In this thesis we introduce, define and quantitatively assess the stability of the algorithms for the reconstruction of networks. We will focus on theory, development and implementation of operative procedures and algorithms for the assessment of stability in complex networks for biological systems, with gene regulatory networks as the key example. A major issue affecting network inference is i...

The rapid development and applications of high throughput measurement techniques bring the biological sciences into a 'big data' era. The vast available data for enzyme and metabolite concentrations, fluxes, and kinetics under normal or perturbed conditions in biological networks provide unprecedented opportunities to understand the cell functions. On the other hand, it brings new challenges of...

The explosion in the production rate of expression level data has highlighted the need for automated techniques that help scientists analyze, understand, and cluster (sequence) the enormous amount of data that is being produced. Example of such problems are analyzing gene expression level data produced by microarray technology on the genomic scale, sequencing genes on the genomic scale, sequenc...

UNLABELLED The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation...

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensiv...

Research in systems biology has made available large amounts of data about interactions among cell building blocks (e.g., proteins, genes). To properly look up these data and mine useful information, the design and development of automatic tools has become crucial. These tools leverage Biological Networks as a formal model to encode molecular interactions. Biological networks can be fed as inpu...

There has been much progress in recent years towards building larger and larger computational models for biochemical networks, driven by advances both in high throughput data techniques, and in computational modeling and simulation. Such models are often given as unstructured lists of species and interactions between them, making it very difficult to understand the logicome of the network, i.e....

This paper discusses an area of application that seems somewhat remote from the other application areas of the symposia. The intension is not to dwell on biological and experimental issues, but briefly to introduce the biological background and setting and then move on to discuss aspects of statistical methodology that frequently are applied in the biosciences and elsewhere: Markov chain Monte ...

MOTIVATION Analogous to biological sequence comparison, comparing cellular networks is an important problem that could provide insight into biological understanding and therapeutics. For technical reasons, comparing large networks is computationally infeasible, and thus heuristics, such as the degree distribution, clustering coefficient, diameter, and relative graphlet frequency distribution ha...

MOTIVATION Many biological networks, including transcriptional regulation, metabolism, and the absorbance spectra of metabolite mixtures, can be represented in a bipartite fashion. Key to understanding these bipartite networks are the network architecture and governing source signals. Such information is often implicitly imbedded in the data. Here we develop a technique, network component mappi...