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Another observation that could be made from the color map concerns the absence of four reactions,
t107.SF3b10 in
,
t108.SF3b14a in
,
t112.SF3b49 in
and
t115.SF3b14b in in
, from a large body of T-
invariants (i13-i38), independently of the trivial T-invariants. These reactions model the influx of four
SF3b specific factors. They were drawn as logical places, each with a specified efflux reaction prior to the
C-complex stage, because experiments failed to detect these factors within spliceosomal C-complexes
[Makarov
et al.
, 2002] (see supplementary material). The T-invariants, i13-i38, describe the signaling
pathways that reach the final stage of spliceosome assembly. Hence, these routes pass the stage, where
the SF3b factors are leaving the active assembly process. Among others, the specified SF3b factors are
required for subsequent rounds of spliceosome assembly, thus the absence of their influx reactions from
T-invariants, i13-i38, can be interpreted as a way to remain within range of the spliceosome assembly
site. A different scenario occurs for the T-invariants that enter the discard pathway, which is triggered
before C-complex formation. Here, no explicit efflux reactions could be adapted from literature for
the SF3b factors, hence their influx reaction is part of the T-invariants i44-i71 (Fig. 11). This raises
the question, what happens with these factors and when, if the discard pathway is activated during
spliceosome assembly.
DISCUSSION
The present work describes a Petri net model, which combines different scenarios of spliceosome
assembly over the basic assembly stages of this multi-protein complex.
The spliceosome is a component of the nucleus, which is newly built after or as soon as a precursor
RNA emerges from the RNA polymerase II transcription complex. The assembly process involves
biochemical reactions, which can be distinguished in enzymatic and association reactions. Unlike in
metabolic networks, which commonly model the conversion of low molecular compounds to produce
energy or target metabolites,
e.g.
, amino acids [Schuster and Hilgetag, 1994; Koch
et al.
, 2005], the
spliceosome assembly involves many enzymatic reactions, which act on double stranded RNAs as
substrate and proteins or NTPs as co-factors [Mayas
et al.
, 2006, Staley and Guthrie, 1998]. This is due
to the snRNA containing core components of the spliceosome, which interact via multiple RNA-RNA
contacts making it necessary to re-open intermediate conformations at several stages during the assembly
process. Additionally, phosphorylation reactions as known from signal transduction networks [Pawson
and Nash, 2000] assure the specificity and localization of splicing factors. Consequently, the model
presented here, consists of several types of molecules (RNA, proteins and compounds of both) and
reactions, which have been designed and tested individually prior to the setup of the complete network.
Hundreds of individual studies have investigated components of the spliceosome or individual bio-
chemical reactions. The knowledge of many years of laboratory work is available but needs to be
translated into a machine readable and human comprehensible language. Thus, one of the main purposes
of this work is to channel biochemical knowledge about the spliceosome into a formalized description,
suitable for computational analysis. One major difficulty is the handling of non-standardized identifiers
of the involved proteins, which exacerbates the combination of smaller models, initially devised from
individual reports and successively combined into a larger network of interactions. Thus, the power of
predictive modeling will increase as more submodels become integrated, covering more details of the
spliceosome assembly pathway.
We establish a network of ordered basic interactions leading to the assembly of an active spliceosome,
including also the example of a discard pathway, which was previously suggested [Villa and Guthrie,
2005].
In total, about 100 proteins where integrated into the model.
Many proteins, participating in
