A methodology for the structural and functional analysis of signaling and regulatory networks

By Steffen Klamt, J. Sáez-Rodríguez, Jonathan A. Lindquist, Luca Simeoni, Ernst Dieter Gilles. BMC Bioinformatics, 2006

448 citations recorded by the source corpus.

BackgroundStructural analysis of cellular interaction networks contributes to a deeper understanding of network-wide interdependencies, causal relationships, and basic functional capabilities. While the structural analysis of metabolic networks is a well-established field, similar methodologies have been scarcely developed and applied to signaling and regulatory networks.ResultsWe propose formalisms and methods, relying on adapted and partially newly introduced approaches, which facilitate a structural analysis of signaling and regulatory networks with focus on functional aspects. We use two different formalisms to represent and analyze interaction networks: interaction graphs and (logical) interaction hypergraphs. We show that, in interaction graphs, the determination of feedback cycles and of all the signaling paths between any pair of species is equivalent to the computation of elementary modes known from metabolic networks. Knowledge on the set of signaling paths and feedback loops facilitates the computation of intervention strategies and the classification of compounds into activators, inhibitors, ambivalent factors, and non-affecting factors with respect to a certain species. In some cases, qualitative effects induced by perturbations can be unambiguously predicted from the network scheme. Interaction graphs however, are not able to capture AND relationships which do frequently occur in interaction networks. The consequent logical concatenation of all the arcs pointing into a species leads to Boolean networks. For a Boolean representation of cellular interaction networks we propose a formalism based on logical (or signed) interaction hypergraphs, which facilitates in particular a logical steady state analysis (LSSA). LSSA enables studies on the logical processing of signals and the identification of optimal intervention points (targets) in cellular networks. LSSA also reveals network regions whose parametrization and initial states are crucial for the dynamic behavior.We have implemented these methods in our software tool CellNetAnalyzer (successor of FluxAnalyzer) and illustrate their applicability using a logical model of T-Cell receptor signaling providing non-intuitive results regarding feedback loops, essential elements, and (logical) signal processing upon different stimuli.ConclusionThe methods and formalisms we propose herein are another step towards the comprehensive functional analysis of cellular interaction networks. Their potential, shown on a realistic T-cell signaling model, makes them a promising tool.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/eefe2d38cbca5535d3ce3e7f66794ec632b225c3

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Citations in the corpus

Logic-Based Models for the Analysis of Cell Signaling Networks (2010, background): “and network properties analysis logic steady state (41, 50)”
Hypergraphs and Cellular Networks (2009, background): “In fact, any Boolean network can be represented by a directed hypergraph [9], which can be advantageous when analyzing biologically relevant network properties [9,20,21].”
Inference of complex biological networks: distinguishability issues and optimization-based solutions (2011, methodology): “During the last decade, many methods have been developed to solve the network-inference (sometimes called reverse-engineering [4]) problems arising in e.g. gene expression [5-13], signal transduction [14-17] and metabolic networks [18-25].”
Current approaches to gene regulatory network modelling (2007, background): “published an example for control logic networks [46], based on hypergraphs, which are an extension to the graphs described above.”
A Logical Model Provides Insights into T Cell Receptor Signaling (2007, methodology): “Furthermore, we consider two timescales [13]: early ( s ¼ 1) and late ( s ¼ 2), involving processes occurring during or after the ﬁrst minutes of activation, respectively (the time-scale for each interaction is given in Table S2).”
Structural and functional analysis of cellular networks with CellNetAnalyzer (2007, unknown): “…of tf1 ) (cid:129) i is an ambivalent factor for k if P ik ∞ and N ik ∞ (example: rec2 is an ambivalent factor for tf2 ) The global dependencies collected in the dependency matrix facilitate valuable qualitative predictions about the effects of perturbation or knock-out experiments [8].”