

Neil Dalchau
Principal Research Manager
About
I am the project lead for Station B, a research group at Microsoft Research Cambridge that is exploring opportunities for using computational and mathematical methods to aide the engineering process on biological substrates. I am broadly interested in how to program computation and decision-making in biological systems. The applications of programmed biology are numerous, including the synthesis of medicines and industrial chemicals, through to the direct treatment of disease.
My general approach to research is to use mathematical and statistical models to interpret experimental measurements, employing techniques from dynamical systems theory and machine learning. These methodologies have their roots elsewhere in science but are also clearly applicable to biological applications. However, there is a significant challenge in the multidimensionality of biological systems, and so I am interested in developing new methodologies or adjusting existing ones to address this challenge.
I studied Mathematics at the University of Oxford, UK…
Featured content

Efficient inference for dynamical models using variational autoencoders
Dynamical systems theory provides a mathematical framework for studying how complex systems evolve over time, such as the neurons in our brains, the global climate system, or engineered cells. But predicting how these systems will behave in the future or…

Researchers build nanoscale distributed DNA computing systems from artificial protocells
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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...

Researchers build nanoscale computational circuit boards with DNA
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