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NetContrl4BioMed: A pipeline for biomedical data acquisition and analysis of network controllability

Krishna Kanhaiya, Vladimir Rogojin, Keivan Kazemi, Eugen Czeizler, Ion Petre, NetContrl4BioMed: A pipeline for biomedical data acquisition and analysis of network controllability. BMC Bioinformatics 19(7), 3–12, 2018.

Abstract:

Background

Network controllability focuses on discovering combinations of external interventions that can drive a biological system to a desired configuration. In practice, this approach translates into finding a combined multi-drug therapy in order to induce a desired response from a cell; this can lead to developments of novel therapeutic approaches for systemic diseases like cancer.

Result

We develop a novel bioinformatics data analysis pipeline called NetControl4BioMed based on the concept of target structural control of linear networks. Our pipeline generates novel molecular interaction networks by combining pathway data from various public databases starting from the user’s query. The pipeline then identifies a set of nodes that is enough to control a given, user-defined set of disease-specific essential proteins in the network, i.e., it is able to induce a change in their configuration from any initial state to any final state. We provide both the source code of the pipeline as well as an online web-service based on this pipeline http://combio.abo.fi/nc/net_control/remote_call.php.

Conclusion

The pipeline can be used by researchers for controlling and better understanding of molecular interaction networks through combinatorial multi-drug therapies, for more efficient therapeutic approaches and personalised medicine.

BibTeX entry:

@ARTICLE{jKaRoKaCzPe18a,
  title = {NetContrl4BioMed: A pipeline for biomedical data acquisition and analysis of network controllability},
  author = {Kanhaiya, Krishna and Rogojin, Vladimir and Kazemi, Keivan and Czeizler, Eugen and Petre, Ion},
  journal = {BMC Bioinformatics},
  volume = {19},
  number = {7},
  pages = {3–12},
  year = {2018},
  ISSN = {1471-2105},
}

Belongs to TUCS Research Unit(s): Computational Biomodeling Laboratory (Combio Lab)

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