University College London
1st floor,
90 High Holborn,
London WC1V 6LJ

Short bio

Eric’s PhD was in extragalactic astrophysics and cosmology in the Cavendish Laboratory at the University of Cambridge. He then began work into statistical & population genetics and biological networks & evolutionary biology in the Theoretical Systems Biology group at Imperial College, London. Following a stint in science communication as Scientific Liaison Officer at Sense About Science, he worked in the Centre for Process Systems Engineering developing mathematical models of protein synthesis. He subsequently joined the Evolutionary Epidemiology group at MRC Centre for Outbreak Analysis & Modelling developing computational models for how evolutionary changes at the genetic sequence level interact with those at the epidemiological scale.

In 2012 he was appointed Associate Director for UCL’s Institute for Biomedical Engineering responsible for creating new links and collaborative efforts between groups/disciplines across UCL & partner hospitals. From 2015 he headed Research & Development at UCL’s new Institute of Healthcare Engineering and in 2018 joined the Centre for Medical Image Computing where he works at the interface of brain and cardiac imaging, human genetics and patient health and biomarker data, using a mixture of neuroimaging, molecular genetics, computational biology and machine learning.

He is currently supported by the National Institute for Health Research (NIHR) University College London Hospital (UCLH) Biomedical Research Centre (BRC) research themes in Healthcare Engineering & Imaging (HE&I) and Healthcare Informatics, Genomics/Omics, and Data Science (HIGODS). In this capacity he is also part of  the UCL Institute for Health Informatics (IHI) and has an honorary NHS contract at UCLH NHS Trust. 


Current projects

Increased Genetic Cardiovascular Risk Accelerates Brain Atrophy

Cardiac disease is a well-known risk factor for developing Alzheimer’s Disease (AD). In this work we are investigating whether genetic risk for coronary artery disease (CAD) affects longitudinal brain atrophy and how the latter correlates with the amount of white matter hyperintensities (WMH) at baseline. To quantify genetic risk, we compute polygenic risk scores (PRS) for participants in the AD Neuroimaging Initiative (ADNI) study.


UKBiobank: Using MRI imaging to investigate how cardiac shape and function effect dementia-associated brain lesions and what genes and health factors underlie these changes

A diagnosis of dementia can mean a significantly diminished quality of life for those affected as well as their family and carers. As our population ages it is more important than ever that we seek a more complete understanding of the underlying causes of this disease. While there are undoubtedly many contributing factors, not least just getting older, evidence that changes in the shape and function of the heart may directly affect the brain, are gaining traction.

On brain images from particular MRI scans there are bright white patches that increase in older individuals. Interestingly, these same patches are often linked to dementia and cognitive decline. While there are different kinds of dementia – the most well-known and the one which affects the most people is Alzheimer’s disease – certain signs are invariably present. Prominent amongst these is a change to the blood flow in the vessels that provide oxygen and nutrients to the brain cells that are changed by the dementia. What then, causes these changes to the blood flow? We think it may have something to do with the way the heart is operating and uniquely, UK Biobank, offers the opportunity to investigate this link as of the approximately half-million individuals in the study, up to 100,000 will have scans of both their brain and their heart.

If we can establish a link between the heart and brain we would like to explore if there is something in the genes of these individuals that contributes to this. Once again, UK Biobank is an exceptionally rare resource in that the genetic code for each of the individuals with heart and brain images has also been mapped. This will allow us to understand something about the genetic switch that causes these changes, and knowing this, can help in the search for potential treatments.

If there are certain kinds of genes responsible, then additional information in UK Biobank such as levels of blood pressure, cholesterol, obesity and other health measures that are routinely taken in hospitals and GP surgeries can be tested for an association to those genes. If the gene switches are causing these health measures to change, which in turn are altering the heart function, that is in turn causing changes in the brain leading to dementia, then we have a made a small step in understanding the processes that cause distress to so many.



As Pathfinder Lead for Imaging in the AboutMe programme Eric is working with Software Engineers in UCL Computer Science (CS), Software Developers at the Institute of Health Informatics and Clinical Scientists at the UCLH NHS Trust to develop a pipeline for moving patient imaging data securely and anonymously from the Hospital to the University. In practice this involves implementing a dockerised container for XNAT/PostgreS/Nginx to push raw imaging data from the hospital PACS to the AboutMe Server and then to anonymise and push imaging DICOM files from behind the hospital firewall to UCL CS servers. This then allows the raw imaging data from patient CT and MRI scans to be processed on local High Performance Cluster and GPU-based hardware using novel bespoke image analysis algorithms developed in the Centre for Medical Image Computing.

