Generating the Logicome of a Biological Network

A4 Conference proceedings


Internal Authors/Editors


Publication Details

List of Authors: Charmi Panchal, Sepinoud Azimi, Ion Petre
Editors: María Botón-Fernández
Carlos Martín-Vide
Sergio Santander-Jiménez
Miguel A. Vega-Rodríguez
Publication year: 2016
Publisher: Springer
Book title: Algorithms for Computational Biology. Third International Conference, AlCoB 2016, Trujillo, Spain, June 21-22, 2016, Proceedings
Title of series: Lecture Notes in Computer Science
Number in series: 9702
Start page: 38
End page: 49
ISBN: 978-3-319-38826-7
eISBN: 978-3-319-38827-4
ISSN: 0302-9743


Abstract

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.


Keywords

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

Last updated on 2019-16-09 at 08:06