by Ioannis Tollis and Nicholas Ayache

Initiated by the European Commission as a major multidisciplinary scientific challenge, the Virtual Physiological Human (VPH) aims to develop robust, in silico models of human physiology and pathology. The desired outcomes include the identification of novel diagnostic biomarkers, the optimization of clinical decision-making and the discovery of innovative therapies. In this way, individualized models of human function could serve as virtual testbeds for a better understanding of pathophysiological processes (ie the disturbance of normal mechanical, physical and biochemical functions), as well as evaluating potential therapeutical strategies in silico.

illustration

This scientific direction is reflected in the recent calls for projects for the European Union's 7th Framework Programme on VPH, which will target:

  • patient-specific computational modelling and simulation of organs or systems targeting specific clinical needs such as prediction of diseases
  • data integration and knowledge extraction, and most importantly
  • clinical applications and demonstration of the tangible benefits of patient-specific computational models.

This European initiative will give rise to new environments for predictive, individualized, evidence-based, more effective and safer healthcare. In addition, better therapy and the modelling of adverse drug effects will reduce medical errors and improve patient safety.

However, in developing such patient-specific models it is crucial to implement multi-layered models that describe different properties (eg electrical, mechanical and biochemical), and appropriate image analysis and data assimilation tools to identify their specific parameters from patient images. An ultimate goal is to meaningfully integrate these models to describe/model/mimic some life function, rather than individual properties, at different scales; for example, from cellular dynamics to organ function. To realize this ambitious scientific vision it will be necessary to address a number of challenges related both to infrastructure and data management, computational issues, validation and legal ethical issues.

by Marco Viceconti

The Virtual Physiological Human (VPH) is a methodological and technological framework that, once established, will enable collaborative investigation of the human body as a single complex system.

by Vangelis Sakkalis and Ioannis G. Tollis

Recent research has exploited graph theory in the development and implementation of an advanced electroencephalogram (EEG) analysis framework for modelling and visualizing cognitive brain functions under normal or pathological conditions. There is special interest in using graph theory to study brain networks, since it offers a unique perspective to the study of local and distributed brain interactions.

by Xavier Pennec

Understanding and modelling the individual anatomy of the brain and its variability over a population is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. This also raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. Applications are very important both in neuroscience, to minimize the influence of the anatomical variability in functional group analyses, and in medical imaging, to better drive the adaptation of generic models of the anatomy (atlas) into patient-specific data.

by Alejandro F. Frangi, Aurelio Ruiz and Martin Hofmann-Apitius

The @neurIST project will develop a vertical and integrative approach to knowledge discovery, personalized risk assessment, patient guideline generation and treatment design. The project will have a big impact on the way that cerebral aneurysms are understood and handled and will provide a reusable and scalable approach to other diseases.

by Christian Barillot

Activities of 'VisAGeS' - a research team jointly affiliated to INSERM (National Institute of Health and Scientific Research) and INRIA - are focused on computational modelling of pathologies of the central nervous system. The team addresses a number of general problems: the conception of the surgical room of the future, achieving a better understanding of normal and pathological behaviour of the brain and other organs, and the promotion and support of virtual organizations of biomedical actors by means of HealthGrid technologies.

by Olivier Clatz, Ender Konukoglu, Pierre-Yves Bondiau, Simon Warfield, Hervé Delingette and Nicholas Ayache

Computational models of brain tumor have gained attention among scientists in the last decade. Equations describing these models now include different components of the growth: cell proliferation, migration through the tissue and expansion. Recent efforts were devoted to the inclusion of patient-specific data into the model. Simulation results demonstrate a good correlation with radiological observations and allow for new perspectives in neuro-oncology.

by Georgios Stamatakos

Approaching biology as the physical science of living matter dictates the development of a parsimonious mathematical and computational formulation of multiscale biological phenomena. Such a long-term endeavour must be collaborative on a worldwide scale. The combination of cancer biology with in silico oncology can serve as a valuable paradigm for such a process. Here we outline simulation results on the response of tumorous and normal tissues to therapeutic schemes. These simulations were developed over the last decade by the In Silico Oncology Group at the National Technical University of Athens.

