CABeRNET: a Cytoscape app for augmented Boolean models of gene regulatory NETworks

Andrea Paroni,Alex Graudenzi,Marco Antoniotti,Giulio Caravagna,Giancarlo Mauri,Chiara Damiani

doi:10.1186/s12859-016-0914-z

Abstract

BackgroundDynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development. Yet, the topological and functional characterization of real GRNs is often still partial and an exhaustive picture of their functioning is missing.ResultsWe here introduce CABeRNET, a Cytoscape app for the generation, simulation and analysis of Boolean models of GRNs, specifically focused on their augmentation when a only partial topological and functional characterization of the network is available. By generating large ensembles of networks in which user-defined entities and relations are added to the original core, CABeRNET allows to formulate hypotheses on the missing portions of real networks, as well to investigate their generic properties, in the spirit of complexity science.ConclusionsCABeRNET offers a series of innovative simulation and modeling functions and tools, including (but not being limited to) the dynamical characterization of the gene activation patterns ruling cell types and differentiation fates, and sophisticated robustness assessments, as in the case of gene knockouts. The integration within the widely used Cytoscape framework for the visualization and analysis of biological networks, makes CABeRNET a new essential instrument for both the bioinformatician and the computational biologist, as well as a computational support for the experimentalist. An example application concerning the analysis of an augmented T-helper cell GRN is provided.

Highlights

Dynamical models of gene regulatory networks (GRNs) are highly effective in describing complex biological phenomena and processes, such as cell differentiation and cancer development
We remark that it is not possible to provide a precise upper bound for the size of the networks to simulate CABERNET, because, as it is known from Random Boolean Networks (RBNs)/Noisy Random Boolean Networks (NRBNs) literature, the dynamical behaviour of even small networks can be dramatically heterogeneous, strongly depending on the dynamical regime, which is defined by a series of key structural parameters
In [59] the dynamics of such network was simulated with a Boolean approach and it was shown that the attractors reproduce real gene activation patterns of distinctly differentiated T-helper cell types

Summary

Results

Our group has recently been focusing on the investigation of the dynamical properties of multicellular systems via multiscale simulations, with particular attention to the conditions that would favor the emergence and development of tumors. By pruning the ATN with increasingly larger thresholds, the TES at the higher level, including all the 8 attractors connected by noiseinduced transitions and representing multi-/toti-potent cells, progressively splits in TESs enclosing an increasingly lower number of attractors, up to the 7 TESs at the lower level, which correspond to single attractors, when the threshold is equal to 1 This network was selected as plausible because the resulting emergent differentiation tree matches that of hematopoietic cells (taken from [60], see Fig. 2). By simulating selective single knockouts of the genes in the original T-helper core, we can assess the distinctive relevance in maintaining the correct differentiation scheme In this example, we performed 40 single knockout experiments (KO), by forcing the specific Boolean function of each gene to inactivation (i.e., 0 output for any regulatory input), and we tried to match the resulting differentiation trees with that of hematopoietic cells. We remark that the generated networks could be used within any multiscale simulation frameworks, in order to investigate, e.g., the processes of homeostasis and clonal expansion, as proposed in [35, 36]

Conclusions

Background