Alexandru Floares

RODES - a class of algorithms for reverse engineering drug gene regulatory networks

Mathematical modeling is essential for understanding and controlling gene networks by drugs or genes replacements. Various formalisms, such as Bayesian networks, Boolean networks, differential equation models, qualitative differential equations, stochastic equations, and rule-based systems, have been used. The ordinary differential equations (ODE) approach tries to elucidate a deeper understanding of the exact nature of the regulatory circuits and their regulation mechanisms. RODES (Reversing Ordinary Differential Equations Systems) algorithms decouple the systems of differential equations, reducing the problem to that of reverse engineering individual algebraic equations. It automatically identifies the structure of accurate ODE systems models of gene regulatory network and drug gene regulatory network, estimate their parameters and the biochemical and pharmacological mechanisms involved. RODES algorithm reduces the complexity of the problem, and the execution time, due to the fact that for evaluating the fitness function is not necessary to integrate the ODE system. It is also able to deal with the common situations of information as variables missing from data. Applied to drug gene networks the neural network version of RODES algorithm enable and automate the reconstruction of the time-series of the transcription factors, microRNAs, or drug related compounds which are usually missing in microarray experiments.

Mario Cannataro

Management and analysis of protein-to-Protein Interaction (PPI) data

The tutorial describes main aspects of Interactomics starting from technologies for data generation, databases for data storage, standards for data modelling, and methods and techniques for data analysis and knowledge extraction.

Interactomics is a new discipline in the “omics” world that focuses on the modeling, storage and retrieval of protein-to-protein interactions (PPI), as well as on algorithms for analysing and predicting interactions.
Different protein functions are performed when proteins interact each others. Interactions may involve two or more proteins and be differently stable along the time, e.g. by forming a protein complex.
Wet lab technologies allow both to find binary interactions (I.e. involving only two proteins) as well as multiple interactions (e.g. a protein complex).
PPIs are often stored in specialized databases where each binary interaction is represented by a couple of interacting proteins (Pi, Pj).
The set of all protein-to-protein interactions happening in an organism is represented by a graph said protein-to-protein interaction network (PIN). The nodes of PINs, i.e. the proteins, represent biological entities, while the edges represent the interactions among them.
The set of all interactions occurring in an organism, i.e. its PIN, is obtained by extracting all interaction (Pi, Pj) contained in a given PPI database, i.e. the edges, and by building the related graph.
Thus, from the computer side point of view, Interactomics regards the generation of PPI data, their storage and querying through PPI databases and finally the analysis of the graphs representing PINs.

The tutorial discusses technologies, standards and databases for generating, representing and storing PPI data. It also describes main algorithms and tools for the analysis, comparison and knowledge extraction from PINs, Moreover, some case studies and applications of PINs are also discussed.

CONTENTS:
•    PPI Data, Databases and Networks,
•    Algorithms and  Tools for the Analysis of PPI Networks (motif extraction, networks alignment),
•    Case Studies