by Robert G. Belleman, Michael Scarpa and Bram Stolk

Can virtual reality help to understand tumour growth? Researchers at the Section Computational Science of the University of Amsterdam (UvA), SARA Computing and Networking Services (SARA) in the Netherlands and the In-Silico Oncology Group of the National Technical University of Athens (NTUA) have combined interactive Virtual Reality visualization with in-silico tumour simulation models to better comprehend tumour growth and optimize the planning of treatment schemes.

by Jean Clairambault, François Fages and Sylvain Soliman

'Temporal Genomics for Tailored Chronotherapeutics' (Tempo), is a European project partly funded by the European Union's FP6-LifeSciHealth programme. The project investigates the possibility of individual cancer therapeutics by genetic profiling of cellular drug processing mechanisms and their circadian rhythms.

by Miguel A. González Ballester, Philippe Büchler and Nils Reimers

Researchers at the MEM Research Center (Institute for Surgical Technology and Biomechanics, University of Bern), in collaboration with Stryker Osteosynthesis, are constructing advanced statistical digital models of bone shape and biomechanical properties. These models will lead to the design of a new breed of orthopaedic implants that will guarantee an optimal fit for the whole range of patients.

by Gábor Renner and György Szántó

A computer aided system has been developed for the support of orthopedic surgery. The system provides a wide range of facilities for the design, control and navigation of clinical interventions, primarily aimed at knee surgery. For the purposes of pre-operative planning and the control during the operation, the system builds 3D models of the anatomical structures based on individual image sequences of the patient. The flexible structure enables the system to be configured to different orthopedic operations. The first application is prepared for knee ligament surgery (anterior cruciate ligament reconstruction). The work has been accomplished by a team composed of the R/D staff of SZTAKI and the Orthopedic Department of the Semmelweis University, Budapest.

by Hervé Delingette, Maxime Sermesant, Nicholas Ayache, Dominique Chapelle, Miguel Fernandez, Jean-Fréderic Gerbeau and Michel Sorine

The CardioSense3D action is an INRIA initiative that aims to develop a patient-specific simulation of cardiac activity that is suitable for clinical applications. This simulation includes a coupled model of the electrophysiological and mechanical behaviour of the heart, whose parameters are estimated based on the medical images and signals acquired on a given patient.

by Esra Neufeld

Hyperthermia is a promising treatment modality for various types of cancer. The difficulty of administering high-quality patient-specific treatment has so far hindered the acceptance of hyperthermia in most countries. Can a new approach for treatment-planning tools help?

by Matej Oresič, Jyrki Lötjönen and Catherine Bounsaythip

There has long been a consensus that there is a pressing need to bridge the gap between basic and clinical sciences, to ensure that basic research discoveries of potential relevance to patient care are effectively applied. This is a formidable challenge to implement. One of the key problems is the lack of a framework or model that would link clinically relevant information to the knowledge obtained across multiple disciplines, experimental platforms and biological systems.

by Tom Vercauteren, Aymeric Perchant and Nicholas Ayache

Fibered confocal microscopy allows the acquisition of in vivo and in situ images at the cellular level, in combination with standard endoscopic procedures or needle biopsies for solid organs. This makes it a promising tool for clinical molecular imaging, an activity aiming at in vivo characterization and measurement of biological processes. Confocal microscopy images represent a new source of information for developing patient-specific digital models that integrate knowledge of cellular dynamics. This is also an unrivaled technique for refining digital patient models down to the microscopic level.

by Kostas Marias, Thanassis Margaritis and Ioannis G. Tollis

In order to assess the clinical importance of models of human pathology (eg cancer), it is necessary to validate them with pre- and post-treatment clinical data. This in turn requires that the size and shape of the tumour, along with structural and physiological information, be determined with high resolution, accuracy and precision. ICS-FORTH has been involved in several research projects addressing image analysis, with the aim of defining optimal methods to robustly extract multiscale anatomical and functional information related to the underlying pathology. This information can be used to initialize and validate models of pathophysiology and to test simulations and predictions of the success of therapeutic regimes.

by Filippo Geraci, Mauro Leoncini, Manuela Montangero, Marco Pellegrini and Maria Elena Renda

