@article {2012,
	title = {Decompositions of large-scale biological systems based on dynamical properties},
	journal = {Bioinformatics (Oxford, England). 2012 Jan; 28(1):76-83},
	number = {PMID:22072388;},
	year = {2012},
	publisher = {Oxford University Press},
	abstract = {MOTIVATION: Given a large-scale biological network represented as an influence graph, in this article we investigate possible decompositions of the network aimed at highlighting specific dynamical properties.\\r\\nRESULTS: The first decomposition we study consists in finding a maximal directed acyclic subgraph of the network, which dynamically corresponds to searching for a maximal open-loop subsystem of the given system. Another dynamical property investigated is strong monotonicity. We propose two methods to deal with this property, both aimed at decomposing the system into strongly monotone subsystems, but with different structural characteristics: one method tends to produce a single large strongly monotone component, while the other typically generates a set of smaller disjoint strongly monotone subsystems.\\r\\nAVAILABILITY: Original heuristics for the methods investigated are described in the article.},
	doi = {10.1093/bioinformatics/btr620},
	url = {http://hdl.handle.net/1963/5226},
	author = {Nicola Soranzo and Fahimeh Ramezani and Giovanni Iacono and Claudio Altafini}
}
@article {2009,
	title = {ERNEST: a toolbox for chemical reaction network theory},
	journal = {Bioinformatics 25 (2009) 2853-2854},
	year = {2009},
	publisher = {Oxford University Press},
	abstract = {Summary: ERNEST Reaction Network Equilibria Study Toolbox is a MATLAB package which, by checking various different criteria on the structure of a chemical reaction network, can exclude the multistationarity of the corresponding reaction system. The results obtained are independent of the rate constants of the reactions, and can be used for model discrimination.\\nAvailability and Implementation: The software, implemented in MATLAB, is available under the GNU GPL free software license from http://people.sissa.it/~altafini/papers/SoAl09/. It requires the MATLAB Optimization Toolbox.},
	doi = {10.1093/bioinformatics/btp513},
	url = {http://hdl.handle.net/1963/3826},
	author = {Nicola Soranzo and Claudio Altafini}
}
@article {2009,
	title = {mRNA stability and the unfolding of gene expression in the long-period yeast metabolic cycle},
	journal = {BMC Systems Biology (2009) 3:18},
	year = {2009},
	publisher = {BioMed Central},
	abstract = {Background: In yeast, genome-wide periodic patterns associated with energy-metabolic oscillations have been shown recently for both short (approx. 40 min) and long (approx. 300 min) periods.\\nResults: The dynamical regulation due to mRNA stability is found to be an important aspect of the genome-wide coordination of the long-period yeast metabolic cycle. It is shown that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates.\\nConclusion: The cascade of events occurring during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses/stimuli.},
	doi = {10.1186/1752-0509-3-18},
	url = {http://hdl.handle.net/1963/3630},
	author = {Nicola Soranzo and Mattia Zampieri and Lorenzo Farina and Claudio Altafini}
}
@article {2008,
	title = {Discerning static and causal interactions in genome-wide reverse engineering problems},
	journal = {Bioinformatics 24 (2008) 1510-1515},
	year = {2008},
	abstract = {Background. In the past years devicing methods for discovering gene regulatory mechanisms at a genome-wide level has become a fundamental topic in the field of system biology. The aim is to infer gene-gene interactions in a more sophisticated and reliable way through the continuously improvement of reverse engineering algorithms exploiting microarray technologies. Motivation. This work is inspired by the several studies suggesting that co-expression is mostly related to \\\"static\\\" stable binding relationships, like belonging to the same protein complex, rather than other types of interactions more of a \\\"causal\\\" and transient nature (metabolic pathway or transcription factor-binding site interaction). Discerning static relationships from causal ones on the basis of their characteristic regulatory structures and in particular identifing \\\"dense modules\\\" with protein complex, and \\\"sparse modules\\\" with causal interactions such as those between transcription factor and corresponding binding site, the performances of different network inference algorithms in artificial and real networks (derived from E.coli and S.cerevisiae) can be tested and compared. Results. Our study shows that methods that try to prune indirect interactions from the inferred gene networks may fail to retrieve genes co-participating in a protein complex. On the other hand they are more robust in the identification of transcription factor-binding sites dependences when multiple transcription factors regulate the expression of the same gene. In the end we confirm the stronger co-expression regarding genes belonging to a protein complex than transcription factor-binding site, according, also, to the effect of multiple transcription factors and a low expression variance.},
