Network inference and biological dynamics

SEBINI: Software Environment for BIological Network Inference

UNLABELLED The Software Environment for BIological Network Inference (SEBINI) has been created to provide an interactive environment for the deployment and evaluation of algorithms used to reconstruct the structure of biological regulatory and interaction networks. SEBINI can be used to compare and train network inference methods on artificial networks and simulated gene expression perturbation...

Representing perturbed dynamics in biological network models.

We study the dynamics of gene activities in relatively small size biological networks (up to a few tens of nodes), e.g., the activities of cell-cycle proteins during the mitotic cell-cycle progression. Using the framework of deterministic discrete dynamical models, we characterize the dynamical modifications in response to structural perturbations in the network connectivities. In particular, w...

Analysis of Biological Network Data Using Likelihood-free Inference Techniques

Biological Networks have received much attention in recent years, but statistical tools for network analysis are still in their infancy. In this paper we focus on Protein Interaction Networks (PINs) that typically comprise thousands of proteins and interactions. PINs are the result of long evolutionary histories. Here we adopt simple mathematical models that capture essentials of protein evolut...

Strategies for increasing the applicability of biological network inference

Classifying pairs with trees for supervised biological network inference

Networks are ubiquitous in biology, and computational approaches have been largely investigated for their inference. In particular, supervised machine learning methods can be used to complete a partially known network by integrating various measurements. Two main supervised frameworks have been proposed: the local approach, which trains a separate model for each network node, and the global app...