Developing an integrated system for biological network exploration

Network analysis and visualization have been used in systems biology to extract biological insight from complex datasets. Many existing network analysis tools either focus on visualization but have limited scalability, or focus on analysis but have limited visualizations. The separation of analyzing the raw data from visualizing the analysis results causes systems biologists to jump between forming a question, building a massive network, identifying a subnetwork for visualization, and using the visualization as feedback and inspiration for the next question. This iterative process can take several days, making it difficult for researchers to maintain the mental map of the questions queried. In addition, biological data is stored in different formats and has differing annotations, thus systems biologists often run into hurdles when merging large or heterogeneous networks. The polymorphic nature of the datasets presents a challenge for researchers to integrate data to answer biological questions. A more systematic method for merging networks, resolving data conflicts, and analyzing networks may improve the efficiency and scalability of heterogeneous multi-network analysis. Towards improving and pushing forward multi-network analysis to help a researcher easily combine multiple heterogeneous biological data networks to answer biological questions, this dissertation reports several accomplishments that provide (i) a set of standard multi-network operations, (ii) standard merging rules for heterogeneous networks, (iii) standard methods to reproduce network analyses, (iv) a single integrated software environment that allows users to visualize and explore the network analysis results and (v) several examples applying these methods in biological analysis. These efforts have culminated in three academic publications

Qualitative dynamics semantics for SBGN process description

International audienceBackground: Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far. Results: We propose two qualitative dynamics semantics for SBGN-PD reaction networks, namely the general semantics and the stories semantics, that we formalize using asynchronous automata networks. While the general semantics extends standard Boolean semantics of reaction networks by taking into account all the main features of SBGN-PD, the stories semantics allows to model several molecules of a network by a unique variable. The obtained qualitative models can be checked against dynamical properties and therefore validated with respect to biological knowledge. We apply our framework to reason on the qualitative dynamics of a large network (more than 200 nodes) modeling the regulation of the cell cycle by RB/E2F. Conclusion: The proposed semantics provide a direct formalization of SBGN-PD networks in dynamical qualitative models that can be further analyzed using standard tools for discrete models. The dynamics in stories semantics have a lower dimension than the general one and prune multiple behaviors (which can be considered as spurious) by enforcing the mutual exclusiveness between the activity of different nodes of a same story. Overall, the qualitative semantics for SBGN-PD allow to capture efficiently important dynamical features of reaction network models and can be exploited to further refine them

Two Qualitative Dynamics Semantics for SBGN Process Description Maps

International audienceQualitative dynamics semantics allow to model large reaction networks with un-known kinetic parameters. In this poster, we present two qualitative dynamics seman-tics for reaction networks formalized into the SBGN Process Description language(SBGN-PD). These two semantics, namely the general semantics and the stories se-mantics, allow to model any SBGN-PD map into an automata network, that can thenbe simulated to catch the main dynamical features of the network. While the generalsemantics refines the standard Boolean semantics of reaction networks by taking intoaccount all the main features of SBGN-PD, the stories semantics allows to modelseveral molecules of a network by a unique variable, reducing in this way the size ofthe models. We present those two semantics and compare them on two biologicalnetwork examples

Additional file 2 of Qualitative dynamics semantics for SBGN process description

Relationship between stories and general semantics. Provides detailed sketches of proof for the properties relating the stories and the general semantics. (PDF 251 kb

Additional file 1 of Qualitative dynamics semantics for SBGN process description

Encoding of an asynchronous automata network into the Petri net formalism. Provides the translation of the AN of Fig. 2 into the Petri net formalism. Each local state of the AN is encoded into a place in the Petri net, and each transition of the AN is encoded into one transition in the PN, with one input and one output arc. Transition conditions of the AN are encoded under the form of read arcs in the Petri net. (PDF 88.7 kb