Dr John C Vardakis, BEng, PhD - University of Leeds/Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) - Research Fellow in Image-based Computational Modelling in Cardiology

Dr John C Vardakis

BEng, PhD

University of Leeds/Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB)

Research Fellow in Image-based Computational Modelling in Cardiology

Leeds | United Kingdom

Main Specialties: Cardiovascular Disease, Neurology, Spinal Cord Injury Medicine, Sports Medicine, Statistics, Surgery, Vascular & Interventional Radiology, Vascular Surgery

Additional Specialties: CFD, FEM, Biomedical Engineering

ORCID logohttps://orcid.org/0000-0003-2391-5257

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Dr John C Vardakis, BEng, PhD - University of Leeds/Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) - Research Fellow in Image-based Computational Modelling in Cardiology

Dr John C Vardakis

BEng, PhD


Dr John Vardakis joined the Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) at the University of Leeds in December 2019, as a Research Fellow in Image-based Computational Modelling in Cardiology. He will be part of the InSilc project, where he will assist in the development of image-based models of the coronary vasculature and the simulation of coronary flow dynamics.

Previously, John was a Senior Research Associate to Prof. Yiannis Ventikos (in the field of Brain Biomechanics) at University College London (2014-2019). His main focus whilst occupying this position was the development of a computational framework that aided in the understanding of cerebral diseases arising from Dementia. The foundations of the mathematical modelling that he worked on revolved around Multiple-Network Poroelastic Theory, adapted to patient-specific cases and simulated through a combination of CFD and in-house FEM-based numerical templates. John participated in the VPH-DARE@IT project (2013-2017), that delivered the first patient-specific predictive models for early differential diagnosis of dementia and its evolution. Additionally, he also collaborated with the UCL Institute of Neurology, to work on the Epilepsy Navigator project (2015-2016). His role involved the conceptual redesign and feasibility study of mechanical components and procedures allied to the Stereoelectroencephalography electrode placement. In the last couple of years, his research also focused on plant mechanobiology and high energy plasma hydrodynamics applications.

John holds a D.Phil (Ph.D) in Integrative Cerebral Dynamics from the University of Oxford (2010-2014). His DPhil was supervised by Prof. Yiannis Ventikos, and both structured and funded through the Centre for Doctoral Training in Healthcare Innovation (2009-2010). He completed the Science Innovation Plus programme in the Centre for Entrepreneurship and Innovation at the Said Business School (University of Oxford, 2011-2012) as part of his D.Phil studies, and received his B.Eng (Hons) in Mechanical Engineering from King's College London in 2008. His final year project was supervised by Prof. Kaspar Althoefer (modelling of tool-environment interaction dynamics during laparoscopy). He was awarded the Siemens Prize (1st), King's College Engineering Society Centenary Prize and Gilbert Cook Bursary during his studies.

Primary Affiliation: University of Leeds/Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB) - Leeds , United Kingdom


Additional Specialties:

Research Interests:

View Dr John C Vardakis’s Resume / CV


Feb 2015
University of Oxford
Engineering Science
Aug 2008
King's College London
Mechanical Engineering


Dec 2019
Image-based Computational Modelling in Cardiology
Research Fellow
University of Leeds
May 2015
Integrative Cerebral Dynamics
Research Associate




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A multiple-network poroelastic model for biological systems and application to subject-specific modelling of cerebral fluid transport

Int J Eng Sci, 2020 Feb;147:103204

International Journal of Engineering Science

Biological tissue can be viewed as porous, permeable and deformable media infiltrated by fluids, such as blood and interstitial fluid. A finite element model has been developed based on the multiple-network poroelastic theory to investigate transport phenomenon in such biological systems. The governing equations and boundary conditions are adapted for the cerebral environment as an example. The numerical model is verified against analytical solutions of classical consolidation problems and validated using experimental data of infusion tests. It is then applied to three-dimensional subject-specific modelling of brain, including anatomically realistic geometry, personalised permeability map and arterial blood supply to the brain. Numerical results of smoking and non-smoking subjects show hypoperfusion in the brains of smoking subjects, which also demonstrate that the numerical model is capable of capturing spatio-temporal fluid transport in biological systems across different scales.