AboutMe is a BRC programme which aims to embed research into routine clinical care. Better use of routine data to improve quality and safety of care is already known, however if this can be further supplemented by embedding –omics (e.g. genomics, proteomics, metabolomics) this will yield unparalleled opportunities of understanding cause, consequences of disease and discovering interventions at key points in the clinical course to improve patient outcomes.

However what is required in order to achieve this is a re-engineering of the workflows specifically creating the pipelines such that  data can be accessed with ease, within governance frameworks, and research becomes an easy by-product. We have already begun to work on these pipelines at UCLH with existing consented data on stroke, and this will be soon followed up with an outpatient based project in blood pressure. The aim is to demonstrate that we can make NHS pipelines robust and access data for research, but also how these data, e.g. genomics are best used and returned to patients. This shifts the paradigm of both routine clinical care workflows and aligns it research, but for a consensual aim of patient benefit and public health.


BOBCAT: Biosensor for fast pOint-of-care Blood Analysis of Troponin

Chest pain accounts for ~6% of all Emergency Department attendances in England and Wales, representing 700,000 visits annually, a number that continues to increase. Some studies estimate that ~80% of these individuals are admitted with an average length of stay of three days, however <25% eventually receive a diagnosis of myocardial infarction. Investigations used to confirm or refute myocardial damage include an electrocardiogram (ECG) and high-sensitivity troponin on the background of symptoms and relevant risk factors. Patients often go to their GP and whilst an ECG is within scope of primary care, troponin is not. However, if there was a point-of-care test for troponin this would have a significant health care and cost impact. In this proposal we aim to build and test a point-of-care troponin test that can be deployed in primary care. To achieve this, we have assembled a team with expertise in biosensor design, bioelectronics, data analysis and clinical experience in cardiovascular disease.

Development of a handheld point-of-care device for the detection of Troponin I and T with the aim of:

  • Building a nanosensor to detect high sensitivity Troponin I and T
  • Testing sensitivity and specificity against gold standard laboratory hospital-based troponin measures from stored samples
  • Linking nanosensor to a handheld device and test on stored samples
  • Deploying hand-held sensor in proof-of-concept clinical study

Recent funding

  • co-PI on £50k BRC HE&I award – successful
  • co-PI on £50k BRC cardiovascular award – successful
  • co-PI on £143k BRC cardiovascular award – successful


  • primary supervisor for MRes student project “Combining Imaging Biomarkers and AI on medical records to shed light on Alzheimer’s disease”
  • primary supervisor for 3rd Year Undergraduate project “Do genetic variants for cholesterol increase the likelihood of dementia?”


