run multi-factorial experimentation accurately and at pace demonstrates itself as we discover a three factor interaction that achieves 200x yield of literature precedent.

 

7 Factors Tested

80 Experiments Run

 

Fig.5. Another significant uplift in yield, but we’re seeing some zeros in production again.

Fig.6. Discovered a three factor interaction between a temperature, a timing and a genetic factor that gave zero production. Conversely, 1/8 of the design (Plasmid copy number low, time high, temp low) gives significant uplift.

 

Week 4 - Optimisation

Factors and synergies identified in previous three weeks allow us to focus in on the three factor interaction plus one other important factor - achieving 100% production in runs once more, and increasing yield even further.

 

5 Factors Tested

50 Experiments Run

 
 

Fig.7. Further increase in yield, and no zero production seen.

 
 

Week 5 - Robustness Test

 

Re-producing 8 Best Yielding Runs from Week 4

8 Experiments Run

 

Fig.8. All five weeks of experimentation shown side by side (blue bars represent final robustness test) Overall optimisation resulted in 200x improvement from literature yields of carboxylesterase in E. coli

 

Case Study Conclusion

This iterative approach, and quantity of multi-factorial experiments allowed us to generate rapid, accurate, replicable yields with expression levels consistently above 0.8 U/µl and 200x literature precedent in only 5 weeks.

What Could this Mean for my Laboratory?

  1. Reduce costs by 33% by dramatically reducing manual steps and increasing robustness
  2. Achieve 200x improvement in enzymatic activity compared to literature precedents in your own experiments
  3. 50% reduction in overall internal vector construction timelines

Antha increased throughput by an order of magnitude and enabled an estimated 25% reduction in overall vector construction timelines, a 33% reduction in costs, while dramatically reducing manual steps and increasing robustness. This enabled the rapid investigation of large arrays of genetic options alongside process factors for full bioprocess optimisation
— David Pollard | Merck & Co Inc

To see Antha in action, request a demo using the form below

We'll be in touch to learn about your laboratory needs, and arrange a time and place for a demonstration