R & D Activities

The research directions of the Computational BioMedicine Laboratory are:

Biomedical Ιnformatics: The Laboratory is focusing on various computational aspects of biomedical informatics through the following independent but highly correlated and interdisciplinary activities, such as (a) ontology based integration and analysis of genomic and medical information for health applications; (b) parallel and distributed computing approaches to demanding molecular-biomedical applications; (c) analysis, simulation and modeling of complex biomedical processes, (d) design and development of novel data mining methods, algorithms, tools and decision support systems .

Personal Health Systems and Pervasive Mobile Monitoring: Our group is strongly involved in the development of technologies for personal health systems focusing on the development of innovative personalized health services to empower individuals in well-being and disease prevention, and chronic disease management. Personal Health Systems covers well-being, prevention of specific diseases or follow-up and management of existing chronic diseases enhance patient empowerment and self-care management. These technologies have the personal health record as the underlying architecture embracing remote monitoring for sensor data collection, functional and semantic interoperability, extraction of relevant features for detection of alarming and/or alerting subsystems, personalized feedback and recommendation services for the patient or informal caregiver. Our activities include smart ubiquitous intelligent health systems and un-obstructive ubiquitous acquisition, transmission and interpretation of different bio-signals from fixed or mobile locations. We focus on the creation of innovative multi-purpose heterogeneous networking infrastructures providing in-transit persistent information storage for personal health systems’ and monitoring environment services able to overcome current network instabilities and incompatibilities.

Computational Medicine: During the last decade our group is developing patient-specific multiscale computer based (in silico) models aiming towards a better understanding of the physiology and the pathology of human organs, with a special focus on one of the major concerns in clinical practice, Cancer. Our aim is to achieve the fastest possible transform of scientific discoveries arising from diverse scientific fields such as laboratory, clinical or population studies and in silico predictive models of various disease staging, into clinical applications in order to reduce their incidence, morbidity and mortality. Our computational modeling approaches mainly focus on developing sophisticated multiscale mathematical models of cancer for testing different therapeutic interventions. These approaches, along with incorporation of physiologically-based in silico clinical trials tools, aim to provide data regarding the optimization of intended therapeutic scheme for each specific patient.

Medical Image Analysis: Medical Image analysis and process of data require the estimation of pharmacokinetic properties of tracers used in clinical practice. To this respect, following today’s well established use of in-silico clinical trials platforms and tools of physiologically-based models we try to combine image analysis techniques with physiologically-based pharmacokinetic (PB/PK) approaches for the in-silico evaluation of tracer kinetics used in medical imaging analysis in population level with a focus on Gd-based contrast agents used in DCE-MRI. The DCE-MRI tools we develop aims to quantify the leakage of the contrast agent, from the neovascularization into the tissue, by quantitative analysis of the data through the use of PK models. We have implemented standard models e.g. for estimating vessel permeability (Ktrans) but the group is also implementing a number of novel PK models for better addressing the underlying cancer biological processes. We also develop a number of image analysis technologies for quantifying diffusion with Apparent Diffusion Coefficient (ADC), a widely used quantitative marker for cancer assessment and tumor microenvironment identification. Our group also collaborates closely with the University Hospital of Heraklion (Medical Imaging Department) and other European clinical organizations for assisting the clinical evaluation and translation of these tools and biomarkers to the clinical setting.

Computational Neuroscience: Our R&D priorities in the particularly challenging problem of evaluating brain function are focusing on neural dynamics and synchronization phenomena. Such phenomena have been increasingly recognized to be an important mechanism by which specialized cortical and sub-cortical regions integrate their activity to form distributed neuronal assemblies that function in a cooperative manner. Synchronous oscillations of certain types of such assemblies in different frequency bands relate to different perceptual, motor or cognitive states and may be indicative of a wider range of cognitive functions or brain pathologies. The objective is to elucidate the longstanding “obscured” brain dynamics and find ways to improve and broaden the currently clinically used methodologies, as well as to improve the quality of life for several diseased people.

Translational Bioinformatics & Open Bioinformatics Science: 

    Analysis of heterogeneous biomedical –omic data (Differential Gene Expression, Pathway Analysis, Machine Leaning methodologies & tools)
    Bioinformatics in the service of Precision Medicine (Gene Variants Annotation and Prioritization; Analysis of structural protein data)
    Bioinformatics in the service of Open & Reproducible Bioinformatics Science (Open & Collaborative Bioinformatics Workflow Platforms)
    SARS-CoV-2 infection molecular landscape & Prognostic Modelling

Computational Evolutionary Biology: The group is focusing on computational and theoretical population genetics and evolutionary biology. Its primary interest is the inference of adaptive forces and non-adaptive processes that govern the evolution of natural populations. To infer the evolutionary processes, the group uses a combination of mathematical and computational approaches and analysis of real data. The main areas of research are the following: a) analysis of modern and ancient human genetic data to understand the recent human evolution; b) development of statistical methods and implementation of software to localize positive selection in the genomes; c) application of the evolutionary methodologies on other evolvable systems (e.g., language evolution); d) development of simulation methods to obtain insights into novel, complex and realistic evolutionary scenarios and e) analysis of fast-evolving genomes, such as viral genomes. 

Hybrid Molecular Imaging Unit (HMIU): The goal of the Hybrid Molecular Imaging Unit (HMIU) is to develop targeted imaging techniques which facilitate the development of novel, precision medicine, treatment strategies for a wide spectrum of disease entities, such as malignancies, cardiovascular diseases, neurological, dermatological, musculoskeletal, and rheumatological disorders, as well as multisystem endocrinopathies. Our research efforts are focused on the development and translation of novel in-vivo molecular imaging agents, which enable early detection, accurate assessment of disease status and efficient monitoring of treatment response. By employing cutting-edge imaging technology together with advanced radiochemistry applications HMIU aims to make major contributions in the field of biomedical research.