A new approach to the analysis of large data sets resulting from microarray experiments yields high-quality results that are orders of magnitude faster than competing state-of-the-art approaches. This overcomes a significant performance bottleneck normally evident in such complex systems.

by Ioannis Tsamardinos

What gene's expression is causing another one to be expressed? Which combination of mutations is causing disease? Knowledge of causal relations is paramount in simulating the digital patient, understanding the mechanisms of disease, designing drugs and treating patients. Recent theoretical and algorithmic advances in the discovery of causal relations from observational data promise to boost our biomedical knowledge.

by Jurriaan D. Mulder

The Personal Space Station (PSS) brings Virtual Reality (VR) to the desktop of the medical and scientific professional. Its purpose is to make VR more useful and accessible for the effective analysis of 3D and 4D data in medical and biological research. To this end, PS-Tech in the Netherlands and CWI are developing and improving new techniques and methods for the application of VR in 3D and 4D data analysis.

by Frank Klefenz

The human auditory system processes very complex audio signals and deduces meaningful information like speech and music. Conventional speech processors for cochlea implants use mathematically based information-coding strategies. In a new approach being investigated by researchers at the Fraunhofer Institute for Digital Media Technology (IDMT), the human auditory system is digitally modelled as naturally as possible. This leads to a better understanding of the neural representation of sounds and their subsequent processing.

by Hugo Schnack

Magnetic resonance imaging (MRI) is a very useful tool for in vivo detection of possible morphological differences in the brains of psychiatric patients as compared to healthy persons. Due to the relatively large size of the voxels - the 3D pixels that make up the images - classification is difficult. Simulated 'orange-in-a-box' images can help to improve the classification algorithms.

Digital Biological Cell

by Tomá Bílý and Michal Karásek

Can mathematicians describe digital cells in the same way they can mimic the behaviour of biological systems? It appears that digital cells can mimic some behavioural properties, while others are being intensively studied. From some level of approximation, we can use this knowledge to connect the predictive power of these digital cell models to the methods commonly used in clinical general practice.

by Lakshmi Sastry and Srikanth Nagella

The Integrative Biology (IB) project, funded by EPSRC, is nearing completion with the building of a customized Grid framework. This is being used to run large multi-scale models, from cellular to whole organ simulations, to manage a growing database of in-vitro experimental data and simulation results, and to support advanced visualization for interactive data analysis with comparison and assimilation of experimental and observed data. The services offered by the computational framework are based on the requirements of two application areas: arrhythmia and cancer. Computational and experimental biologists are using the prototype infrastructure, thereby aiding the STFC computer scientists in improving the framework and its services.

by Mike Holcombe

Systems biology, an integrated research field involving experimental biology and computational modelling, takes a systems-level view of biological phenomena without losing the detail and complexity that is inherent in all biological systems. I call this the 'in virtuo' approach (in preference to 'in silico' which seems to imply a specific computational technology). Two investigations are discussed: one looking at how part of the innate immune system works, and the other at how skin seems to heal wounds.

by Martin Reczko, Panayiota Poirazi, Anastasis Oulas, Eleftheria Tzamali, Maria Manioudaki, Vassilis Tsiaras and Ioannis Tollis

Substantial advances in predictive, preventive and personalized (PPP) medicine are starting to emerge from computational simulations of complex networked models of metabolism ranging to the molecular level of detail. From the systems biology perspective of the digital patient, diseases are perturbations of biological networks through defective genes or environmental stimuli, and therapies are the interventions needed to restore these networks to their normal states. The Bioinformatics group at FORTH Heraklion is developing novel computational methods for identifying new parts of these networks both from genomic sequences and from metabolite time-series, and to generate meaningful visualizations of them.

by Vincent G. Duffy

The 'Digital Human Modeling and Perception-Based Safety Design' project is intended to minimize or reduce the need for physical prototyping in design. Researchers at Purdue University from across different colleges have the opportunity to work collaboratively on projects in this area through the Regenstrief Center for Healthcare Engineering and Discovery Park. The work has origins in automotive, aerospace and military vehicle design.

Next issue: July 2019
Special theme:
Digital Health
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