Signalling networks
Description:
The SignaLinK group is working with signalling networks in collaboration with Péter Csermely (Semmelweis University, LINK-group), Tibor Vellai (Eötvös University, Dept. of Genetics), Tamás Vicsek and Bálint Szabó (Eötvös University, Dept. of Biological Physics). Our aim is to experimentally prove our hypothesis on embed adaptation of network topology in C. elegans.
Due to the methodological advances in the past 5 to 6 years, our knowledge about the constituents of the cell and their links increased faster than the conceptualization of the underlying pathways and emergent properties. This is the reason why we do not have up-to-date signaling networks, which are neither subjective nor historically based. The geneticists and biochemists as well as system biologists have been working in this field for a long time but their approaches and aims are diverse. System biologists start with a huge amount of experimental data (mostly coming from high-throughput experiments) and examine their structure and tacit information structure. Geneticists and biochemists mostly select their research topics based on local traditions or current trends. These approaches often result in local and fragmented knowledge. Therefore, we currently have more than a dozen systems for signaling networks and classifications of signal transduction pathways. The traditional concept is the establishment of separate pathways and their cross-talk with each other. The scientific community becomes increasingly aware that this classification is rather biased and subjective. If we want to carry out experiments based on network properties, we need to have an objective, multiply checked network. Therefore we deceided to build a universal signaling network and will perform physiological experiments based on the network analysis.
If you have any questions regarding the above mentioned project or about our results, please write to: korcsmaros [at] gmail.com
A collection of groups working with signaling networks
A collection of useful and important biological sites in network science
The list of publications:




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