Computational models of signalling networks for non-linear control

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.

[1]  Susan Stepney,et al.  Controlling Complex Dynamics with Artificial Biochemical Networks , 2010, EuroGP.

[2]  Paul Horton,et al.  Network-based de-noising improves prediction from microarray data , 2006, BMC Bioinformatics.

[3]  Hiroaki Kitano,et al.  The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models , 2003, Bioinform..

[4]  S Lay,et al.  Computer simulated evolution of a network of cell-signaling molecules. , 1994, Biophysical journal.

[5]  Wolfgang Banzhaf,et al.  Evolving Control Metabolisms for a Robot , 2001, Artificial Life.

[6]  D. Bray.  Protein molecules as computational elements in living cells , 1995, Nature.

[7]  M. Schwartz,et al.  Interactions between mitogenic stimuli, or, a thousand and one connections. , 1999, Current opinion in cell biology.

[8]  F. M. Izraelev.  Nearly linear mappings and their applications , 1980 .

[9]  R. Somogyi,et al.  The gene expression matrix: towards the extraction of genetic network architectures , 1997 .

[10]  Ravi Iyengar,et al.  Modeling Signaling Networks , 2002, Science.

[11]  James Decraene,et al.  Evolving Artificial Cell Signaling Networks: Perspectives and Methods , 2007, Advances in Biologically Inspired Information Systems.

[12]  Steffen Klamt,et al.  A methodology for the structural and functional analysis of signaling and regulatory networks , 2006, BMC Bioinformatics.

[13]  Zuyi Huang,et al.  Fuzzy Modeling of Signal Transduction Networks , 2008 .

[14]  U. Bhalla,et al.  Emergent properties of networks of biological signaling pathways. , 1999, Science.

[15]  S. Shen-Orr,et al.  Network motifs: simple building blocks of complex networks. , 2002, Science.

[16]  Helmut A. Mayer,et al.  Multi-chromosomal representations and chromosome shuffling in evolutionary algorithms , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[17]  H. Kestler,et al.  Network modeling of signal transduction: establishing the global view , 2008, BioEssays : news and reviews in molecular, cellular and developmental biology.

[18]  Michal Linial,et al.  Using Bayesian Networks to Analyze Expression Data , 2000, J. Comput. Biol..

[19]  H. Sauro,et al.  Preliminary Studies on the In Silico Evolution of Biochemical Networks , 2004, Chembiochem : a European journal of chemical biology.

[20]  Peter K. Sorger,et al.  Logic-Based Models for the Analysis of Cell Signaling Networks† , 2010, Biochemistry.

[21]  B. Kholodenko,et al.  Quantification of information transfer via cellular signal transduction pathways , 1997, FEBS letters.

[22]  Wolfgang Banzhaf,et al.  Artificial Chemistries – Towards Constructive Dynamical Systems , 2004 .

[23]  Susan Stepney,et al.  Controlling legged robots with coupled artificial biochemical networks , 2011, ECAL.

[24]  Kyran Dale.  Evolving Reaction-Diffusion Controllers for Minimally Cognitive Animats , 2006, SAB.

[25]  Sergey I. Nikolenko,et al.  Directed Cycles in Bayesian Belief Networks: Probabilistic Semantics and Consistency Checking Complexity , 2005, MICAI.

[26]  Susan Stepney,et al.  Using Artificial Epigenetic Regulatory Networks to Control Complex Tasks within Chaotic Systems , 2012, IPCAT.

[27]  Susan Stepney,et al.  Evolved Artificial Signalling Networks for the Control of a Conservative Complex Dynamical System , 2012, IPCAT.

[28]  R. Albert.  Boolean modeling of genetic regulatory networks , 2004 .

[29]  Alfonso Martinez Arias,et al.  Filtering transcriptional noise during development: concepts and mechanisms , 2006, Nature Reviews Genetics.

[30]  Rachel Cavill,et al.  Multi-chromosomal genetic programming , 2005, GECCO '05.