Computational models of signalling networks for non-linear control

TY - JOUR

T1 - Computational models of signalling networks for non-linear control

AU - Fuente, Luis A

AU - Lones, Michael A

AU - Turner, Alexander P

AU - Stepney, Susan

AU - Caves, Leo S

AU - Tyrrell, Andy M

N1 - Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

PY - 2013/5

Y1 - 2013/5

N2 - Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.

AB - Artificial signalling networks (ASNs) are a computational approach inspired by the signalling processes inside cells that decode outside environmental information. Using evolutionary algorithms to induce complex behaviours, we show how chaotic dynamics in a conservative dynamical system can be controlled. Such dynamics are of particular interest as they mimic the inherent complexity of non-linear physical systems in the real world. Considering the main biological interpretations of cellular signalling, in which complex behaviours and robust cellular responses emerge from the interaction of multiple pathways, we introduce two ASN representations: a stand-alone ASN and a coupled ASN. In particular we note how sophisticated cellular communication mechanisms can lead to effective controllers, where complicated problems can be divided into smaller and independent tasks.

KW - Algorithms

KW - Animals

KW - Biological Evolution

KW - Cell Communication

KW - Computer Simulation

KW - Humans

KW - Kinetics

KW - Models, Biological

KW - Nonlinear Dynamics

KW - Signal Transduction

U2 - 10.1016/j.biosystems.2013.03.006

DO - 10.1016/j.biosystems.2013.03.006

M3 - Article

C2 - 23499817

VL - 112

SP - 122

EP - 130

JO - Biosystems

JF - Biosystems

SN - 0303-2647

IS - 2

ER -