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.