NEMo: An Evolutionary Model With Modularity for PPI Networks

Modeling the evolution of biological networks is a major challenge. Biological networks are usually represented as graphs; evolutionary events not only include addition and removal of vertices and edges but also duplication of vertices and their associated edges. Since duplication is viewed as a primary driver of genomic evolution, recent work has focused on duplication-based models. Missing from these models is any embodiment of modularity, a widely accepted attribute of biological networks. Some models spontaneously generate modular structures, but none is known to maintain and evolve them. We describe network evolution with modularity (NEMo), a new model that embodies modularity. NEMo allows modules to appear and disappear and to fission and to merge, all driven by the underlying edge-level events using a duplication-based process. We also introduce measures to compare biological networks in terms of their modular structure; we present comparisons between NEMo and existing duplication-based models and run our measuring tools on both generated and published networks. Index indicates

NEMo: An Evolutionary Model with Modularity for PPI Networks

Qijia Jiang, Bernard Moret, Gabriela Clara Racz, Min Ye, Xiuwei Zhang

Modelling the evolution of biological networks is a major challenge. Biological networks are usually represented as graphs; evolutionary events include addition and removal of vertices and edges, but also duplication of vertices and their associated edges. Since duplication is viewed as a primary driver of genomic evolution, recent work has focused on duplication-based models. Missing from these models is any embodiment of modularity, a widely accepted attribute of biological networks. Some models spontaneously generate modular structures, but none is known to maintain and evolve them. We describe NEMo (Network Evolution with Modularity), a new model that embodies modularity. NEMo allows modules to emerge and vanish, to fission and merge, all driven by the underlying edge-level events using a duplication-based process. We introduce measures to compare biological networks in terms of their modular structure and use them to compare NEMo and existing duplication-based models and to compare both generated and published networks.

Springer Int Publishing Ag2016

Models and Algorithms in Biological Network Evolution with Modularity

Min Ye

Networks are commonly used to represent key processes in biology; examples include transcriptional regulatory networks, protein-protein interaction (PPI) networks, metabolic networks, etc. Databases store many such networks, as graphs, observed or inferred. Generative models for these networks have been proposed. For PPI networks, current models are based on duplication and divergence (D&D): a node (gene) is duplicated and inherits some subset of the connections of the original node. An early finding about biological networks is modularity: a higher-level structure is prevalent consisting of well connected subgraphs with less substantial connectivity to other such subgraphs. While D&D models spontaneously generate modular structures, neither have these structures been compared with those in the databases nor are D&D models known to maintain and evolve them. Given that the preferred generative models being based on D&D, the network inference models are also based on the same principle. We describe NEMo (Network Evolution with Modularity), a new model that embodies modularity. It consists of two layers: the lower layer is a derivation of the D&D process thus node-and-edge based, while the upper layer is module-aware. NEMo allows modules to appear and disappear, to fission and to merge, all driven by the underlying edge-level events using a duplication-based process. We also introduce measures to compare biological networks in terms of their modular structure. We present an extensive study of six model organisms across six public databases aimed at uncovering commonalities in network structure. We then use these commonalities as reference against which to compare the networks generated by D&D models and by our module-aware model NEMo. We find that, by restricting our data to high-confidence interactions, a number of shared structural features can be identified among the six species and six databases. When comparing these characteristics with those extracted from the networks produced by D&D models and our NEMo model, we further find that the networks generated by NEMo exhibit structural characteristics much closer to those of the PPI networks of the model organisms. We conclude that modularity in PPI networks takes a particular form, one that is better approximated by the module-aware NEMo model than by other current models. Finally, we draft the ideas for a module-aware network inference model that uses an altered form of our module-aware NEMo as the core component, from a parsimony perspective.

EPFL2017

Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods

Dario Floreano, Daniel Marbach, Claudio Mattiussi, Thomas Schaffter

Reverse engineering methods are typically first tested on simulated data from in silico networks, for systematic and efficient performance assessment, before an application to real biological networks. In this paper we present a method for generating biologically plausible in silico networks, which allow realistic performance assessment of network inference algorithms. Instead of using random graph models, which are known to only partly capture the structural properties of biological networks, we generate network structures by extracting modules from known biological interaction networks. Using the yeast transcriptional regulatory network as a test case, we show that extracted modules have a biologically plausible connectivity because they preserve functional and structural properties of the original network. Our method was selected to generate the "gold standard" networks for the gene network reverse engineering challenge of the third DREAM conference (Dialogue on Reverse Engineering Assessment and Methods, Cambridge, MA, 2008).

2009

Models and Algorithms in Biological Network Evolution with Modularity

Min Ye

EPFL2017