Microsoft and Antha: Closed Loop Design, Build, Test and Learn Engineering Principles for Synthetic Genetic Networks
Company Size
1,000+
Region
- Europe
Country
- United Kingdom
Product
- Antha
- Station B Platform
Tech Stack
- Machine Learning
- Bayesian Inference
- Markov Chain Monte Carlo (MCMC)
- Ordinary Differential Equations (ODE)
Implementation Scale
- Departmental Deployment
Impact Metrics
- Digital Expertise
- Innovation Output
- Productivity Improvements
Technology Category
- Analytics & Modeling - Machine Learning
- Functional Applications - Remote Monitoring & Control Systems
- Platform as a Service (PaaS) - Data Management Platforms
Applicable Industries
- Healthcare & Hospitals
- Life Sciences
Applicable Functions
- Product Research & Development
- Quality Assurance
Use Cases
- Digital Twin
- Machine Condition Monitoring
- Predictive Maintenance
- Remote Control
Services
- Software Design & Engineering Services
- System Integration
About The Customer
Microsoft Research Cambridge, UK, was the first Microsoft Research lab established outside of the United States. Since its inception in 1997 with just three researchers, it has grown to over 130 researchers and engineers. The lab has established itself as a credible academic partner and a source of new ideas that have been integrated into Microsoft's core business. The lab's diverse team includes sociologists, mathematicians, computer scientists, designers, and biologists, contributing to some of Microsoft's most successful products and services. The Biological Computation group at Microsoft Research began working with Synthace in 2014, using Antha as part of its research into synthetic biology methods and applications.
The Challenge
The Biological Computation group at Microsoft Research in Cambridge faced the challenge of developing an integrated platform for programming biological systems more effectively. The goal was to improve all phases of the Design, Build, Test, Learn (DBTL) workflow typically used in Synthetic Biology. The main bottleneck was the physical build and test stages in the laboratory, which required a rapid and flexible method for programming the laboratory hardware. The challenge was to accelerate the DBTL cycle without compromising on experimental flexibility, and to generate sufficient breadth of data through complex experimental designs executed in a reproducible manner.
The Solution
Microsoft Research interfaced its Station B platform with Antha to leverage its experimental simulation and physical execution functionality. Antha enabled the digital to physical transition of the DBTL cycle, automating the process and ensuring data was structured appropriately for machine learning workflows. The Station B platform facilitated the combinatorial design of genetic devices, which were then physically executed on liquid handling automation platforms controlled by Antha. This integration allowed Microsoft scientists to accelerate the DBTL cycle, focusing on refining biological designs and applying computational analysis rather than manual lab operations. Antha's optimised Construct Assembly workflows enabled rapid and flexible programming of liquid handling platforms, driving the physical execution of genetic construct assemblies and subsequent automated transformation and plating out of transformants.
Operational Impact
Quantitative Benefit
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