The PhD course in Innovative Biomedical Technologies in Clinical Medicine is designed to carry out a training program in translational medicine. To this end, particular attention is paid not only to advanced biotechnologies (like NGS), but also to the ability to manage and analyze large amount of data (Big Data Analysis) through innovative bioinformatics and computer science tools, which take into account the overall complexity of diseases (Network Medicine). This is crucial for complex diseases that are determined not only by specific molecular alterations but also by perturbations extended to whole biological systems, both at the individual and the population level. On the other hand, an effective use of these technologies requires adequate knowledge and familiarity with clinical models, which can allow foreseeing the application of findings to the clinical setting. This requires the creation of a new profile of researcher with specific skills not only for using innovative technologies in the investigation of molecular mechanisms of diseases but also for applying the results of this investigation to the diagnosis, prognosis and therapy of specific clinical conditions.

Therefore, the main purposes of the doctorate are:
a) to respond to increased demand for new models of teaching aimed at preparing to the so-called translational medicine;
b) to achieve a better integration between basic and clinical knowledge as well as bioinformatics, network science and computational biology;
c) to create new professionals who are expert in translational and network medicine, able to combine the expertise in the use of advanced information and communication technologies with the clinical skills necessary to identify relevant scientific issues as well as potential applications of new findings;

The specific educational objectives of the PhD program are the following:
a) to learn epidemiological and statistical elements of genetic susceptibility in multifactorial diseases (complex traits);
b) to learn the most advanced methodologies to study genome and its variability (genomic analysis of SNPs and CNV; techniques for DNA methylation analysis; microbial study; next-generation sequencing (NGS) techniques for genome and/or exome analysis] and bioinformatics methodologies underlying the evaluation of genetic research results [design and analysis of genome-wide association studies (GWAS); design and analysis of NGS studies: panel design for molecular characterization of specific diseases, experimental design for whole genome/exome sequencing, sequence alignment, variant calling, variant prioritization, in silico prediction of effects of variants];
c) to learn the methods of studying the epigenetic modifications of the chromatin and techniques of DNA methylation analysis;
d) to learn the methods for gene expression analysis (mRNA, miRNA and lncRNA) [arrays; digital-PCR; RNA-seq; miRNA-seq];
e) to learn cell culture techniques, including the isolation, characterization and culturing of progenitor cells, stem cells and iPS; organoid cultures, innovative culture system which recapitulate the in vivo architecture, functionality, and genetic signature of original tissues;
f) to learn techniques for in vitro and ex vivo cells of innate and adaptive immunity;
g) to learn the main techniques for genetic manipulation and conditioning [transgenic; knock-out; Knock-in; CRISPR–Cas9 technology];
h) to learn proteomic and metabolomic techniques for identifying disease predictors;
i) to learn the principles and applications of network medicine [integration of different datasets; identification of modules, hubs and nodes; generation of interactome models to infer and assess function, to understand mechanisms, and to prioritize candidates for further investigation; relationship between molecular and phenotypic networks to understand the determinants of disease expression];
j) to learn and apply innovative imaging techniques;
k) to learn the principles of pharmacology with particular reference to pharmacogenetics
l) to apprehend the principles and applications of BigData Science [generation and manipulation of massive datasets from clinical and/or genomic data, design of big data algorithms];
m) to learn the basics of clinical trials (experimental design, statistical estimation, statistical tests and decision rules, controlled trials, randomization methods, double blind experiments, etc.), experimental data management as well as publication and interpretation of results;
n) to learn the basics of in silico clinical trials (development of computer simulation models in experimental physiology, pathophysiology, pharmacokinetics and pharmacodynamics; development of computer simulation in the evaluation of a medicinal product, medical device, or a personalized medical treatment);
o) to learn the principles of experimentation on animals including elements of biomedical ethics
p) to understand the business and industrial aspects related to advanced biotechnology
q) to design and implement predictive algorithms (machine learning) to analyze and extract useful knowledge from the large variety and dimension of available medical data, both in the form of networks (biological and social), sequences (patients’ trajectories), and unstructured data (text and images, such as in patients’ records and social data).


Le attività presentate in questa pagina sono finanziate totalmente, o in parte, dai fondi del Piano nazionale di ripresa e resilienza.
L'elenco e le finalità delle attività intraprese dalla Sapienza sono descritte nella pagina dedicata Sapienza per il Pnrr.

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