Abstract
Cells perceive and respond to their microenvironment as a part of their functioning via networks of processes resulting from molecular interactions. The complexity of such networks has been the subject of studies that address their various aspects. Some of these include static methods that focus on graph representations and their consequent properties, while others take a dynamical systems approach based on simulations. Here, we address the problem of identifying dominant pathways in biological networks that are represented as activation and repression edges. For this purpose, we propose a hybrid method that combines static graph properties with a dynamic quantification of information flow that results from stochastic simulations. We first illustrate our method on a simple example, and then apply it to the Escherichia coli transcription network consisting of 4639 regulatory edges.
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Notes
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All the data and scripts are available for download at: ozan-k.com/pathways.zip.
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Kahramanoğulları, O. (2019). Enumerating Dominant Pathways in Biological Networks by Information Flow Analysis. In: Holmes, I., Martín-Vide, C., Vega-Rodríguez, M. (eds) Algorithms for Computational Biology. AlCoB 2019. Lecture Notes in Computer Science(), vol 11488. Springer, Cham. https://doi.org/10.1007/978-3-030-18174-1_3
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