Dataset Information

BowTieBuilder: modeling signal transduction pathways.

ABSTRACT:

Background

Sensory proteins react to changing environmental conditions by transducing signals into the cell. These signals are integrated into core proteins that activate downstream target proteins such as transcription factors (TFs). This structure is referred to as a bow tie, and allows cells to respond appropriately to complex environmental conditions. Understanding this cellular processing of information, from sensory proteins (e.g., cell-surface proteins) to target proteins (e.g., TFs) is important, yet for many processes the signaling pathways remain unknown.

Results

Here, we present BowTieBuilder for inferring signal transduction pathways from multiple source and target proteins. Given protein-protein interaction (PPI) data signaling pathways are assembled without knowledge of the intermediate signaling proteins while maximizing the overall probability of the pathway. To assess the inference quality, BowTieBuilder and three alternative heuristics are applied to several pathways, and the resulting pathways are compared to reference pathways taken from KEGG. In addition, BowTieBuilder is used to infer a signaling pathway of the innate immune response in humans and a signaling pathway that potentially regulates an underlying gene regulatory network.

Conclusion

We show that BowTieBuilder, given multiple source and/or target proteins, infers pathways with satisfactory recall and precision rates and detects the core proteins of each pathway.

SUBMITTER: Supper J

PROVIDER: S-EPMC2712453 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

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Publications

BowTieBuilder: modeling signal transduction pathways.

Supper Jochen J Spangenberg Lucía L Planatscher Hannes H Dräger Andreas A Schröder Adrian A Zell Andreas A

BMC systems biology 20090630

<h4>Background</h4>Sensory proteins react to changing environmental conditions by transducing signals into the cell. These signals are integrated into core proteins that activate downstream target proteins such as transcription factors (TFs). This structure is referred to as a bow tie, and allows cells to respond appropriately to complex environmental conditions. Understanding this cellular processing of information, from sensory proteins (e.g., cell-surface proteins) to target proteins (e.g., T ...[more]

PMID: 19566957

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Project description:BACKGROUND: Combinatorial complexity is a challenging problem in detailed and mechanistic mathematical modeling of signal transduction. This subject has been discussed intensively and a lot of progress has been made within the last few years. A software tool (BioNetGen) was developed which allows an automatic rule-based set-up of mechanistic model equations. In many cases these models can be reduced by an exact domain-oriented lumping technique. However, the resulting models can still consist of a very large number of differential equations. RESULTS: We introduce a new reduction technique, which allows building modularized and highly reduced models. Compared to existing approaches further reduction of signal transduction networks is possible. The method also provides a new modularization criterion, which allows to dissect the model into smaller modules that are called layers and can be modeled independently. Hallmarks of the approach are conservation relations within each layer and connection of layers by signal flows instead of mass flows. The reduced model can be formulated directly without previous generation of detailed model equations. It can be understood and interpreted intuitively, as model variables are macroscopic quantities that are converted by rates following simple kinetics. The proposed technique is applicable without using complex mathematical tools and even without detailed knowledge of the mathematical background. However, we provide a detailed mathematical analysis to show performance and limitations of the method. For physiologically relevant parameter domains the transient as well as the stationary errors caused by the reduction are negligible. CONCLUSION: The new layer based reduced modeling method allows building modularized and strongly reduced models of signal transduction networks. Reduced model equations can be directly formulated and are intuitively interpretable. Additionally, the method provides very good approximations especially for macroscopic variables. It can be combined with existing reduction methods without any difficulties.

Dataset Information

BowTieBuilder: modeling signal transduction pathways.

Background

Results

Conclusion

Publications

BowTieBuilder: modeling signal transduction pathways.

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