Chief Researcher Officer

Synteny Biotechnology

Biography

I am the Chief Research Officer at Synteny Biotechnology. Our organisation uses modern AI methodologies combined with high-throughput experimental techniques to understand the specificities of T-cells, and in doing so, enable a new generation of T-cell based therapies and diagnostics. The company was founded by Lilly Wollman and Jamie Blundell, and I joined in January 2022 to build the organisation from scratch. By working at Synteny, I am able to focus on researching the fascinating natural computation that our bodies perform to identify pathogens and dysfunctional cells.

Before Synteny, I was a Principal Scientist and Research Manager at Microsoft Research Cambridge and project lead for Station B. Before that, I was a PhD student at University of Cambridge, where I worked on circadian timing in plants in the laboratory of Alex Webb.

During my career, I have always operated at the intersection of biological data and computational analysis. I have made biological discoveries using a wide range of computational techniques. The majority of my earlier work used ordinary differential equation (ODE) models and stochastic chemical kinetics (essentially continuous-time Markov chains). I have also developed techniques for parameter inference and parameter synthesis with dynamical models. In my later years at Microsoft Research, I became interested in probabilistic machine learning models and active learning approaches, including Bayesian optimization. At Synteny, the primary observations are of amino acid sequences, and so I have become interested in using methods that can classify functional properties of those sequences, or repertoires (sets) of (T-cell receptor and antigen) sequences.

At Synteny, I have been able to reignite my interest in Immunology, which started when I first joined Andrew Phillips’ research group at Microsoft in 2009. Together, we developed some of the first dynamical models of antigen presentation by class I molecules of the major histocompability complex (MHC), collaborating with Tim Elliott, who at the time was at the University of Southampton.

Interests

  • Immunology
  • Synthetic Biology
  • DNA Computing
  • Bayesian Inference
  • Bayesian Optimization

Education

  • PhD in Plant Sciences, 2009

    University of Cambridge

  • MMath in Mathematics, 2005

    University of Oxford

Industry Experience

 
 
 
 
 

Chief Research Officer

Synteny

Jan 2022 – Present Cambridge, UK
 
 
 
 
 

Principal Research Manager

Microsoft Research

Nov 2020 – Oct 2021 Cambridge, UK
Station B sought to improve the way we engineer biological systems, bringing together computational modelling, lab automation and machine learning. My role was to lead this project, and seek opportunities for Microsoft in the biotechnology industry.
 
 
 
 
 

Principal Scientist

Microsoft Research

Jan 2015 – Sep 2020 Cambridge, UK
 
 
 
 
 

Scientist

Microsoft Research

Mar 2012 – Dec 2014 Cambridge, UK
 
 
 
 
 

Postdoctoral Researcher

Microsoft.Research

Feb 2012 – Sep 2009 Cambridge, UK
During this post-doc, I was researching the use of dynamical systems models applied to immune system processes and synthetic biology. In particular, I helped the construction of the first model of the MHC class I antigen presentation mechanism, a central player in the adaptive immune system (Dalchau et al., PLoS Computational Biology 2011). In Synthetic Biology, I constructed and analysed models of synthetic cell populations which had been modified to give rise to Turing patterns (unpublished) or periodic travelling waves (Dalchau et al., J Royal Society Interface 2012).
 
 
 
 
 

Research Intern

Microsoft Research

Jun 2009 – Aug 2009 Cambridge, UK
This internship was a prequel to my subsequent postdoctoral position at Microsoft Research.
 
 
 
 
 

Support and Applications Engineer

Vector Fields Ltd.

Jul 2004 – Sep 2005 Oxford, UK
Vector Fields provide software for the analysis and design of electromagnetic devices. 3d and 2d models solved using Finite Element methods. I supported international clients, and provided consultancy and benchmarks for existing and prospective customers.

Academic Experience

 
 
 
 
 

Affiliate Researcher

Kings College London

Aug 2020 – Present London, UK
Co-supervision of PhD student Grisha Szep, in collaboration with Attila Csikasz-Nagy.
 
 
 
 
 

Visiting Researcher

University of Edinburgh

Nov 2012 – Dec 2017 Edinburgh, UK
Co-supervision of PhD student Yiyu Xiang, in collaboration with Baojun Wang.
 
 
 
 
 

Visiting Researcher

University College London

Oct 2012 – Aug 2017 London, UK
Co-supervision of two PhD students, Laura Parshotam and Ruth Charlotte Eccleston, in collaboration with Peter Coveney. Both students successfully defended their PhD theses on time.
 
