network biology

Many biological systems go through various phases, or states, over different time and space scales. These phases, and their order in time, are often crucial to the functioning of these systems and even determine their evolution/fate. To understand or even predict these phases would yield crucial insight into the temporal organisation of such systems, and ultimately, into their evolution.

The cell cycle, at the heart of all biological development, illustrates this well. Indeed, in order to eventually divide into two cells, the cell typically progresses through 4 macroscopic phases. At the microscopic level, these phases are driven by protein-protein interactions (PPIs).

To investigate the temporal organisation of the cell cycle, however, the typical static PPI network is not enough. That is why we build a temporal PPI network, by integrating time series of protein concentrations to the static one. By doing so, we open the whole toolbox of temporal network and higher-order networks to study our biological system.

In particular, in a CENTURI-funded project and in collaboration with Alain Barrat (CPT) and Laurent Tichit (I2M), we infer cell cycle phases and subphases, revealing the temporal organisation of the cycle over multiple timescales. We do this by clustering snapshots of the temporal network.

In a CENTURI funded project and in collaboration with Alain Barrat (CPT) and Laurent Tichit (I2M), we model the cell cycle as a temporal network.

Biological networks are not static, but dynamic. With the availability of time-series datasets, we can follow the dynamics of expression changes - on protein or RNA level - experimentally. To integrate these types of data with biological networks is currently not so easy, as few ready-to-use tools exist. In order to address this gap, we have developed the Cytoscape app TimeNexus. In TimeNexus, we project temporal data on a multilayer network: each layer represents one time-point. We refer to these networks as 'temporal multilayer networks (tMLNs). With TimeNexus, a user can easily create such a tMLN simply by providing tabular information on the network itself, as well as the temporal layers in form of time-series data. TimeNexus furthermore provides a framework for visualizing a tMLN, as well as for extracting active subnetworks using the Cytoscape apps PathLinker and the ANAT server.

We recently submitted TimeNexus for publication. Find the pre-print here.

You can also download the TimeNexus app directly from the Cytoscape App store.

This project is done in close collaboration with Laurent Tichit (I2M). It was funded by a shared ANR grant our team has with Aziz Moqrich (IBDM), 'MYOCHRONIC'.