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Efficient attractor analysis based on self-dependent subsets of elements--an application to signal transduction
M Cárdenas-García1, J Lagunez-Otero, N Korneev
1Instituto de Quimica, UNAM, Ciudad Universitaria, Coyoacán, México. maura@servidor.unam.mx
Abstract:
External signals are transmitted to the cells through receptors activating signal transduction pathways. These pathways form a complicated interconnected network, which is able to answer to different stimuli. Here we analyze an important pathway for oncogenesis namely RAS/MAPK signal transduction pathway. We show that the interaction of the elements of this pathway induces topological structure in the element set and that the knowledge of the topology simplifies the analysis of the set. With a computer algorithm, we isolate from a large and complex group, smaller, independent, more manageable subsets, and build their hierarchy. Subsets introduction makes easier the search for attractors in discrete dynamical system, it permits the prediction of final states for elements involved in signal transduction pathways.
Insights
This study simplifies complex cell signaling networks, like the RAS/MAPK pathway, by identifying topological structures. This approach aids in predicting cellular responses and understanding oncogenesis.
Area of Science:
- Cellular Biology
- Systems Biology
- Computational Biology
Background:
- Signal transduction pathways transmit external signals via receptors, forming complex networks.
- The RAS/MAPK pathway is crucial in oncogenesis and cellular responses to stimuli.
Purpose of the Study:
- To analyze the topological structure within the RAS/MAPK signal transduction pathway.
- To develop a method for simplifying the analysis of complex biological networks.
Main Methods:
- Utilized a computer algorithm to identify topological structures in the RAS/MAPK pathway.
- Isolated smaller, independent subsets from the larger network and established their hierarchy.
Main Results:
- Demonstrated that interactions within the pathway induce a topological structure.
- Showed that this topological knowledge simplifies pathway analysis.
- Successfully isolated manageable subsets and built their hierarchy.
Conclusions:
- Network topology simplifies the analysis of signal transduction pathways.
- Subset identification facilitates the search for attractors in discrete dynamical systems.
- This approach aids in predicting the final states of elements in signaling pathways, with implications for understanding oncogenesis.