Network Motifs, Feedback Loops and the Dynamics of Genetic Regulatory Networks

We analyse a suite of Boolean networks which have been evolved to exhibit limit cycle-type dynamics in terms of the distribution of small network motifs and feedback loops. We find that asynchronously updated Boolean networks can be evolved to exhibit fuzzy limit cycle dynamics without significant changes to the number of nodes and links in the network. Analysis of all possible triads of nodes in the networks and all feedback loops of length one to eight reveal no significant differences between the evolved and unevolved networks. We conclude that the reductionist, motif-based approach to network analysis may be inadequate to full understanding of network dynamics, and that some dynamic behaviour is an emergent property of complex networks as a whole.

[1]  David W. Corne,et al.  Genetic network reverse-engineering and network size; can we identify large GRNs? , 2004, 2004 Symposium on Computational Intelligence in Bioinformatics and Computational Biology.

[2]  M. Oren,et al.  The p53-Mdm2 module and the ubiquitin system. , 2003, Seminars in cancer biology.

[3]  M. Gopinathan,et al.  A two variable delay model for the circadian rhythm of Neurospora crassa. , 2004, Journal of theoretical biology.

[4]  M S Turner,et al.  Modelling genetic networks with noisy and varied experimental data: the circadian clock in Arabidopsis thaliana. , 2005, Journal of theoretical biology.

[5]  Igor Jurisica,et al.  Functional topology in a network of protein interactions , 2004, Bioinform..

[6]  S. Mangan,et al.  The coherent feedforward loop serves as a sign-sensitive delay element in transcription networks. , 2003, Journal of molecular biology.

[7]  S. Mangan,et al.  Structure and function of the feed-forward loop network motif , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[8]  H. Ueda,et al.  Genome-wide Transcriptional Orchestration of Circadian Rhythms inDrosophila * 210 , 2002, The Journal of Biological Chemistry.

[9]  Kazuyuki Aihara,et al.  Modeling genetic switches with positive feedback loops. , 2003, Journal of theoretical biology.

[10]  D. Thieffry,et al.  Dynamical behaviour of biological regulatory networks—I. Biological role of feedback loops and practical use of the concept of the loop-characteristic state , 1995 .

[11]  Janet Wiles,et al.  Towards more biological mutation operators in gene regulation studies. , 2004, Bio Systems.

[12]  Wolfgang Banzhaf,et al.  Network motifs in natural and artificial transcriptional regulatory networks , 2002, Journal of Biological Physics and Chemistry.

[13]  J. Ferrell.  Self-perpetuating states in signal transduction: positive feedback, double-negative feedback and bistability. , 2002, Current opinion in cell biology.

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

[15]  S. Kauffman.  Metabolic stability and epigenesis in randomly constructed genetic nets. , 1969, Journal of theoretical biology.

[16]  Jeff Hasty,et al.  Engineered gene circuits , 2002, Nature.

[17]  B. Schönfisch,et al.  Synchronous and asynchronous updating in cellular automata. , 1999, Bio Systems.

[18]  A. Levine,et al.  Surfing the p53 network , 2000, Nature.

[19]  Hamid Bolouri,et al.  Control of internal and external noise in genetic regulatory networks. , 2004, Journal of theoretical biology.

[20]  Peter Ruoff,et al.  PER/TIM-mediated amplification, gene dosage effects and temperature compensation in an interlocking-feedback loop model of the Drosophila circadian clock. , 2005, Journal of theoretical biology.

[21]  Torsten Reil,et al.  Dynamics of Gene Expression in an Artificial Genome - Implications for Biological and Artificial Ontogeny , 1999, ECAL.

[22]  Janet Wiles,et al.  Evolving Genetic Regulatory Networks Using an Artificial Genome , 2004, APBC.

[23]  E. D. Di Paolo,et al.  Rhythmic and non-rhythmic attractors in asynchronous random Boolean networks. , 2001, Bio Systems.

[24]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .