Living systems require to process information originating from a complex environment in order to control their physiological functions. Important mechanisms in this task are the signaling networks. A signaling network is a collection of proteins and another biomolecules involved in the transduction of stimuli to the machinery in charge of the regulation of gene expression and protein levels. Mutation in one or more of the proteins of a signaling network can provoke malfunctions that may lead to serious diseases such as cancer. This has motivated considerable research towards understanding and predicting how cells will respond to native signals and artificial perturbations.

The complexity of the signaling networks demands efficient methodologies to integrate and analyze large amount of experimental data available. Mathematical models combined with computational procedures have proven to be powerful tools for this purpose. Extensive models that account only for the topology of signaling networks have been used to analyze their structural properties. Specifically, an optimization-based framework was introduced to systematically identify: i) input-output connections implied by the signaling network structure and ii) disruptions strategies to prevent disease-related outputs [1]. In this work we extend this framework by using kinetic models of signaling networks. This makes it possible to take into consideration the dynamic behavior of the network and allows for a more quantitative description of the state of its components (e.g., inactive/activated) and perturbations (e.g., activation/inhibition). This approach allows for the differentiation between input-output paths that are indistinguishable from a topological perspective and to identify disruption strategies that network-based analysis is unable to reveal. These new features are highlighted through two examples using a prototype model of overlapping MAPK cascades and a simplified MAPK signaling network that exhibits bistability.

References

1. Dasika, M.S., Burgard, A., and Maranas, C.D., A computational framework for the topological analysis and targeted disruption of signal transduction networks. Biophysical Journal, 2006. 91(1): p. 382-398.