Aligning Statistical Dynamics Captures Biological Network Functioning
arXiv:1912.12551
Abstract
Empirical studies of graphs have contributed enormously to our understanding of complex systems. Known today as network science, what was originally a theoretical study of graphs has grown into a more scientific exploration of communities spanning the physical, biological, and social. However, as the quantity and types of networks have grown so has their heterogeneity in quality and specificity. This has hampered efforts to develop general network theory capable of inferring functioning and predicting dynamics across study systems. We have successfully approached this challenge by aligning networks to each other rather than comparing parameter estimates from individually fitted models or properties of edge topologies. By comparing the predictability of statistical dynamics originating from each network's constituent nodes we were able to build a functional classifier that distinguished underlying processes in both synthetic and real-world network data spanning the entire biological scale from cellular machinery to ecosystems.
arXiv commentSupplementary Information included, 35 pages total, 8 main text & 4 SI figures, and 3 SI tables. Accompanying software at https://cran.r-project.org/package=netcom
References in corpus (7)
- Statistical mechanics of complex networks
- Community structure in social and biological networks
- Lethality and centrality in protein networks
- Classes of behavior of small-world networks
- Community detection in complex networks using Extremal Optimization
- Duplication-divergence model of protein interaction network
- Uncovering Biological Network Function via Graphlet Degree Signatures