	doi = {10.1093/bioinformatics/btn220},
	url = {http://hdl.handle.net/1963/2757},
	author = {Mattia Zampieri and Nicola Soranzo and Claudio Altafini}
}
@article {2008,
	title = {Origin of Co-Expression Patterns in E.coli and S.cerevisiae Emerging from Reverse Engineering Algorithms},
	journal = {PLoS ONE 3 (2008) e2981},
	year = {2008},
	abstract = {Background: The concept of reverse engineering a gene network, i.e., of inferring a genome-wide graph of putative genegene interactions from compendia of high throughput microarray data has been extensively used in the last few years to deduce/integrate/validate various types of \\\"physical\\\" networks of interactions among genes or gene products. Results: This paper gives a comprehensive overview of which of these networks emerge significantly when reverse engineering large collections of gene expression data for two model organisms, E.coli and S.cerevisiae, without any prior information. For the first organism the pattern of co-expression is shown to reflect in fine detail both the operonal structure of the DNA and the regulatory effects exerted by the gene products when co-participating in a protein complex. For the second organism we find that direct transcriptional control (e.g., transcription factor-binding site interactions) has little statistical significance in comparison to the other regulatory mechanisms (such as co-sharing a protein complex, colocalization on a metabolic pathway or compartment), which are however resolved at a lower level of detail than in E.coli. Conclusion: The gene co-expression patterns deduced from compendia of profiling experiments tend to unveil functional categories that are mainly associated to stable bindings rather than transient interactions. The inference power of this systematic analysis is substantially reduced when passing from E.coli to S.cerevisiae. This extensive analysis provides a way to describe the different complexity between the two organisms and discusses the critical limitations affecting this type of methodologies.},
	doi = {10.1371/journal.pone.0002981},
	url = {http://hdl.handle.net/1963/2722},
	author = {Mattia Zampieri and Nicola Soranzo and Daniele Bianchini and Claudio Altafini}
}
@article {2007,
	title = {Comparing association network algorithms for reverse engineering of large scale gene regulatory networks: synthetic vs real data},
	journal = {Bioinformatics 23 (2007) 1640-1647},
	year = {2007},
	abstract = {Motivation: Inferring a gene regulatory network exclusively from microarray expression profiles is a difficult but important task. The aim of this work is to compare the predictive power of some of the most popular algorithms in different conditions (like data taken at equilibrium or time courses) and on both synthetic and real microarray data. We are in particular interested in comparing similarity measures both of linear type (like correlations and partial correlations) and of nonlinear type (mutual information and conditional mutual information), and in investigating the underdetermined case (less samples than genes). Results: In our simulations we see that all network inference algorithms obtain better performances from data produced with \\\"structural\\\" perturbations, like gene knockouts at steady state, than with any dynamical perturbation. The predictive power of all algorithms is confirmed on a reverse engineering problem from E. coli gene profiling data: the edges of the \\\"physical\\\" network of transcription factor-binding sites are significantly overrepresented among the highest weighting edges of the graph that we infer directly from the data without any structure supervision. Comparing synthetic and in vivo data on the same network graph allows us to give an indication of how much more complex a real transcriptional regulation program is with respect to an artificial model. Availability: Software and supplementary material are freely available at the URL http://people.sissa.it/~altafini/papers/SoBiAl07/},
	doi = {10.1093/bioinformatics/btm163},
	url = {http://hdl.handle.net/1963/2028},
	author = {Nicola Soranzo and Ginestra Bianconi and Claudio Altafini}
}