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December 2019

Impact Factor 9.052

3 Reads

Highly integrated workflows for exploring cardiovascular conditions: Exemplars of precision medicine in Alzheimer's disease and aortic dissection

Morphologie, 103(343), 148-160


SummaryFor precision medicine to be implemented through the lens of in silico technology, it is imperative that biophysical research workflows offer insight into treatments that are specific to a particular illness and to a particular subject. The boundaries of precision medicine can be extended using multiscale, biophysics-centred workflows that consider the fundamental underpinnings of the constituents of cells and tissues and their dynamic environments. Utilising numerical techniques that can capture the broad spectrum of biological flows within complex, deformable and permeable organs and tissues is of paramount importance when considering the core prerequisites of any state-of-the-art precision medicine pipeline. In this work, a succinct breakdown of two precision medicine pipelines developed within two Virtual Physiological Human (VPH) projects are given. The first workflow is targeted on the trajectory of Alzheimer's Disease, and caters for novel hypothesis testing through a multicompartmental poroelastic model which is integrated with a high throughput imaging workflow and subject-specific blood flow variability model. The second workflow gives rise to the patient specific exploration of Aortic Dissections via a multi-scale and compliant model, harnessing imaging, computational fluid-dynamics (CFD) and dynamic boundary conditions. Results relating to the first workflow include some core outputs of the multiporoelastic modelling framework, and the representation of peri-arterial swelling and peri-venous drainage solution fields. The latter solution fields were statistically analysed for a cohort of thirty-five subjects (stratified with respect to disease status, gender and activity level). The second workflow allowed for a better understanding of complex aortic dissection cases utilising both a rigid-wall model informed by minimal and clinically common datasets as well as a moving-wall model informed by rich datasets.RésuméPour que la médecine actuelle puisse profiter de la technologie in silico, il est impératif que les flux de recherche biophysique offrent un aperçu précis des traitements spécifiques à une maladie particulière et à un sujet particulier. Les limites de la médecine peuvent être repoussées à l’aide de flux de travail multi-échelles, centrés sur la biophysique, qui tiennent compte des constituants fondamentaux des cellules et des tissus, et de leurs environnements dynamiques. L’utilisation de techniques numériques permettant de capter le large spectre des flux biologiques au sein d’organes et de tissus complexes, déformables et perméables est d’une importance capitale lorsqu’il s’agit d’examiner les conditions essentielles de tout pipeline médical de précision de pointe. Dans ce travail, une analyse succinte de deux pipelines de médecine de précision développés dans le cadre de deux projets VPH (Virtual Physiological Human) est donnée. Le premier flux de travail se concentre sur la trajectoire de la maladie d’Alzheimer et permet de tester de nouvelles hypothèses au moyen d’un modèle poroélastique à plusieurs compartiments qui est intégré à un flux de travail d’imagerie à haut débit et à un modèle de variabilité du débit sanguin spécifique au sujet. Le deuxième flux de travail donne lieu à l’exploration spécifique des dissections aortiques chez le patient par le biais d’un modèle multi-échelle conforme, exploitant l’imagerie, la dynamique des fluides computationnelle (CFD) et les conditions limites dynamiques. Les résultats relatifs au premier flux de travail comprennent certains des principaux extrants du cadre de modélisation multiporoélastique et la représentation des zones de gonflement péri-artériel et de solution de drainage périveineux. Ces dernières zones de solutions ont été analysées statistiquement sur une cohorte de trente-cinq sujets (stratifiés en fonction de l’état pathologique, du sexe et du niveau d’activité). Le deuxième flux de travail a permis de mieux comprendre les cas complexes de dissection aortique à l’aide d’un modèle à parois rigides fondé sur des ensembles de données minimales et cliniquement communes et d’un modèle à parois mobiles reposant sur de riches données.

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November 2019
2 Reads

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data.

Front Comput Neurosci 2019 3;13:60. Epub 2019 Sep 3.

Department of Mechanical Engineering, University College London, London, United Kingdom.

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http://dx.doi.org/10.3389/fncom.2019.00060DOI Listing
September 2019
4 Reads
2.201 Impact Factor

Fluid–structure interaction for highly complex, statistically defined, biological media: Homogenisation and a 3D multi-compartmental poroelastic model for brain biomechanics

Journal of Fluids and Structures (2019),https://doi.org/10.1016/j.jfluidstructs.2019.04.008.

