title: Comparing Alzheimer's and Parkinson's diseases networks using graph communities structure.
abstract: BACKGROUND
  Recent advances in large datasets analysis offer new insights to modern biology allowing system-level investigation of pathologies. Here we describe a novel computational method that exploits the ever-growing amount of "omics" data to shed light on Alzheimer's and Parkinson's diseases. Neurological disorders exhibit a huge number of molecular alterations due to a complex interplay between genetic and environmental factors. Classical reductionist approaches are focused on a few elements, providing a narrow overview of the etiopathogenic complexity of multifactorial diseases. On the other hand, high-throughput technologies allow the evaluation of many components of biological systems and their behaviors. Analysis of Parkinson's Disease (PD) and Alzheimer's Disease (AD) from a network perspective can highlight proteins or pathways common but differently represented that can be discriminating between the two pathological conditions, thus highlight similarities and differences.
  
  
  RESULTS
  In this work we propose a strategy that exploits network community structure identified with a state-of-the-art network community discovery algorithm called InfoMap, which takes advantage of information theory principles. We used two similarity measurements to quantify functional and topological similarities between the two pathologies. We built a Similarity Matrix to highlight similar communities and we analyzed statistically significant GO terms found in clustered areas of the matrix and in network communities. Our strategy allowed us to identify common known and unknown processes including DNA repair, RNA metabolism and glucose metabolism not detected with simple GO enrichment analysis. In particular, we were able to capture the connection between mitochondrial dysfunction and metabolism (glucose and glutamate/glutamine).
  
  
  CONCLUSIONS
  This approach allows the identification of communities present in both pathologies which highlight common biological processes. Conversely, the identification of communities without any counterpart can be used to investigate processes that are characteristic of only one of the two pathologies. In general, the same strategy can be applied to compare any pair of biological networks.
authors.affiliation: Institute of Systems Analysis and Computer Science, National Research Council of Italy, Via dei Taurini, 19, Roma, 00185, Italy. sinnefa@gmail.com.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. sinnefa@gmail.com.
authors.forename: Alberto
authors.initials: A
authors.lastname: Calderone
authors.affiliation: Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. m.formenti6@campus.unimib.it.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. m.formenti6@campus.unimib.it.
authors.forename: Matteo
authors.initials: M
authors.lastname: Formenti
authors.affiliation: Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. f.aprea@campus.unimib.it.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. f.aprea@campus.unimib.it.
authors.forename: Federica
authors.initials: F
authors.lastname: Aprea
authors.affiliation: Laboratory of Neuronal Networks, Department of Mental and Physical Health and Preventive Medicine, Second University of Naples, Naples, Italy, Via L. Armanni, 5, Napoli, 80138, Italy. michelepapasun@gmail.com.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. michelepapasun@gmail.com.
authors.forename: Michele
authors.initials: M
authors.lastname: Papa
authors.affiliation: Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. lilia.alberghina@unimib.it.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. lilia.alberghina@unimib.it.|NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, Piazza della Scienza, 4, Milano, 20126, Italy. lilia.alberghina@unimib.it.
authors.forename: Lilia
authors.initials: L
authors.lastname: Alberghina
authors.affiliation: Lab of Neuroscience "R. Levi-Montalcini", Dept. of Biotechnology and Biosciences, University of Milano-Bicocca, Milano, 20126, Italy. annamaria.colangelo@unimib.it.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. annamaria.colangelo@unimib.it.|NeuroMI Milan Center for Neuroscience, University of Milano-Bicocca, Piazza della Scienza, 4, Milano, 20126, Italy. annamaria.colangelo@unimib.it.
authors.forename: Anna Maria
authors.initials: AM
authors.lastname: Colangelo
authors.affiliation: Institute of Systems Analysis and Computer Science, National Research Council of Italy, Via dei Taurini, 19, Roma, 00185, Italy. paola.bertolazzi@iasi.cnr.it.|SYSBIO Centre of Systems Biology, University of Milano-Bicocca, Milano, 20126, Italy. paola.bertolazzi@iasi.cnr.it.
authors.forename: Paola
authors.initials: P
authors.lastname: Bertolazzi
mesh_terms: D000544:Alzheimer Disease / Q000235:genetics / Q000378:metabolism* / Q000473:pathology*; D003196:Computer Graphics; D004260:DNA Repair; D005947:Glucose / Q000378:metabolism; D006801:Humans; D008928:Mitochondria / Q000378:metabolism; D010300:Parkinson Disease / Q000235:genetics / Q000378:metabolism* / Q000473:pathology*; D012313:RNA / Q000378:metabolism; D015398:Signal Transduction; D049490:Systems Biology / Q000379:methods*
chemical_list: D012313:RNA; D005947:Glucose