Station B

Station B





Building a platform for programming biology

The ability to program biology could enable fundamental breakthroughs across a broad range of industries, including medicine, agriculture, food, construction, textiles, materials and chemicals. It could also help lay the foundation for a future bioeconomy based on sustainable technology. Despite this tremendous potential, programming biology today is still done largely by trial-and-error. To tackle this challenge, the field of synthetic biology has been working collectively for almost two decades to develop new methods and technology for programming biology. Station B is part of this broader effort, with a focus on developing an integrated platform that enables selected partners to improve productivity within their own organisations, in line with Microsoft’s core mission. The Station B project builds on over a decade of research at Microsoft on understanding and programming information processing in biological systems, in collaboration with several leading universities. The name Station B is directly inspired by Station Q, which launched Microsoft’s efforts in quantum computing, but focuses instead on biological computing.

The Station B platform is being developed at Microsoft Research in Cambridge, UK, which houses Microsoft’s first molecular biology laboratory. The platform aims to improve all phases of the Design-Build-Test-Learn workflow typically used for programming biological systems:

  • The Design phase will incorporate biological programming languages that operate at the molecular, genetic and network levels. These languages can in turn be compiled to a hierarchy of biological abstractions, each with their associated analysis methods, where different abstractions can be selected depending on the biological question being addressed. For example, a Continuous Time Markov Chain can be used to determine how random noise affects system function, using stochastic simulation or probabilistic model-checking methods.
  • The Build phase will incorporate compilers that translate high-level programs to DNA code, together with a digital encoding of the biological experiments to be performed.
  • The Test phase will execute biological experiments using lab robots in collaboration with technology partner Synthace, by using their award-winning Antha software, a powerful software platform built on Azure Internet of Things that gives biologists sophisticated control over lab hardware.
  • The Learn phase will incorporate a range of methods for extracting biological knowledge from experimental data, including Bayesian inference, symbolic reasoning and deep learning methods, running at scale on Azure.

These phases will be integrated with a biological knowledge base that stores computational models representing the current understanding of the biological systems under consideration. As new experiments are performed, the knowledge base will be updated via automated learning.

Researcher in Microsoft wet lab examining biological compound

Cambridge (UK) houses Microsoft’s first wet lab, where the Station B team is developing its end-to-end system. Photo by Jonathan Banks.

Partnering for transformation

Station B is a collaborative effort being carried out with selected technology, academic and commercial partners.

Technology partner Synthace provides a key abstraction layer for the digital encoding of biological experiments. Their Antha software allows the same digitally encoded experiment to be executed by a range of lab automation devices made by different manufacturers, much like printer drivers allow the same PDF to be printed by any make or model of printer. This ability to run experiments in the same way each time gives users higher confidence in their results. Antha aims to tackle the reproducibility crisis in biological experiments by digitally encoding all aspects of the experiment in a systematic fashion. It also allows substantial scaling up of experiments. This automated generation of reproducible experimental data at scale is a key requirement for machine learning, allowing users to pose and learn from much more sophisticated lines of inquiry.

Female scientist running biological experiment with male researcher in background.

Synthace’s lab automation platform allow users to run experiments from the cloud and precisely replicate each step in complicated scientific protocols. Photo by Jonathan Banks.

Female researcher working in bio-medical lab

Oxford Biomedica is pioneering gene therapies and is working with the Station B team to apply modeling and machine learning to improve its experimental and production processes. Photo by Jonathan Banks.

Princeton University are the first academic partners for Station B. The focus of the partnership will be to develop and apply the Station B platform to understand the formation of biofilms – surface associated colonies of bacteria that kill as many people as cancer and play a key role in antibiotic resistance, recognised by the world health organisation as a global crisis. The partnership will involve a collaboration with Prof. Bonnie Bassler, a world-leading microbiologist and chair of Princeton’s Department of Molecular Biology. The Princeton team also includes Bassler’s longtime collaborator Prof. Ned Wingreen, a physicist in Princeton’s Lewis-Sigler Institute for Integrative Genomics. The Station B platform will be deployed in the Bassler lab to construct and test different versions of proteins that are key to biofilm formation, and to build on their extensive inventory of genetic components, models and experimental data collected over many years of studying bacterial biofilms.

Oxford Biomedica are the first commercial partners for Station B and are a leading cell and gene therapy company. They secured a deal with Novartis to produce the first treatment approved in the U.S. and the E.U. that reprograms a patient’s own immune cells to recognize and kill cancer cells in patients with leukemia and lymphoma. The drug must be specially made for each individual and costs nearly half a million dollars to treat a child with acute lymphoblastic leukemia. Before the treatment, those children typically had weeks or months to live. After receiving the cell therapy, 81 percent of the children in clinical trials went into remission. The initial goal of the partnership will be to apply the modeling and machine learning expertise developed at Station B to improve both the production yield and quality of therapies, in order to reduce overall costs and facilitate the future production of new therapies.



Blogs & podcasts

Programming biology with Dr. Andrew Phillips

Episode 67 | March 13, 2019 | When we think of information processing systems, we often think of computers, but we ourselves are made up of information processing systems – trillions of them – also known as the cells in our bodies. While these cells are robust, they’re also extraordinarily complex and not altogether predictable. Wouldn’t it be great, asks Dr. Andrew Phillips, head of the Biological Computation Group at Microsoft Research in Cambridge, if we could figure out exactly how these building blocks…

Microsoft Research Podcast | March 2019

With lessons learned from computers, a new platform could help boost production of lifesaving biological therapies

In recent years, companies have figured out how to engineer bacteria to make cement, helping reduce the pollution involved in traditional manufacturing. Using more advanced techniques, scientists have even programmed patients’ immune cells to recognize and kill leukemia cells, giving children who had virtually no chance of survival years of prolonged life.

Microsoft Innovation Stories | March 2019

Scientists discover how bacteria use noise to survive stress

Mutations in the genome of an organism give rise to variations in its form and function—its phenotype. However, phenotypic variations can also arise in other ways. The random collisions of molecules constituting an organism—including its DNA and the proteins that transcribe the DNA to RNA—result in noisy gene expression that can lead to variations in behavior even in the absence of mutations. In a research paper published in Nature Communications, researchers at…

Microsoft Research Blog | January 2019

Scientists use machine learning to predict DNA binding rates from sequence

By Microsoft Research Lab – Cambridge and Department of Bioengineering, Rice University The binding of DNA strands by Watson-Crick base pairing is a fundamental process in biotechnology, which is used around the world for reading and writing DNA sequences and for assembling DNA nanostructures. Yet this process remains poorly understood, and there is still no way to accurately predict how quickly two DNA strands will bind. The…

Microsoft Research Blog | November 2017

Researchers build nanoscale computational circuit boards with DNA

By Microsoft Research Human-engineered systems, from ancient irrigation networks to modern semiconductor circuitry, rely on spatial organization to guide the flow of materials and information. Living cells also use spatial organization to control and accelerate the transmission of molecular signals, for example by co-localizing the components of enzyme cascades and signaling networks. In a new paper published today by the journal Nature Nanotechnology, scientists at the…

Microsoft Research Blog | July 2017

U.K. Researcher Garners TR35 Accolade

By Douglas Gantenbein, Senior Writer, Microsoft News Center Pioneering research into programming biology has earned a Microsoft Research scientist a prestigious TR35 award, presented by Technology Review. Andrew Phillips, a 34-year-old scientist who leads the Biological Computation group at Microsoft Research Cambridge, received the award, given each year by Technology Review to recognize the world’s top innovators under the age of 35. The awards span energy, medicine, computing, communications…

Microsoft Research Blog | August 2011

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