Journal of Fluids and Structures

Numerous problems of relevance in physiology and biomechanics, have at their core, the presence of a deformable solid matrix which experiences flow-induced strain. Often, this fluid–structure interaction (FSI) is directed the opposite way, i.e. it is solid deformation that creates flow, with the heart being the most prominent example. In many cases, this interaction of fluid and solid is genuinely bidirectional and strongly coupled, with solid deformation inducing flow and fluid pressure deforming the solid. Although an FSI problem, numerous cases in biomechanics are not tractable via the traditional FSI methodologies: in the internalflows that are of interest to use, the number and range of fluid passages is so vast that the direct approach of a deterministically defined boundary between fluid and solid is impossible to apply. In these cases, homogenisation and statistical treatment of the material-fluid system is possibly the only way forward. Such homogenisation,quite common to flow-only systems through porous media considerations, is also possible for FSI systems, where the loading is effectively internal to the material. A prominent technique of this type is that of poroelasticity. In this paper, we discuss a class of poroelastic theory techniques that allow for the co-existence of a multitude of – always statistically treated – channels and passages of widely different properties: termed multiple-network poroelasticity (or multicompartmental poroelasticity). This paradigm is particularly suitable for the study of living tissue, that is invariably permeated – perfused – by fluids, often different in nature and across a wide range of scales. Multicompartmental poroelasticity is capable of accounting for bidirectional coupling between the fluids and the solid matrix and allows us to track transport of a multitude of substances together with the deformation of the solid material that this transport gives rise to or is caused by, or both. For the purposes of demonstration, we utilise a complex and physiologically very important system, the human brain (specifically, we target the hippocampus), to exemplify the qualities and efficacy of this methodology during the course of Alzheimer’s Disease. The methodology we present has been implemented through the Finite Element Method, in a general manner, allowing for the co-existence of an arbitrary number of compartments. For the applications used in this paper to exemplify the method, a four-compartment implementation is used. A unified pipeline is used on a cohort of 35 subjects to provide statistically meaningful insight into the underlying mechanisms of the neurovascular unit (NVU) in the hippocampus, and to ascertain whether physical activity would have an influence in both swellingand drainage by taking into account both the scaled strain field and the proportion of perfused blood injected into the brain tissue. A key result garnered from his study is the statistically significant differences in right hemisphere hippocampal NVU swelling between males in the control group and females with mild cognitive impairment during high and low activity states.

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May 2019
166 Reads

Disturbed flow induces a sustained, stochastic NF-κB activation which may support intracranial aneurysm growth in vivo

Scientific Reports. 2019. 4738(9)

Scientific Reports

Intracranial aneurysms are associated with disturbed velocity patterns, and chronic inflammation, but the relevance for these findings are currently unknown. Here, we show that (disturbed) shear stress induced by vortices is a sufficient condition to activate the endothelial NF-kB pathway, possibly through a mechanism of mechanosensor de-activation. We provide evidence for this statement through in-vitro live cell imaging of NF-kB in HUVECs exposed to different flow conditions, stochastic modelling of flow induced NF-kB activation and induction of disturbed flow in mouse carotid arteries. Finally, CFD and immunofluorescence on human intracranial aneurysms showed a correlation similar to the mouse vessels, suggesting that disturbed shear stress may lead to sustained NF-kB activation thereby offering an explanation for the close association between disturbed flow and intracranial aneurysms.

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March 2019
19 Reads

Response to letter to the editor concerning "A fully dynamic multi-compartmental poroelastic system: Application to aqueductal stenosis".

J Biomech 2017 06 16;58:243-246. Epub 2017 May 16.

Department of Mechanical Engineering, University College London, UK. Electronic address:

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http://dx.doi.org/10.1016/j.jbiomech.2017.04.032DOI Listing
June 2017
61 Reads
2.751 Impact Factor

A fully dynamic multi-compartmental poroelastic system: Application to aqueductal stenosis.

J Biomech 2016 07 28;49(11):2306-2312. Epub 2015 Nov 28.

Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK. Electronic address:

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http://dx.doi.org/10.1016/j.jbiomech.2015.11.025DOI Listing
July 2016
52 Reads
2.751 Impact Factor

Investigating cerebral oedema using poroelasticity.

Med Eng Phys 2016 Jan 31;38(1):48-57. Epub 2015 Dec 31.

Department of Mechanical Engineering, University College London, Torrington Place, London WC1E 7JE, UK. Electronic address:

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http://dx.doi.org/10.1016/j.medengphy.2015.09.006DOI Listing
January 2016
60 Reads
1.825 Impact Factor

Exploring the efficacy of endoscopic ventriculostomy for hydrocephalus treatment via a multicompartmental poroelastic model of CSF transport: a computational perspective.

PLoS One 2013 31;8(12):e84577. Epub 2013 Dec 31.

Department of Mechanical Engineering, University College London, Torrington Place, London, United Kingdom.

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September 2014
60 Reads
3.234 Impact Factor

Multicompartmental Poroelasticity as a Platform for the Integrative Modeling of Water Transport in the Brain

n: GA. Holzapfel & E Kuhl (ed.), Computer Models in Biomechanics: From Nano to Macro, Springer-Verlag, Heidelberg

This work proposes the implementation of a multiple-network poroelastic theory (MPET) model for the purpose of investigating in detail the transport of water within the cerebral environment. The key advantage of using the MPET representation is that it accounts for fluid transport between CSF, brain parenchyma and cerebral blood. A further novelty in the model is the amalgamation of anatomically accurate Choroid Plexus regions, with their individual feeding arteries. This model is used to demonstrate and discuss the impact of aqueductal stenosis on the cerebral ventricles, along with possible future treatment techniques.

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October 2012
65 Reads