My research consists in developing mechanistic or phenomenological models across different biological scales in health and disease conditions; neural networks 'build' internal (non explainable) models of the system under study. An important difference between today engineering systems and biological systems is that Nature is programmed for self-assembly (i.e. not like IKEA products, more like self-folding origami). Large sets of reactions involving molecular complexes of DNA, RNA, proteins, lipids, sugars etc self assemble into different types of devices (mechanisms such rotors, motors, control with mechanical and or electric properties ) that assemble in cells which are the unit of biology. 

The role of Bioinformatics is (mostly) to identify the list of parts and their variants. The parts have co-evolved through mutation and selection events (Nothing in Biology Makes Sense Except in the Light of Evolution - Theodosius Dobzhansky). Nowadays, Bioinformatics is mostly based on algorithms, network science and machine learning. The role of systems biology is to identify the rules leading to the multiscale self assembly and produce (dynamical systems or other) models (Biology is more theoretical than physics - Bob Lefkowitz's 2012 Nobel lecture) useful for medicine.

Computational Biology (aka theoretical biology ?) is (in my opinion) a more general umbrella. Medicine is an evolving interdisciplinary field (it requires multi-teams work, their composition depending on the nature of the diagnosed disease and comorbidities). Computer scientists will play a central and multi varied role in computational medicine, particularly addressing challenges of 1) data integration: genomics and various omics for different tissues, space and time - such as chromatine, epigenetic, trancriptomic, proteomic and signalling networks, various microbiota association with different regions of the body- epidemiological, electronic health records, radiomics data, etc and 2) multiscale modelling of the highly nonlinear disease trajectory -in the Waddington-1968 sense- and taking into account biomarkers group remodulation and comorbidity attractors.

Bioinformatics algorithms (undergraduate course)

Graph Neural Networks (Advanced Computing MPhil Course).

AI and Biodiversity (AI and Environmental Risk MPhil course)

---

Books

 

---

My Impact on Society

I am active in science communication (various articles for magazines, for instance the italian edition of Scientific American), and engage often with the media and in public science events (science cafe' in various cities, participation at European Researchers' Night, Coding for kids schools, education in computer science such as CS4HS in 2010 (Education in Computer Science for High School Educators) at Carnegie Mellon University, etc). This is very important as a scientist is fundamentally a visionary who needs to interact with society.

I have been involved for several years in the remarkable EUROPEAN COMMUNITY CAPSSI (Collective Awareness Platforms for Sustainability and Social Innovation) and NGI (Next Generation Internet) Initiatives that have pioneered new models to create awareness of emerging sustainability challenges (CAPSSI) and inclusivity, openness, protection of data (NGI).

Examples of past research projects

EU Grant: GO-DS21, Gene Overdosage and Comorbidities During the Early Lifetime in Down Syndrome

EU Grant: PROPAG-AGEING - The continuum between healthy ageing and idiopathic Parkinson Disease within a propagation perspective of inflammation and damage: the search for new diagnostic, prognostic and therapeutic targets.

MRC Grant: CLIMB (R. Cardinal) Clinical Informatics for Mind and Brain health.
EU grant: MIMOmics (Methods for Integrated analysis of multiple Omics datasets).
EU grant: Epihealthnet: To improve the health of the human population by understanding mechanisms and pathways in early development, with special emphasis on epigenetic changes and developmentally relevant metabolic signals, which create biological variation and have a long term effect on the health of individuals.
EU Grant: MISSION-T2D aims at developing and validating an integrated, multilevel patient-specific model for the simulation and prediction of metabolic and inflammatory processes in the onset and progress of the type 2 diabetes (T2D).

NERC (Natural Environment Research council) DREAM (Data, Risk and Environmental Analytical Methods): The CDT research underway seeks to utilise emerging technologies, techniques and tools, to more accurately monitor the environment, enabling cutting edge research.
EU Grant Metable:-Advanced bioinformatics for genome and metagenome analyses with discovery of novel biocatalysts from extremophiles: implications for improving industrial bioprocesses.
Socialnets: Social networking for pervasive adaptation

---