- Preprocessing and analysis of static and dynamic RNA-seq and microarray expression data.
- Preprocessing of SNP arrays and exome sequencing data and multivariate analysis of genetic variations
- Optimal primer design, pre-processing and analysis of 16S sequencing data and reverse engineering of microbial networks.
- Methods for the preprocessing and quantification of mass spectrometry data and for the modeling of protein turnover from SILAC data.
- Deterministic and stochastic modelling of transcriptional networks and signaling pathways, reverse engineering of transcriptional networks and miRNA-mRNA regulatory motifs, and integration of genetic phenotypic and environmental risk factors via Systems Genetics approaches
- Advanced data mining and machine learning methods for robust biomarker discovery, predictive modeling and clustering from microarrays and next generation sequencing data
This research area deals with the use of mathematical modeling techniques to develop and validate mathematical models able to either simulate the behavior of complex, dynamical biological systems, or to estimate key parameters usable to quantify physiological processes. Depending on the aim and the available experimental data, the models can be whole-body, organ/tissues, cellular or multiscale, formulated as ordinary or partial differential equations, deterministic or stochastics, minimal or large scale.
- Development of control algorithms for automatic insulin delivery in type 1 diabetes (Artificial Pancreas): personalization, run2run adaptation, fault detection and fault compensation for safe unsupervised AP use.
- In silico validation and clinical testing of AP algorithms.
- Parameter estimation techniques at both the individual and population level (nonlinear-mixed effects)
- Deconvolution techniques for biomedical signals analysis
- Development and validation of “Minimal" models of the glucose system to measure signals and parameters (such insulin sensitivity, beta-cell function, hepatic insulin extraction, hepatic glucose production, glucose utilization and rate of appearance of ingested glucose) from intravenous/oral tolerance tests.
- Development and validation of "Maximal" models of the glucose-insulin system for in silico simulation.
- Development and validation of Models of the glucose-insulin system for use in control applications (artificial pancreas).
Neuroimaging is a crucial method of investigation for studying the human brain in healthy and impaired populations. Cutting edge improvements in imaging are, for instance, integral to intervention and prognostication in the neuro-oncology field. Our research activities include development and application of statistical methods for modeling and integrate the complexity of the physiological information provided from MRI, PET and electrocortical data.
- Confocal microendoscopy in Barrett’s esophagus: methods for the evaluation of the cellular architecture and of the functional and morphological alterations of the esophageal mucosa. Identification and classification of mucosal alterations (gastric metaplasia, intestinal metaplasia, high grade displasia, neoplasia) to provide a target for biopsies and quantitative markers for cancer surveillance.
- Small joint morphology: detection of markers from 2D and 3D images of the small joints. Automatic assessment of synovial shape and dimension
- Synovial perfusion: perfusion identification and modelling of the local perfusion within the synovia. Detection and characterization of perfusion patterns linked to different underlying diseases or substrates. Develop methods for an accurate differential diagnosis of rheumatic diseases based on the perfusion patterns and imaging biomarkers.
- Functional magnetic resonance imaging (fMRI): functional connectivity, dynamic causal modelling to study the effective connectivity during tasks or in resting state; assessment of cerebral hemodynamic impairment thought fMRI data analysis, methods to integrate EEG & fMRI.
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