Discriminative Topological Features Reveal Biological Network Mechanisms (original) (raw)

The Ins and Outs of Network-Oriented Modeling: From Biological Networks and Mental Networks to Social Networks and Beyond

Trans. Comput. Collect. Intell., 2019

Network-Oriented Modeling has successfully been applied to obtain network models for a wide range of phenomena, including Biological Networks, Mental Networks, and Social Networks. In this paper it is discussed how the interpretation of a network as a causal network and taking into account dynamics in the form of temporal-causal networks, brings more depth. The basics and the scope of applicability of such a Network-Oriented Modelling approach are discussed and illustrated. This covers, for example, Social Network models for social contagion or information diffusion, adaptive Mental Network models for Hebbian learning and adaptive Social Network models for evolving relationships. From the more fundamental side, it will be discussed how emerging network behavior can be related to network structure.

Editorial: Network bioscience Volume II

Frontiers in Genetics, 2023

Editorial on the Research Topic Network bioscience Volume II Network biology is based on the intuition that the quantitative modeling and algorithmic tools of network theory offer new possibilities to understand, model, and simulate the cell's internal organization and evolution, fundamentally altering our view of cell biology. As network biology has been gaining ground and recognition in the last 20 years, the scope of its application, while still well grounded in molecular biology and genetics, has moved steadily from tackling fundamental biological questions towards translational medicine, including modeling of diseases and applications in drug design and drug action prediction. This Research Topic Network Bioscience Vol II follows in the track of the first one Network Bioscience completed in 2019 (Antoniotti et al., 2019), and it aims at collecting cutting-edge research on the many guises of network bioscience. The papers contained in the present Research Topic are examples of how network and graph analysis can be used to elucidate various aspects of biological systems from inferring missing annotations, handling heterogeneous data types, including the vast literature available online, understanding metabolic dynamics, phenotype-genotype linking, to relationships assessment among diverse omics data for drug design and drug repositioning, to a deeper understanding of modularity in gene networks. Among the recent trends with a potential of high impact, a most notable one is the incorporation of causality considerations and concepts within the classical network models so to make better use of perturbation data that are currently not exploited to their full potential. In particular such hybrid causal network models help bridging the gap between descriptive and actionable network models, the former successfully describe biological systems as they are, the latter allows us to formulate questions and find answers within the vast scope of what-if, counterfactual, worlds.

Data-driven Analysis of Complex Networks and their Model-generated Counterparts

2018

Data-driven analysis of complex networks has been in the focus of research for decades. An important question is to discover the relation between various network characteristics in real-world networks and how these relationships vary across network domains. A related research question is to study how well the network models can capture the observed relations between the graph metrics. In this paper, we apply statistical and machine learning techniques to answer the aforementioned questions. We study 400 real-world networks along with 2400 networks generated by five frequently used network models with previously fitted parameters to make the generated graphs as similar to the real network as possible. We find that the correlation profiles of the structural measures significantly differ across network domains and the domain can be efficiently determined using a small selection of graph metrics. The goodness-of-fit of the network models and the best performing models themselves highly ...

Construction, comparison and evolution of networks in life sciences and other disciplines

Journal of The Royal Society Interface

Network approaches have become pervasive in many research fields. They allow for a more comprehensive understanding of complex relationships between entities as well as their group-level properties and dynamics. Many networks change over time, be it within seconds or millions of years, depending on the nature of the network. Our focus will be on comparative network analyses in life sciences, where deciphering temporal network changes is a core interest of molecular, ecological, neuropsychological and evolutionary biologists. Further, we will take a journey through different disciplines, such as social sciences, finance and computational gastronomy, to present commonalities and differences in how networks change and can be analysed. Finally, we envision how borrowing ideas from these disciplines could enrich the future of life science research.

