Observatorio de I+D+i UPM

Memorias de investigación
Research Publications in journals:
Discrete Nondeterministic Modeling of the Fas Pathway
Year:2008
Research Areas
  • Artificial intelligence
Information
Abstract
Abstract: Computer modeling of molecular signaling cascades can provide useful insight into the underlying complexities of biological systems. We present a refined approach for the discrete modeling of protein interactions within the environment of a single cell. The technique we offer utilizes the Membrane Systems paradigm which, due to its hierarchical structure, lends itself readily to mimic the behavior of cells. Since our approach is nondeterministic and discrete, it provides an interesting contrast to the standard deterministic ordinary differential equations techniques. We argue that our approach may outperform ordinary differential equations when modeling systems with relatively low numbers of molecules – a frequent occurrence in cellular signaling cascades. Refinements over our previous modeling efforts include the addition of nondeterminism for handling reaction competition over limited reactants, increased efficiency in the storing and sorting of reaction waiting times, and modifications of the model reactions. Results of our discrete simulation of the type I and type II Fas-mediated apoptotic signaling cascade are illustrated and compared with two approaches: one based on ordinary differential equations and another based on the well-known Gillespie algorithm.
International
Si
JCR
Si
Title
INTERNATIONAL JOURNAL OF FOUNDATIONS OF COMPUTER SCIENCE
ISBN
0129-0541
Impact factor JCR
0,656
Impact info
Volume
19
10.1142/S0129054108006194
Journal number
5
From page
1147
To page
1162
Month
OCTUBRE
Ranking
Participants
  • Participante: OSCAR H. IBARRA (University of California)
  • Autor: Alfonso Vicente Rodriguez-Paton Aradas (UPM)
  • Participante: JOHN JACK (Louisiana Tech University)
  • Autor: Paul Andrei Paun (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Inteligencia Artificial (LIA)
  • Departamento: Inteligencia Artificial
S2i 2020 Observatorio de investigación @ UPM con la colaboración del Consejo Social UPM
Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
Cofinanciación del MINECO en el marco del Programa INNPACTO (IPT-020000-2010-22)