Generating the Logicome of a Biological Network

Charmi Shah, Sepinoud Azimi Rashti, Ion Petre

Tutkimustuotos: Artikkeli kirjassa/raportissa/konferenssijulkaisussaKonferenssiartikkeliTieteellinenvertaisarvioitu

Abstrakti

There has been much progress in recent years towards building larger and larger computational models for biochemical networks, driven by advances both in high throughput data techniques, and in computational modeling and simulation. Such models are often given as unstructured lists of species and interactions between them, making it very difficult to understand the logicome of the network, i.e. the logical connections describing the activation of its key nodes. The problem we are addressing here is to predict whether these key nodes will get activated at any point during a fixed time interval (even transiently), depending on their initial activation status. We solve the problem in terms of a Boolean network over the key nodes, that we call the logicome of the biochemical network. The main advantage of the logicome is that it allows the modeler to focus on a well-chosen small set of key nodes, while abstracting away from the rest of the model, seen as biochemical implementation details of the model. We validate our results by showing that the interpretation of the obtained logicome is in line with literature-based knowledge of the EGFR signalling pathway. There has been much progress in recent years towards building larger and larger computational models for biochemical networks, driven by advances both in high throughput data techniques, and in computational modeling and simulation. Such models are often given as unstructured lists of species and interactions between them, making it very difficult to understand the logicome of the network, i.e. the logical connections describing the activation of its key nodes. The problem we are addressing here is to predict whether these key nodes will get activated at any point during a fixed time interval (even transiently), depending on their initial activation status. We solve the problem in terms of a Boolean network over the key nodes, that we call the logicome of the biochemical network. The main advantage of the logicome is that it allows the modeler to focus on a well-chosen small set of key nodes, while abstracting away from the rest of the model, seen as biochemical implementation details of the model. We validate our results by showing that the interpretation of the obtained logicome is in line with literature-based knowledge of the EGFR signalling pathway.
AlkuperäiskieliEi tiedossa
OtsikkoAlgorithms for Computational Biology. Third International Conference, AlCoB 2016, Trujillo, Spain, June 21-22, 2016, Proceedings
ToimittajatMaría Botón-Fernández Carlos Martín-Vide Sergio Santander-Jiménez Miguel A. Vega-Rodríguez
KustantajaSpringer
Sivut38–49
ISBN (elektroninen)978-3-319-38827-4
ISBN (painettu)978-3-319-38826-7
DOI - pysyväislinkit
TilaJulkaistu - 2016
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference, Algorithms for Computational Biology, AlCoB 2016. - Algorithms for Computational Biology. Third International Conference, AlCoB 2016
Kesto: 21 kesäkuuta 201622 kesäkuuta 2016

Konferenssi

KonferenssiInternational Conference, Algorithms for Computational Biology, AlCoB 2016.
Ajanjakso21/06/1622/06/16

Keywords

  • Pathway analysis
  • biomodeling
  • cell signaling
  • epidermal growth factor
  • logic circuits

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