Taxonomies of networks from community structure

Physical Review E, 2012

The study of networks has grown into a substantial interdisciplinary endeavor across the natural, social, and information sciences. Yet there have been very few attempts to investigate the interrelatedness of the different classes of networks studied by different disciplines. Here, we introduced a framework to establish a taxonomy of networks from various origins. The provision of this family tree not only helps understand the kinship of networks, but also facilitates the transfer of empirical analysis, theoretical modeling, and conceptual developments across disciplinary boundaries. The framework is based on probing the mesoscopic properties of networks, an important source of heterogeneity for their structure and function. Using our method, we computed 1 arXiv:1006.5731v1 [physics.data-an] 29 Jun 2010 a taxonomy for 752 individual networks and a separate taxonomy for 12 network classes. We also computed three within-class taxonomies for political, fungal, and financial networks, and found them to be insightful in each case.

How do biological networks differ from social networks? (an experimental study)

2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), 2014

In this paper we outline important differences between (1) protein interaction networks and (2) social and other complex networks, in terms of fine-grained network community profiles. While these families of networks present some general similarities, they also have some stark differences in the way the communities are formed. Namely, we find that the sizes of the best communities in such biological networks are an order of magnitude smaller than in social and other complex networks. We furthermore find that the generative model describing biological networks is very different from the model describing social networks. While for latter the Forest-Fire model best approximates their network community profile, for biological networks it is a random rewiring model that generates networks with the observed profiles. Our study suggests that these families of networks should be treated differently when deriving results from network analysis, and a fine-grained analysis is needed to better understand their structure.

Networks in biology: Handling biological complexity requires novel inputs into network theory

Complexity, 2011

Networks in Biology Handling Biological Complexity Requires Novel Inputs into Network Theory T he year 2009 was the tenth anniversary of the first publication on scale-free networks [1] and the fiftieth anniversary of the invention of random graphs [2]. Science magazine devoted a special section to review the present status of network theory, complexity research and its application to different disciplines [3]. Understanding and modeling complex systems without consideration of network topology and network evolution became out-of-date and practically impossible. The Erdös-Rényi model initiated a real breakthrough in the sense that statistical properties of graphs and networks became accessible without knowledge of the connection details. Among many other applications random networks became a useful reference in the biology of macromolecules for mapping polynucleotide sequences into structures [4]. Real world social networks, in particular communication networks, were found to have substantial shorter mean distances between agents than those predicted by the theory of random networks. Watts and Strogatz [5] invented networks that were found to match the available empirical data. These networks are called small-world networks as they have the small-world property 1 [6], and they can be understood as intermediates between a regular lattice and a random graph. Watts and Strogatz characterize small-world networks by a degree of randomness (p), which varies between p 5 0 for the regular and p 5 1 for the random network. Only 1 year later, Barabási and Albert conceived smallworld networks, which are created by the construction principle of preferential attachment [1]: Starting with a fully connected network of three nodes, further nodes are attached one by one with a larger probability for the incoming node to connect to a node that has already more neighbors. The resulting networks are scale-free-besides having the small-world property-and accordingly many of their properties fulfill power laws. 2 Comparison with real world data from a great

A taxonomy of networks

ArXiv e-prints, 2010

The study of networks has grown into a substantial interdisciplinary endeavor across the natural, social, and information sciences. Yet there have been very few attempts to investigate the interrelatedness of the different classes of networks studied by different disciplines. Here, we introduced a framework to establish a taxonomy of networks from various origins. The provision of this family tree not only helps understand the kinship of networks, but also facilitates the transfer of empirical analysis, theoretical modeling, and conceptual developments across disciplinary boundaries. The framework is based on probing the mesoscopic properties of networks, an important source of heterogeneity for their structure and function. Using our method, we computed 1 arXiv:1006.5731v1 [physics.data-an] 29 Jun 2010 a taxonomy for 752 individual networks and a separate taxonomy for 12 network classes. We also computed three within-class taxonomies for political, fungal, and financial networks, and found them to be insightful in each case.