Network orientation via shortest paths

The graph orientation problem calls for orienting the edges of a graph so as to maximize the number of pre-specified source–target vertex pairs that admit a directed path from the source to the target. Most algorithmic approaches to this problem share a common preprocessing step, in which the input graph is reduced to a tree by repeatedly contracting its cycles. Although this reduction is valid from an algorithmic perspective, the assignment of directions to the edges of the contracted cycles becomes arbitrary, and the connecting source–target paths may be arbitrarily long. In the context of biological networks, the connection of vertex pairs via shortest paths is highly motivated, leading to the following problem variant: given a graph and a collection of source–target vertex pairs, assign directions to the edges so as to maximize the number of pairs that are connected by a shortest (in the original graph) directed path. This problem is NP-complete and hard to approximate to within sub-polynomial factors. Here we provide a first polynomial-size integer linear program formulation for this problem, which allows its exact solution in seconds on current networks. We apply our algorithm to orient protein–protein interaction networks in yeast and compare it with two state-of-the-art algorithms. We find that our algorithm outperforms previous approaches and can orient considerable parts of the network, thus revealing its structure and function. Availability and implementation: The source code is available at www.cs.tau.ac.il/*roded/shortest.zip

ANAT 2.0: reconstructing functional protein subnetworks

Abstract Background ANAT is a graphical, Cytoscape-based tool for the inference of protein networks that underlie a process of interest. The ANAT tool allows the user to perform network reconstruction under several scenarios in a number of organisms including yeast and human. Results Here we report on a new version of the tool, ANAT 2.0, which introduces substantial code and database updates as well as several new network reconstruction algorithms that greatly extend the applicability of the tool to biological data sets. Conclusions ANAT 2.0 is an up-to-date network reconstruction tool that addresses several reconstruction challenges across multiple species

Electrical and synaptic integration of glioma into neural circuits

High-grade gliomas are lethal brain cancers whose progression is robustly regulated by neuronal activity. Activity-regulated release of growth factors promotes glioma growth, but this alone is insufficient to explain the effect that neuronal activity exerts on glioma progression. Here we show that neuron and glioma interactions include electrochemical communication through bona fide AMPA receptor-dependent neuron–glioma synapses. Neuronal activity also evokes non-synaptic activity-dependent potassium currents that are amplified by gap junction-mediated tumour interconnections, forming an electrically coupled network. Depolarization of glioma membranes assessed by in vivo optogenetics promotes proliferation, whereas pharmacologically or genetically blocking electrochemical signalling inhibits the growth of glioma xenografts and extends mouse survival. Emphasizing the positive feedback mechanisms by which gliomas increase neuronal excitability and thus activity-regulated glioma growth, human intraoperative electrocorticography demonstrates increased cortical excitability in the glioma-infiltrated brain. Together, these findings indicate that synaptic and electrical integration into neural circuits promotes glioma progression