- Role: PTA
- Assignments:Tecnico di Area Scientifica
- Office:U3- 4° Piano - Stanza 4049
- Telephone number:02 6448-3381
Application and implementation of computational analysis and techniques, in order to study the information content and information flow in biological systems and processes, trying to convert sequence information into functional information for the gene products coded by analyzed sequences.
Analysis of Interaction Between Protein and Drug Candidates with Biacore System and analysis of Enzymatic Kinetics
The in vitro binding/activity data deriving from different experiments are analysed. The statistic analysis of data and the comparison of binding affinities and activities deriving from different experiments are used to rank different chemical structures according to their affinity. Thermodynamic (KD) and kinetic (Kon/Koff) binding parameters are calculated from experiments. These quantitative data are compared with in vivo data on compound potency.
Tools, Systems and Instruments:
Biacore System: Interaction between Proteins and other Molecules, including Small Molecule Drug Candidates
OriginLab: Enzymatic Kinetic
R-Bioconductor (Data Quality Control, Normalization, Filtering, Cluster Analysis, Differential Expression Gene Selection, Cluster of DEGs, Gene Ontology Analysis, KEGG Functional Enrichment, KEGG Pathway Maps, Database Packages)
- Partek Genomic Suite: Microarray Analysis and Next Generation Sequencing
- NeuroExplorer System: Data Analysis for Neurophysiology, Spectral Analysis of Spike and Continuous Data, Custom Analysis and Batch Mode Program with Perl Scripting Language
- SNP-Analyzer: Single Nucleotide Polymorphism Analysis
- BRB Tool: Microarray Analysis
- DMET: Analysis of Drug Metabolism Enzymes and Transporters
- NCBI Tools
- Genomic BLAST Database
- R, R-Studio (with Bioinformatic and Statistic Packages)
Biochemistry and Molecular Biology Teaching Laboratory
Managing, maintenance and support
TOLLerant : Toll-Like Receptor 4 activation and function in diseases: an integrated chemical-biology approach
The TOLLerant project aims to gain information on molecular aspects of TLR4 activation and signalling by using synthetic and natural compounds and nanoparticles that interact selectively with some components (mainly MD-2 and CD14) of the TRL4 recognition system.
TLR4 is an emerging molecular target related to an impressively broad spectrum of modern day disorders still lacking specific pharmacological treatment. These include autoimmune disorders, chronic inflammations, allergies, asthma, infectious and CNS diseases, and cancer.
The short-term scientific objective is to develop novel, non-toxic, synthetic and natural TLR4 modulators (agonists or antagonists) and to assess their therapeutic potential on animal models of TLR4-related acute and chronic pathologies. The long-term scientific objective is to develop a new generation of innovative, TLR4-based therapeutics, to be used as vaccine adjuvants, anti-sepsis agents, and anti-inflammatory agents to treat chronic inflammations (allergy, asthma).
Microarray Data Analysis
Analysis of the gene expression profile of the cells in a specific state.
Throught this method we identify which genes are active and which are inactive in different cell types helping us to understand both how these cells function normally and how they are affected when various genes do not perform properly.
It measures relative changes in levels of specific mRNAs to provide information about what’s going on in the cells from which the mRNA came.
So, this method helps us to discover, for example, the genetic pathways that are changed and disrupted in a wide range of diseases, from cancer to alzheimer, multiple sclerosis ecc. Whole genome expression analysis is helping to stratify diseases states, predict patient outcome and make better therapeutic choices.
Its aim also is to classify different types of cancers based on the patterns of gene activity in the tumor cells, helping to design treatment strategies targeted directly to each specific type of cancer. Additionally, by examining the differences in gene activity between untreated and treated tumor cells.
Statistical theory applied to the exploration of biological data.
Modeling Methods in System Biology
An integrated approach of molecular and computational analysis to further understand cellular functions induced by the dynamic interactions of a large number of gene products.
Gene Regulatory Network and Reverse Engineering
The system-level analysis of functional properties of living cells viewed as a network of interacting molecules.
Conceptualization and implementation of a tool for reverse engineering and dynamic simulation of Gene Regulatory Network (GRNGen).
Identification of genes involved in drug metabolism studying their SNPs as predictors of drug responses
Human genome sequence variation in the form of Single Nucleotide Polymorphisms [SNPs] (as well as more complex structural variation such as insertions, duplications and deletions) underlies each individual’s response to drugs and thus the likelihood of experiencing an adverse drug reaction.
The ongoing challenge of the field of pharmacogenetics is to further understand the relationship between genetic variation and differential drug response (i.e. drug absorption, distribution, metabolism and elimination, shortly known as ADME ) with the overarching goal being that this will lead to improvements in both the safety and efficacy of drugs.
“Pharmacogenetics” is the study of specific SNPs at specific genes with known functions that could be linked to drug response.
The goal of the study is to develop a list of genes and genetic biomarkers involved in ADME properties of drugs, to identify predictors of pharmacokinetic variability that could impact drug safety and efficacy in the current drug development process.
In order to to further understand the relationship between genetic variation and differential drug response (i.e. drug absorption, distribution, metabolism and elimination, shortly known as ADME ) with the overarching goal being that this will lead to improvements in both the safety and efficacy of drugs.
Data Analysis for Neurophysiology
Analysis of populations of neurons, analysis of continuously recorded signal, spike train analysis, burst analysis, cumulative neuronal activities