Selected Publications

  • E de Silva, CH Sudre, J Barnes, MA Scelsi, A Altmann, Do polygenic scores of cerebral small vessel disease MRI markers predict white matter lesions?, 2021 Alzheimer’s Association International Conference
  • Hanyi Chen, Eric de Silva, Carole H Sudre, Jo Barnes, Alexandra L Young, Neil P Oxtoby, Frederik Barkhof, Daniel C Alexander, Andre Altmann, What do data-driven Alzheimer’s disease subtypes tell us about white matter pathology and clinical progression?, 2021 Alzheimer’s Association International Conference
  • Altmann, A., Scelsi, M., Shoai, M., de Silva, E., Aksman, L., Cash, D., Hardy, J., Schott, J., A comprehensive analysis of methods for assessing polygenic burden on Alzheimer’s disease pathology and risk beyond APOE. Brain Communications, 2020
  • de Silva, E., Sudre, C. H., Scelsi, M. A., & Altmann, A. (2019). P1-141: INCREASED GENETIC CARDIOVASCULAR RISK ACCELERATES BRAIN ATROPHY. Alzheimer’s & Dementia, 15, P291.
  • Andre Altmann, Marzia A Scelsi, Maryam Shoai, Eric de Silva, David M Cash, John Hardy, Jonathan M Schott, Alzheimer’s disease polygenic burden beyond APOE acts stronger on Tau than on amyloid, Alzheimers & Dementia, 2019
  • Eric de Silva, Neil M. Ferguson, Christophe Fraser. Inferring pandemic growth rates from sequence data, Journal of the Royal Society Interface, 2012 7;9(73)
  • Betney, E. de Silva, C. Mertens, Y.Knox, J. Krishnan and I. Stansfield. Regulation of release factor expression using a translational negative feedback loop; a systems analysis, RNA.2012 Dec;18(12):2320-34.
  • Lulla Opatowski, Christophe Fraser, Jamie Griffin, Eric de Silva, Maria D. Van Kerkhove, Emily J. Lyons, Simon Cauchemez, Neil M. Ferguson. Transmission Characteristics of the 2009 H1N1 Influenza Pandemic: Comparison of 8 Southern Hemisphere countries, PLoS Pathog 7(9), 2011
  • de Silva E, Krishnan J, Betney R Stansfield I. A mathematical modelling framework for elucidating the role of feedback control in translation termination. J Theor Biol. 264:808- 821 (2010)
  • Betney R, de Silva E, Krishnan J, Stansfield I. Autoregulatory systems controlling translation factor expression: thermostat-like control of translational accuracy. RNA. 2010 16(4):655-63
  • Stumpf, MPH, Thorne, T, de Silva, E, Stewart, R, An, HJ, Lappe, M, Wiuf, C Estimating the size of the human interactome, P NATL ACAD SCI USA, 105, 6959 – 6964 (2008)
  • de Silva E. Close encounters of the irrational kind. ASTRON GEOPHYS. 2007 48:36-36.
  • de Silva, P. Ingram, I. Agrafioti, J. Swire & M.P.H.Stumpf. The effects of incomplete protein interaction data on structural and evolutionary inferences. BMC Biology 2006 3;4:39
  • Arjen R. Mensenkamp, Stuart D. Horswell, Emma L. Jones, Bethan Jones, Eric De Silva, Alec Jeffreys, Paul A. Williams, James E. Dixon, Hetal N. Patel, David N. Perkins, Rossitza P. Naoumova, Michael P. Stumpf, Carol C. Shoulders, Gene Conversion Activity Provides a Mechanism for Restricting Haplotype Diversity of Recombination Hotspots Associated with Bi-directionally Transcribed Genes, resubmitted
  • Chakraborty, M. Reinis, T. Rostron, S. Philpott, T. Dong, A. D’Agostino, R. Musoke, E. de Silva, M.P.H. Stumpf, B. Weiser, R. Burger, S.L. Rowland-Jones nef gene sequence variation among HIV-1-infected African children, HIV Medicine 7, 75-84 (2006).
  • de Silva, M.P.H. Stumpf, Complex networks and simple models in biology, Journal of the Royal Society Interface, 2, 419-430 (2005)
  • de Iorio, E. de Silva, M.P.H. Stumpf, Recombination hotspots as a point process, Philosophical Transac. Roy.Soc.London B., 360, 1597-1603 (2005).
  • de Silva, E. & Stumpf, MPH., 2003, HIV and the CCR5-32 resistance allele, FEMS Microbiology Letters 241 (2004), 1-12
  • de Silva, E, Kelly, L & Stumpf, MPH, The extent and importance of intragenic recombination, Human Genomics, 1(6) (2004), 410


  • de Silva E., Saunders R.D.E., Baker J.C. & Hunstead R., Compact, steep-spectrum radio quasars, III: ages, environment and evolution, in preparation
  • de Silva E., Baker J.C., Hunstead R. & Saunders R.D.E., Compact, steep-spectrum radio quasars, II: optical properties of Molonglo quasars, in preparation
  • de Silva E., Saunders R.D.E., Baker J.C. & Hunstead R., Compact, steep-spectrum radio quasars, I: radio properties of Molonglo quasars, in preparation
  • Baker J.C., Hunstead R.W., Athreya R.M., Barthel P.D., de Silva E., Saunders R.D.E. & Lehnert M., Associated Absorption in Radio Quasars. I. C IV Absorption and the Growth of Radio Sources, The Astrophysical Journal, 568:592-609, 2002
  • de Silva E., Saunders R.D.E., Baker J.C. & Hunstead R., Proceedings of the American Astronomical Society, “The Hi-Redshift Universe”, 1999, eds. Andy Bunker & Wil Van Breugal.