title: Exhaustive analysis of the modular structure of the spliceosomal assembly network: a Petri net approach.
abstract: Spliceosomes are macro-complexes involving hundreds of proteins with many functional interactions. Spliceosome assembly belongs to the key processes that enable splicing of mRNA and modulate alternative splicing. A detailed list of factors involved in spliceosomal reactions has been assorted over the past decade, but, their functional interplay is often unknown and most of the present biological models cover only parts of the complete assembly process. It is a challenging task to build a computational model that integrates dispersed knowledge and combines a multitude of reaction schemes proposed earlier.Because for most reactions involved in spliceosome assembly kinetic parameters are not available, we propose a discrete modeling using Petri nets, through which we are enabled to get insights into the system's behavior via computation of structural and dynamic properties. In this paper, we compile and examine reactions from experimental reports that contribute to a functional spliceosome. All these reactions form a network, which describes the inventory and conditions necessary to perform the splicing process. The analysis is mainly based on system invariants. Transition invariants (T-invariants) can be interpreted as signaling routes through the network. Due to the huge number of T-invariants that arise with increasing network size and complexity, maximal common transition sets (MCTS) and T-clusters were used for further analysis. Additionally, we introduce a false color map representation, which allows a quick survey of network modules and the visual detection of single reactions or reaction sequences, which participate in more than one signaling route. We designed a structured model of spliceosome assembly, which combines the demands on a platform that i) can display involved factors and concurrent processes, ii) offers the possibility to run computational methods for knowledge extraction, and iii) is successively extendable as new insights into spliceosome function are reported by experimental reports. The network consists of 161 transitions (reactions) and 140 places (reactants). All reactions are part of at least one of the 71 T-invariants. These T-invariants define pathways, which are in good agreement with the current knowledge and known hypotheses on reaction sequences during spliceosome assembly, hence contributing to a functional spliceosome. We demonstrate that present knowledge, in particular of the initial part of the assembly process, describes parallelism and interaction of signaling routes, which indicate functional redundancy and reflect the dependency of spliceosome assembly initiation on different cellular conditions. The complexity of the network is further increased by two switches, which introduce alternative routes during A-complex formation in early spliceosome assembly and upon transition from the B-complex to the C-complex. By compiling known reactions into a complete network, the combinatorial nature of invariant computation leads to pathways that have previously not been described as connected routes, although their constituents were known. T-clusters divide the network into modules, which we interpret as building blocks in spliceosome maturation. We conclude that Petri net representations of large biological networks and system invariants, are well-suited as a means for validating the integration of experimental knowledge into a consistent model. Based on this network model, the design of further experiments is facilitated.
authors.affiliation: Chair of Bioinformatics, Friedrich-Schiller University Jena, Jena, Germany. ralf.bortfeldt@agrar.hu-berlin.de
authors.forename: Ralf H
authors.initials: RH
authors.lastname: Bortfeldt
authors.forename: Stefan
authors.initials: S
authors.lastname: Schuster
authors.forename: Ina
authors.initials: I
authors.lastname: Koch
mesh_terms: D000465:Algorithms; D000494:Allosteric Regulation; D016000:Cluster Analysis; D003198:Computer Simulation; D005656:Fungal Proteins / Q000502:physiology; D006801:Humans; D007700:Kinetics; D046911:Macromolecular Substances / Q000378:metabolism; D008954:Models, Biological; D060066:Protein Interaction Maps; D021122:Protein Subunits / Q000502:physiology; D012342:RNA, Small Nuclear / Q000502:physiology; D017411:Ribonucleoproteins, Small Nuclear / Q000502:physiology; D015398:Signal Transduction; D012984:Software; D017381:Spliceosomes / Q000502:physiology*
chemical_list: D005656:Fungal Proteins; D046911:Macromolecular Substances; D021122:Protein Subunits; D012342:RNA, Small Nuclear; D017411:Ribonucleoproteins, Small Nuclear