2013
DOI: 10.1098/rsif.2013.0438
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Abstract: The complexity of many RNA processing pathways is such that a conventional systems modelling approach is inadequate to represent all the molecular species involved. We demonstrate that rule-based modelling permits a detailed model of a complex RNA signalling pathway to be defined. Phosphorylation of the RNA polymerase II (RNAPII) C-terminal domain (CTD; a flexible tail-like extension of the largest subunit) couples pre-messenger RNA capping, splicing and 3′ end maturation to transcriptional elongation and term… Show more

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Cited by 5 publications
(6 citation statements)
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“…Explicitly accommodating combinatorial complexity enables more detailed and more precise investigation of system dynamics without unjustified simplification of models (Suderman and Deeds, 2013;Deeds et al, 2012;Faeder et al, 2005a). Accommodating such complexity is especially relevant for characterizing systems involving biopolymers (Köhler et al, 2014;Aitken et al, 2013) or systems that can undergo a phase transition to a gel state (Goldstein and Perelson, 1984). Clearly, these studies and others that use network-free simulation engines are not at all concerned with stochastic effects, but with the ability to model and simulate these biochemical systems that contain a high degree of combinatorial complexity (Stites et al, 2015;Creamer et al, 2012).…”
Section: Discussionmentioning
confidence: 99%
“…Explicitly accommodating combinatorial complexity enables more detailed and more precise investigation of system dynamics without unjustified simplification of models (Suderman and Deeds, 2013;Deeds et al, 2012;Faeder et al, 2005a). Accommodating such complexity is especially relevant for characterizing systems involving biopolymers (Köhler et al, 2014;Aitken et al, 2013) or systems that can undergo a phase transition to a gel state (Goldstein and Perelson, 1984). Clearly, these studies and others that use network-free simulation engines are not at all concerned with stochastic effects, but with the ability to model and simulate these biochemical systems that contain a high degree of combinatorial complexity (Stites et al, 2015;Creamer et al, 2012).…”
Section: Discussionmentioning
confidence: 99%
“…Moreover, the approximation, M η (θ m ; θ f )p(θ f )dθ f ≈ M η (θ m ; θ * f ) is appropriate when the distribution p(θ f ) is tightly concentrated around its mode θ * f . In general, however, when p(θ f ) does not have this special form, we can resort to downsampling E θ f [M η (θ m )], by generating k samples θ f (1) , . .…”
Section: Marginalising θ Fmentioning
confidence: 99%
“…In this standard Gaussian Processes regression the noise model in the observations is assumed to be a constant σ 2 for all sampled points. In the more general case we work with downsampling solutions that exploit k samples for the free variable, θ f (1) , . .…”
Section: The Regression Taskmentioning
confidence: 99%
“…These small RNAs contain sequences that bind (a set of) core proteins essential for the stability or function of the RNP, guide the RNP to their target as well as take part in catalysis. Target RNA maturation steps are in part co-transcriptional and, in the case of splicing, organized by factors associating with the C terminal domain of RNA polymerase (RNAP) ( 5 7 ).…”
Section: Introductionmentioning
confidence: 99%