 
 
 
 

Postdoctoral Researcher

University of Cambridge

Nov 2008 – May 2009 Cambridge, UK
Investigating noise suppression in gene regulatory networks, advised by Dr Glenn Vinnicombe. Experience with doubly stochastic processes, stochastic control and Markov birth-death processes.
 
 
 
 
 

College Supervisor

Robinson College, University of Cambridge

Oct 2006 – Jul 2008 Cambridge, UK
Taught undergraduate Natural Sciences students for the lecture course ‘Quantitative Biology’ (QB) for two years.

Accomplishments

Honorary Senior Research Associate, Department of Chemistry

Tansley Medal

The New Phytologist Tansley Medal was established in 2009 for the recognition of outstanding contributions made by scientists early in their independent career.
See certificate

Recent Publications

Decentralizing Cell-Free RNA Sensing With the Use of Low-Cost Cell Extracts

Cell-free gene expression systems have emerged as a promising platform for field-deployed biosensing and diagnostics. When combined with programmable toehold switch-based RNA sensors, these systems can be used to detect arbitrary RNAs and freeze-dried for room temperature transport to the point-of-need. These sensors, however, have been mainly implemented using reconstituted PURE cell-free protein expression systems that are difficult to source in the Global South due to their high commercial cost and cold-chain shipping requirements. Based on preliminary demonstrations of toehold sensors working on lysates, we describe the fast prototyping of RNA toehold switch-based sensors that can be produced locally and reduce the cost of sensors by two orders of magnitude. We demonstrate that these in-house cell lysates provide sensor performance comparable to commercial PURE cell-free systems. We further optimize these lysates with a CRISPRi strategy to enhance the stability of linear DNAs by knocking-down genes responsible for linear DNA degradation. This enables the direct use of PCR products for fast screening of new designs. As a proof-of-concept, we develop novel toehold sensors for the plant pathogen Potato Virus Y (PVY), which dramatically reduces the yield of this important staple crop. The local implementation of low-cost cell-free toehold sensors could enable biosensing capacity at the regional level and lead to more decentralized models for global surveillance of infectious disease.

A Deep Learning Model for Predicting NGS Sequencing Depth from DNA Sequence

Targeted high-throughput DNA sequencing is a primary approach for genomics and molecular diagnostics, and more recently as a readout for DNA information storage. Oligonucleotide probes used to enrich gene loci of interest have different hybridization kinetics, resulting in non-uniform coverage that increases sequencing costs and decreases sequencing sensitivities. Here, we present a deep learning model (DLM) for predicting Next-Generation Sequencing (NGS) depth from DNA probe sequences. Our DLM includes a bidirectional recurrent neural network that takes as input both DNA nucleotide identities as well as the calculated probability of the nucleotide being unpaired. We apply our DLM to three different NGS panels: a 39,145-plex panel for human single nucleotide polymorphisms (SNP), a 2000-plex panel for human long non-coding RNA (lncRNA), and a 7373-plex panel targeting non-human sequences for DNA information storage. In cross-validation, our DLM predicts sequencing depth to within a factor of 3 with 93% accuracy for the SNP panel, and 99% accuracy for the non-human panel. In independent testing, the DLM predicts the lncRNA panel with 89% accuracy when trained on the SNP panel. The same model is also effective at predicting the measured single-plex kinetic rate constants of DNA hybridization and strand displacement.

Projects

Variational inference for ODE models

Variational autoencoders can be used to infer hierarchical (global, group-level and individual-level) parameters of dynamical systems models (ordinary differential equations), and leverages gradient-based optimization

DNA Computing

Programming DNA molecules offers the potential for applications from bio-sensing to intelligent nanomedicine

Chemical Reaction Networks

Tools for programming chemical reaction networks (CRN), DNA strand-displacement circuits (DSD) and genetically engineered circuits (GEC)

Synthetic Biology

Synthetic biology aims to use engineering principles to augment natural cells with the ability to carry out well-defined functions that confer industrial or societal benefits

Immunology

Computational modelling of immune system processes

Plant Biology

My research in plant biology includes circadian clocks and how the decision to flower depends on carbon-nitrogen availability

People

People I’ve Worked Closely With

Microsoft Research

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Andrew Phillips

Innovator and leader in molecular and genetic programming

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Colin Gravill

Software Developer