Biological networks such as protein-protein interaction networks or gene regulatory networks are an integral part to understand biological systems. Yet, our current representations of biological networks are mostly static: the factor of time (for instance coming from time-series data) or event-driven interaction (as is found in cell cycle regulation) are not accounted for.
In collaboration with groups from the CPT (Alain Barrat) and I2M (Laurent Tichit), we have two ongoing projects on integrating time as a factor in biological networks.
Using higher-order networks to model temporal processes
The four phases and essential check points of the cell cycle
Extraction of cell cycle phases from the higher-order network
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.
TimeNexus: a cytoscape app to analyse time-series expression data with temporal multilayer networks
TimeNexus projects temporal data on the layers of a multilayer 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'.
Previous work done in network biology for interpretation and integration of data coming from -omics studies:
1) miMerge and miScore for the generation of non-redundant protein interaction networks (Villaveces, et al., Database, 2015, doi: 10.1093/database/bau131)
2) KEGGViewer (Villaveces, et al., F1000Res 3:43, 2014, doi: 10.12688/f1000research.3-43.v1) for the visualization and integration of pathway data; and PsiquicGraph (Villaveces, et al., F1000Res 3:44, 2014, doi: 10.12688/f1000research.3-44.v1) both available via the BioJS platform;
3) the Cytoscape plugins viPEr for generating focus networks based on -omics data and PEANUT for pathway enrichment of focus networks (Garmhausen et al., BMC Genomics 16:790, 2015, doi: 10.1186/s12864-015-2017-z).
Current funding for these projects comes from ANR and CENTURI.
Previous projects were funded by the BMBF grants 'HEPATOSYS' and 'SYBACOL'. and the Max Planck Society.