Dates: from March 1, 2011 to Feb. 28, 2015

Funder: FP7 (European Union)

Project id: FP7-PEOPLE-2010-RG 277166

Total Funding: 100,000€

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Cells, ecosystems and economies are examples of complex systems. In complex systems, individual components interact with each other, usually in nonlinear ways, giving rise to complex networks of interactions that are neither totally regular nor totally random. Partly because of the interactions themselves and partly because of the interaction topology, complex systems cannot be properly understood by just analyzing their constituent parts, which poses important challenges from both a fundamental perspective and an "application" perspective. The structure of the network of interactions was traditionally ignored and approximated by one of two limiting cases: a regular low-dimensional lattice or a completely random uncorrelated graph. It wasn't until recently that the scientific community, spearheaded by physicists, started to look for universality on the statistical description and classification of networks. Although significant progress has been made since then, we are still far from the ultimate goal of developing a general theory describing the impact of network structure on the dynamics of complex systems. The general aims of the project are: (i) to develop a general theoretical framework, based on the mesoscopic structure of networks, to understand the interplay between network structure and system dynamics; and (ii) to apply this framework to biological and socio-economic systems of interest.

Publications

• iMet: A network-based computational tool to assist in the annotation of metabolites from tandem mass spectra - Anal. Chem. 89 (6) , 3474 -3482 (2017).
• Inferring propagation paths for sparsely observed perturbations on complex networks - Sci. Adv. 2 , e1501638 (2016).
• Multilayer stochastic block models reveal the multilayer structure of complex networks - Phys. Rev. X 6 , 011036 (2016).
• Long-term evolution of email networks: statistical regularities, predictability and stability of social behaviors - PLOS ONE 11(1) , e0146113 (2016).
• A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber - Sci. Rep. 5 , 13606 (2015).
• Mapping high-growth phenotypes in the flux space of microbial metabolism - J. R. Soc. Interface 12 , 20150543 (2015).
• The acute impact of polyphenols from Hibiscus sabdariffa in metabolic homeostasis: an approach combining metabolomics and gene-expression analyses - Food Funct. 6 , 2957 -2966 (2015).
• Scaling and optimal synergy: Two principles determining microbial growth in complex media - Phys. Rev. E 91 , 062703 (2015).
• Control of cell–cell forces and collective cell dynamics by the intercellular adhesome - Nat. Cell Biol. 17 , 409 -420 (2015).
• Identifying strategies for mitigating the global warming impact of the EU-25 economy using a multi-objective input–output approach - Energ. Policy 77 , 21 -30 (2015).
• Cavity approach for modeling and fitting polymer stretching - Phys. Rev. E 90 , 052708 (2014).
• Impact of heterogeneity and socioeconomic factors on individual behavior in decentralized sharing ecosystems - Proc. Natl. Acad. Sci. U. S. A. 111 (43) , 15322 -15327 (2014).
• A Network Inference Method for Large-Scale Unsupervised Identification of Novel Drug-Drug Interactions - PLOS Comput. Biol. 9 (12) , e1003374 (2013).
• Energy metabolism and glutamate-glutamine cycle in the brain: a stoichiometric modeling perspective - BMC Systems Biology 7 , 103 (2013).
• Degree of intervality of food webs: From body-size data to models - J. Theor. Biol. 334 , 35 -44 (2013).
• A Novel Methodology to Estimate Metabolic Flux Distributions in Constraint-Based Models - Metabolites 2013 , 838-852 (2013).
• Predicting future conflict between team-members with parameter-free models of social networks - Sci. Rep. 3 , art. no. 1999 (2013).
• Use of a global metabolic network to curate organismal metabolic networks - Sci. Rep. 3 , art. no. 1695 (2013).
• Phenomenological model for predicting the catabolic potential of an arbitrary nutrient - PLOS Comput. Bio. , 8(11): e1002762 (2012).
• Predicting Human Preferences Using the Block Structure of Complex Social Networks - PLOS ONE 7 (9) , e44620 (2012).
• Justice Blocks and Predictability of US Supreme Court Votes - PLOS ONE 6 (11) , e27188 (2011).
• Modular coherence of protein dynamics in yeast cell polarity system - Proc. Natl. Acad. Sci. U. S. A. 108 , 7647 -7652 (2011).