icona researchResearch


  • Computational Biology
  • High-throughput technologies: develop of algorithms to analyze Next-Generation Sequencing and Microarray;
  • Reverse engineering of cellular systems;
  • Systems Biology;
  • Application of mathematic approaches to oncology;

Deep sequencing algorithms
I am interested in the definition of new algorithms and workflow with different purposes: deconvolution for assembly of new genomes, mapping, analysis of RNA-seq, microRNA, ChIP-seq, structural genomics variations detection etc. The results deriving from the application of these algorithms was applied in the genome sequencing project of Hordeum vulgare.

Application of mathematical approaches to oncology
I work on the study of cancer through the application of mathematical models. In particular, I am focus on a specific set of tumors whose growth and progression are influenced by a small subpopulation of cancer cells, known as Cancer Stem Cells (CSCs). I am interested in the study of tumor growth through a multi-level modeling approach considering the dynamics occurring among the different subpopulations of cells, and regulatory networks describing key cellular events in tumorigenesis. Moreover I investigate the application of model fitting techniques of cancer volume data and the integration of these data with the genomic and transcriptomic knowledge in the study of clonal and mutational evolution of cancer.

Systems Biology: Quantitative analysis and Reverse engineering of cellular systems
I work on the development of a methodology that allows to perform qualitative and quantitative analysis of a selected portion of signal transduction events involved in angiogenesis process.
This approach is based on the use of Petri Nets (PN) and in some cases their variant called Stochastic Petri Nets (SPNs), that provide a detailed representation of the biological model. Moreover a simplification process is applied in order to obtain a model where the number of parameters is reduced and the analysis results tractable from the computational point of view.
Moreover I have worked on the development of a object-oriented methodology, based on the Mean Field Analysis, that offers an intuitive way to describe the behavior of biological systems characterized by a huge number of interacting objects.

Microarray analysis and mining
A main issue in microarray transcription profiling is data analysis and mining. When microarrays became a methodology of general use, considerable effort was made to produce algorithms and methods for the identification of differentially expressed genes. In this topic I worked on both the develop of graphical interface in R for data analysis of one channel microarrays (i.e. Affymetrix and Applied Biosystems and on the definition of analysis work-flow for GeneChip Exon. I mined genome-wide transcription data to search putative tumor associated antigen in mammary cancer. For what concern the data mining I also worked on a constraints co-clustering techniques specifically applied on gene expression data, in this context about the definition of constraint we re-written the Gene Ontology (RGO) aiming at obtaining a more compact and informative ontology, leading to closer biological regulated GO terms. The RGO will help researcher to easily identify the genes belonging to the same network module without the need of additional data.